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• Fungal metabolites and nanoparticles suppress disease severity. • Fungal metabolites and nanoparticles reduced oxidative stress in tomato plants. • These treatments positively regulate microbial communities in the soil. • Microbial communities showed more abundance by the application of fungal metabolites and nanoparticles. Numerous techniques have been adopted to promote crop growth and improve the effectiveness of biological control. One of these strategies includes the utilization of antimicrobial metabolites from biocontrol agents combined with other strategies. Nanotechnology is a novel and emerging trend in controlling plant diseases. The present study aimed to improve the understanding through an assessment of the mechanism of how the co-application of nanoparticles (SiNPS) and fungal metabolites prevent tomato bacterial wilt infection and explore how the applied treatments regulate the soil microbial community structures present in the rhizosphere of tomato plants. It was demonstrated under in-vitro conditions that the silicon nanoparticles and metabolites of fungi restricted the growth of Ralstonia solanacearum , the causal agent of bacterial wilt. The combined application of nanoparticles and fungal metabolites during planta investigation significantly reduced the disease severity and improved plant growth. These treatments caused significant changes in rhizosphere microbial communities and enhanced the abundance of plant-beneficial microbes. The results of the current study hence advocate the application of fungal metabolites and SiNPS as an effective and environment-friendly strategy for controlling the wilt disease of bacteria in tomato plants and various other host crops. [ABSTRACT FROM AUTHOR]

Sickle cell disease (SCD) is one of the most devastating inherited haemoglobinopathy that affects mostly children. Its prevalence can reach up to 13 % in the south-eastern part of Madagascar. For socio-cultural and economic reasons, the majority of patients use plants to manage the symptoms of this genetic disease. This work aims to inventory these plants and collect all information on their use, their therapeutic effects, and their eventual side effects in order to compile a database. These plants will be the subject of future chemical, pharmacological and toxicological investigations in the laboratory. Semi-directive interviews were led among 208 respondents from January 2018 to January 2020 in the eastern part of Madagascar. After obtaining the informed consent of the interviewees, collected data on the used medicinal plants were analyzed for various ethnomedicinal parameters including frequency of citation (FC), use value (UV), relative frequency of citation (RFC), fidelity level (FL), family use value (FUV) and informant consensus factor (ICF) related to the SCD management and the other mentioned diseases. Among the 208 respondents, 30.29 % were male; 73.39 % received formal education; 53.37 % were farmers, 8.17 % were traditional healers and 6.73 % were herbal sellers. Most of them (94.23 %) have a good knowledge of SCD and 91.83 % of them use herbal medicine to manage SCD. They recommended 123 plant species belonging to 111 genera and 62 families. Asteraceae (14.50 %) was the most cited family however Blechnaceae was the most valued family (FUV = 0.356). The majority of the cited species (70.73 %) were wild plants. Herbs (42.28 %) were the most used plant life form and the most used common parts were leaves (44.68 %). They usually prepare tea by infusion of plant material (71.83 %). Ficus polita was the most important species with the highest FC (10.93 %), UV (0.393), RFC (75.92 %) and FL (94.94 %). ICF of SCD and its symptoms value was 0.50 showing the homogeneity, and the consensus among the informants in the use of medicinal plants for managing this disease. Some species (08) have been reported for their anti-sickling properties and some of the other species have been reported for their pharmacological properties related to SCD symptoms management. These results confirmed the importance of investigating the use of plant species to manage SCD and its symptoms in the eastern part of Madagascar, suggesting the fundamental role of ethnomedicinal studies for the sustainable use of plant species for future scientific investigations on phyto-drugs for SCD management. [Display omitted] • 123 plant species were cited in the management of SCD and/or its symptoms. • The majority (70.73 %) of them were wild plants. • Herbs (42.28 %) was the most used plant life form and the most used common parts were leaves (44.68 %) and aerial parts (25.53 %). • The majority of the herbal preparation was infusion (71.83 %) and the administration is only by oral route. • The knowledge of medicinal plants depends on the age and the occupation of the informants. • Ficus polita and Stenochlaena tenuifolia were the most important species according to their use value. • Among the 123 species, 8 species have been reported for their anti-sickling properties. [ABSTRACT FROM AUTHOR]

Disease outbreaks are shaped by the 'disease triangle,' comprising a virulent pathogen, a susceptible plant, and appropriate environmental conditions. Recent advances have shed light on the intricate environment–plant–pathogen interactions. Rice resistance is manipulated by cytoplasmic and apoplastic effectors secreted by Magnaporthe oryzae. Environmental factors play a significant role in both blast disease epidemics and the regulation of host (or rice) resistance. Strategies such as protein engineering, genome editing, and host-induced gene silencing have been employed effectively to engineer blast-resistant rice plants. Rice blast is a highly destructive crop disease that requires the interplay of three essential factors: the virulent blast fungus, the susceptible rice plant, and favorable environmental conditions. Although previous studies have focused mainly on the pathogen and rice, recent research has shed light on the molecular mechanisms by which the blast fungus and environmental conditions regulate host resistance and contribute to blast disease outbreaks. This review summarizes significant achievements in understanding the sophisticated modulation of blast resistance by Magnaporthe oryzae effectors and the dual regulatory mechanisms by which environmental conditions influence rice resistance and virulence of the blast fungus. Furthermore, it emphasizes potential strategies for developing blast-resistant rice varieties to effectively control blast disease. [ABSTRACT FROM AUTHOR]

Cryptococcus neoformans and Cryptococcus gattii have been isolated from several different environmental sources, including several different tree species. This could indicate that the two species are ubiquitous in the same way as Saccharomyces cerevisiae. Laboratory infection studies have established that the two species can colonize the surface and inside of different plant species. However, the nature and number of studies make it difficult to form a general hypothesis about the relationship between C. neoformans/gattii and plants. Despite being reported, plant disease from infections is not well documented, and only from mated infection mixes. Interestingly, environmental isolates are often only of one of the two mating types. The profile of carbohydrate-active enzymes (CAZymes) encoded in the genomes of C. neoformans and C. gattii , makes it unlikely that they can degrade the plant cell wall as plant pathogens. Instead, they have the pectinolytic potential to live as commensal epi- or endophytes. While the opportunistic human pathogens Cryptococcus neoformans and Cryptococcus gattii are often isolated from plants and plant-related material, evidence suggests that these Cryptococcus species do not directly infect plants. Studies find that plants are important for Cryptococcus mating and dispersal. However, these studies have not provided enough detail about how plants and these fungi interact, especially in ways that could show the fungi are capable of causing disease. This review synthesizes recent findings from studies utilizing different plant models associated with the ecology of C. neoformans and C. gattii. Unanswered questions about their environmental role are highlighted. Overall, current research indicates that Cryptococcus utilizes plants as a substrate rather than harming them, arguing against Cryptococcus as a genuine plant pathogen. We hypothesize that plants represent reservoirs that aid dispersal, not hosts vulnerable to infection. [ABSTRACT FROM AUTHOR]

In this study, a combination of Serratia nematophila L2 and Bacillus velezensis W24 was used to biocontrol Sclerotinia sclerotiorum. When the mixed ratio of L2 to W24 was 1:1, the inhibition rate on the growth of S. sclerotiorum was 88.1 %. To gain a large number of bacteria, the culture medium and conditions were optimized. When the medium formula involved molasses (8.890 g/L), soy peptone (6.826 g/L), and NaCl (6.865 g/L), and the culture conditions were 32 °C, inoculum 4%, rotation speed 200 rpm, and pH 7, the maximum amounts of bacterial cells obtained. In order to prepare microcapsules, spray drying conditions were optimized. These conditions included the soluble starch concentration of 30 g/100 mL, the inlet air temperature of 160 °C, and the feed flow rate of 450 mL/h. Under these optimized conditions to prepare microcapsules, the mixed strain (L2 and W24) exhibited a survival rate of 93.9 ± 0.9% and a viable bacterial count of 6.4 × 1012 cfu/g. In addition, microcapsules (GW24Ms) which contained strains L2 and W24 had good storage stability. In the pot experiment, GW24Ms could effectively reduce the disease of soybean plants and the control effect was 88.4%. Thus, the microbial agent represents a promising biocontrol solution for managing Sclerotinia in soybean. • The GW24 (W24 and L2 strains were mixed) could inhibit the growth of Sclerotinia sclerotiorum. • The culture conditions and medium of GW24 were optimized. • The GW24 microcapsules were prepared by spray drying. • The GW24 microcapsules had great storage ability. • The GW24 microcapsules had great effects on S. sclerotiorum. [ABSTRACT FROM AUTHOR]

Calonectria leaf blight (CLB) is one of the best-known diseases of Eucalyptus spp., particularly in Asia and South America. Recently, typical symptoms of leaf and shoot blight caused by Calonectria spp. Were observed in a Eucalyptus plantation in the YunNan Province of southwestern China. Isolations were made from diseased leaves and top soil collected below the diseased trees to determine the causal agent of the disease and to consider the distribution characteristics of the Calonectria species. This resulted in 417 isolates, of which 228 were from leaves and 189 were from soil. Based on comparisons of DNA sequences for the act (actin), cmdA (calmodulin), his3 (histone H3), rpb2 (the second largest subunit of RNA polymerase), tef1 (translation elongation factor 1-alpha) and tub2 (β-tubulin) gene regions, as well as morphological characteristics, 11 Calonectria species were identified. These included Calonectria aciculata (0.7 %), Ca. colhounii (1.2 %), Ca. eucalypti (10.6 %) and Ca. honghensis (43.2 %) in the Ca. colhounii species complex, and Ca. aconidialis (15.3 %), Ca. asiatica (9.8 %), Ca. hongkongensis (1.0 %), Ca. ilicicola (6.0 %), Ca. kyotensis (0.5 %), and Ca. yunnanensis (11.3 %) in the Ca. kyotensis species complex. In addition, a novel species, accounting for 0.5 % of the isolates, was discovered and is described here as Ca. dianii sp. nov. in the Ca colhounii species complex. Most (99.1 %) of the isolates collected from the leaves resided in the Ca. colhounii species complex and a majority (95.8 %) of those from the soils were in Ca. kyotensis species complex. These results suggest that Calonectria spp. in the Ca. colhounii species complex infecting leaves might be adapted to that niche and that those in the Ca. kyotensis species complex are better adapted to a soil habitat. Calonectria leaf blight (CLB) is one of the best-known diseases of Eucalyptus spp., particularly in Asia and South America. A large number of Calonectria isolates were obtained from diseased leaves and soil samples collected from below the infected Eucalyptus trees in one plantation in China. A rich Calonectria diversity in the sampled Eucalyptus plantation were revealed. There were clear differences in species diversity and distribution relating to either the leaf or soil environments where they were found. It is becoming increasingly apparent that some species are predominantly soil inhabitants and different to those that cause plant diseases. [ABSTRACT FROM AUTHOR]

The effectiveness of SiO 2 nanoparticles for their potential to control bacterial wilt (BW) disease in tomato plants was studied. Different concentrations of nanoparticles were first evaluated for their in-vitro antibacterial potential against Ralstonia solanacearum , the causal agent of BW disease. The disease suppressive effect of NP was investigated through in-planta experiments in tomato plants. Nanoparticles exhibited maximum toxicity to R. solanacearum at the concentration of 25 µg/ml and showed a 14.2 ± 0.5 mm growth inhibition zone in a lab test. Tomato plants were grown in pathogen-inoculated soil that was amended with NP suspension. The efficacy of NP application was evaluated for their effect on disease suppression, soil pathogen population, and plant growth and defense response. Results showed that NP at 150 ppm significantly reduced the pathogen population in the soil, as indicated by population count through molecular and dilution count analysis. The NP treatment suppresses the disease index and improves plant growth. The activities of plant antioxidant enzymes were increased, and the expression of resistance genes PR1, PR5, PAL , and PDF - 1.2 were enhanced after NP application. Plants grown in NP-treated soil showed improved photosynthesis and reduced oxidative stress as compared to untreated plants under pathogen stress, and the total phenolic and flavonoid content was enhanced significantly. Moreover, DPPH and FRAP content and AsA/DHA and GSH/GSSG ratios were enhanced, followed by NP application, implying that the non-enzymatic antioxidant capacity of the plants was improved. This study reveals the molecular and physiological action mechanism of managing BW in tomato plants. It suggests that SiO 2 nanoparticles could be included in the integrated management of BW disease as one of the effective components. [Display omitted] • SiO 2 nanoparticles exhibited antibacterial activity against ralstonia solanacearum. • Soil treatment with SiO 2 NP suppress pathogen population in soil. • Application of SiO 2 NP enhanced plant growth and resistance. [ABSTRACT FROM AUTHOR]

Around 80% of the food consumed worldwide is provided by agriculture, and almost 30% of these products are affected by phytopathogenic agents, which cause numerous plant diseases. Microorganisms have been used as biocontrol agents to eliminate or decrease damage caused by phytopathogens. For the application of biocontrol agents, such as strains of Bacillus velezensis , several parameters need to be considered, including their habitat and molecular and biochemical conditions, such as plant growth promotion and antifungal activity. B. velezensis strains also have potential as control agents against nematodes in greenhouse cultivation, as probiotic supplements for feed, to pretreat lignocellulosic materials, or in wastewater treatments through the enzymes they produce. Many microorganisms have been reported as bioagents for producing ecofriendly, cost-effective, and safe products. Some Bacillus species of bacteria can be used in agricultural applications. Bacillus velezensis in particular has shown promising results for controlling destructive phytopathogens and in biofungicide manufacturing. Some B. velezensis strains can promote plant growth and display antibiotic activities against plant pathogen agents. In this review, we focus on the often-overlooked potential properties of B. velezensis as a bioagent for applications that will extend beyond the traditional agricultural uses. We delve into its versatility and future prospects, the challenges such uses may encounter, and some drawbacks associated with B. velezensis -based products. [ABSTRACT FROM AUTHOR]

To cause disease in plants, pathogens not only require a susceptible host, but also often require specific environmental conditions suitable for pathogenesis. Multiple aspects of climate are predicted to change dramatically over the coming decades, which will result in increases in CO 2 and, depending on location, temperature, humidity, salinity, flooding, or drought. Climate change will result in increases in the prevalence, dispersal, and range of different plant pathogens, while simultaneously affecting the virulence mechanisms of microbial pathogens. The plant immune system can be compromised by extremes in climatic conditions and crosstalk with abiotic stress signaling pathways. Several recent reports have unraveled how climatic factors affect plants at the molecular level, raising the possibility of modifying these pathways for climate-smart, pathogen-resistant crops. Variations in climate conditions can dramatically affect plant health and the generation of climate-resilient crops is imperative to food security. In addition to directly affecting plants, it is predicted that more severe climate conditions will also result in greater biotic stresses. Recent studies have identified climate-sensitive molecular pathways that can result in plants being more susceptible to infection under unfavorable conditions. Here, we review how expected changes in climate will impact plant–pathogen interactions, with a focus on mechanisms regulating plant immunity and microbial virulence strategies. We highlight the complex interactions between abiotic and biotic stresses with the goal of identifying components and/or pathways that are promising targets for genetic engineering to enhance adaptation and strengthen resilience in dynamically changing environments. [ABSTRACT FROM AUTHOR]

• ZnO nanoparticles (NPs) were active in inhibiting growth of Fusarium oxysporum. • Plants grown in NPs-amended soil showed improved growth. • NPs caused increased activity of plant defense-related enzymes. • Plants showed enhanced expression of defense-related genes after NPs application. Management of Fusarium wilt, a destructive plant disease, mainly relies on chemicals, but research on novel and eco-friendly methods is needed due to the hazardous effects of chemical pesticides. This study explored the soil application of zinc oxide nanoparticles (ZnONPs) to manage Fusarium wilt disease in cucumber plants. The antifungal activity of ZnONPs against in-vitro growth of Fusarium oxysporum was tested through food poisoning test on the potato dextrose agar medium. The in-vivo potential of NPs for managing Fusarium wilt was checked in pot experiment. In-vitro results confirmed the concentration-dependent in-vitro antifungal activity. The NPs application at 500 µg/ml and 600 µg/ml concentrations showed phytotoxicity and significantly inhibited seed germination and growth of cucumber plants. However, NPs at 300 µg/ml significantly suppressed the pathogenic effects of F. oxysporum on cucumber plants without any phytotoxic effect. The pathogen-inoculated cucumber plants grown in soil amended with NPs showed significantly enhanced growth, lower disease severity, and improved root physiology compared to untreated plants. The plants under NPs treatment showed higher chlorophyll and nitrogen content while decreased membrane permeability and malondialdehyde content. Along with direct antifungal activity, the application of NPs caused an increased expression of defense-related genes and higher activities of defense-related enzymes. These findings verified the efficiency of ZnONPs for suppressing Fusarium wilt disease in cucumber crops, thereby promoting a non-chemical and environmentally friendly approach to controlling Fusarium wilt disease. [ABSTRACT FROM AUTHOR]

Starting in the fall of 2019, mortality, blight symptoms, and signs of white fungal mycelia were observed on external host tissues of non-native landscape trees as well as numerous native trees, understory shrubs, and vines throughout northern and central Florida, USA. We determined that the fungus is an undescribed species of Basidiomycota based on morphological characteristics and DNA sequence analysis. Phylogenetic analyses of the internal transcribed spacer (ITS), large subunit (LSU), and translation elongation factor 1-alpha (tef1) regions revealed that this novel plant pathogen is an undescribed taxon of the genus Parvodontia (Cystostereaceae, Agaricales). We propose the name Parvodontia relampaga sp. nov. which describes its unique morphological features and phylogenetic placement. We confirmed the pathogenicity of P. relampaga in greenhouse inoculations on host plants from which strains of this novel pathogen were isolated, including the non-native gymnosperm Afrocarpus falcatus , the non-native and commercially important Ligustrum japonicum , and the native tree Quercus hemisphaerica. P. relampaga was also detected on a total of 27 different species of woody host plants, including such economically and ecologically important hosts as Fraxinus, Ilex, Magnolia, Persea, Prunus, Salix, Vitis , and Vaccinium. For this new plant disease, we propose the name "relampago blight," which refers to the lightning-like rhizomorph growth (relámpago means 'lightning' in Spanish). This study presents a newly discovered fungal taxon with a wide host range on both angiosperms and gymnosperms that may be an emerging pathogen of concern in Florida and the Gulf Coast region. • A novel thread blight pathogen is formally described as Parvodontia relampaga. • " Relampago blight" is the common name of the disease cause by P. relampaga. • This is the first report of any thread blight pathogen in continental USA. • Relampago blight was detected in plant seedlings in central and north Florida. [ABSTRACT FROM AUTHOR]

Climate change might increase plant diseases, reduce crop yields and threaten the livelihoods of millions of smallholder farmers globally. It is thus important to understand the relationships between climate, disease levels and yield to improve management strategies for sustainable agroforestry in a changing climate. One of the major threats to coffee production in Africa is the coffee berry disease (Colletotrichum kahawae). To investigate the effects of climatic and management variables on coffee berry disease (CBD) incidence and yield, we recorded minimum and maximum temperature and relative humidity, as well as CBD and yield, along a broad environmental and management gradient in southwestern Ethiopia during two consecutive years. CBD was affected by several climatic and management variables. For example, CBD incidence increased with minimum temperature during the fruit expansion stage, and decreased with minimum temperature during the endosperm filling stage. CBD incidence was negatively affected by the proportion of resistant cultivars, whereas the coffee structure index (pruning) had no effect on disease incidence. Coffee yield decreased with increasing minimum temperature during the flowering period in 2018 and maximum temperature during the fruit developmental period in 2019. Coffee yield was negatively affected by canopy cover and positively affected by the coffee structure index in both years. Our findings highlight that CBD and yield were affected by different climatic and management variables. Yet, managing for low disease levels and high yield is practically difficult due to season-dependent effects of several climatic variables. One way to break the correlation of climatic variables between seasons might be to take advantage of differences among shade trees in the presence or timing of leaf drop. To reduce CBD incidence, using resistant cultivars is an effective strategy, but this might threaten the wild coffee genetic reservoir. [ABSTRACT FROM AUTHOR]

In an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution's architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios. [ABSTRACT FROM AUTHOR]

Tomato diseases have become a major concern to the tomato production sector around the world. A huge proportion of tomato crops deteriorate yearly during growth or after harvesting due to the infections caused by fungus, viruses and bacteria. Early detection of these diseases plays a crucial role in alleviating overall production loss. Over the past decades, farmers have been using visual observation to identify a crop disease in a field. However, the visual observation method is labour intensive, time consuming, and prone to human error. Currently, intelligent approaches have been widely used to detect and classify these diseases. The objective of this study is to design a convolutional neural network VGG16 net architecture that is able to detect tomato diseases on tomato leaves. A model that can successfully detect 5 tomato leaf diseases was developed: (1) Bacterial spot, (2) Early blight, (3) Late blight, (4) Septoria leaf and (5) Yellow leaf curl with a training accuracy of 0.9328. [ABSTRACT FROM AUTHOR]

The Indian economy benefits from early disease detection in plant leaves. According to reports, 10-30% of crops suffer harm from diseases that are not discovered during the curing process. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have paved the way for transformative solutions in disease identification for agriculture. This study addresses the critical issue of early disease detection by harnessing the power of deep learning models, specifically CNNs. Different leaf disease detection technologies are used for different crops. The pre-trained deep learning model is used in this study to identify and categorize leaf diseases. A dataset of tomato, potato, and bell pepper leaf pictures from the plant village repository was employed for the current investigation. The developed model can detect 12 plant diseases in normal leaf tissue. The quantitative assessment of our CNN-based technique reveals an impressive accuracy rate of 86.21%. This notable accuracy underscores the efficacy of our approach in the challenging domain of plant disease detection. Our findings show potential for the larger agricultural environment beyond the immediate quantitative advantages. This technical advance not only paves the way for improved crop output and lower losses, but also brings in a new era of data-driven sustainability in agriculture. The bottom line is that our research "offers a tangible pathway for leveraging AI-powered solutions to address the long-standing challenges of plant disease detection, thereby significantly contributing to the well-being of farmers and the sustenance of the Indian agricultural sector. [ABSTRACT FROM AUTHOR]

One of the structure of economic development is farming involved with niche skills and technologies. More than 25% of total tomato crop production is lost annually because of various plant diseases. The farmers who grow tomatoes face huge economic losses every year, often due to various diseases that can infect tomatoes. The work carried aims to identify these diseases early and accurately, so can be informed well before the damage, which can minimize the waste. Convolution neural network (CNN) techniques, subset of Machine Learning (ML) are demonstrated to be very efficient for image datasets of various complex problems. Specifically, the work is focused to build and train a customized CNN model to classify the leaf images. Here we are using advanced deep learning architectures AlexNet and VGGNet-16 to recognize various diseases from image datasets collected from agricultural sources to develop models. Comparative analysis has resulted an improvement in accuracy by using deep learning architectures and CNN models. These models are responsible for helping to solve distribution problems, checking whether a leaf is healthy or not and, if so, what disease it is. Disease detected early and treated quickly minimizes the cost and improves the quality and quantity of the crop produced. [ABSTRACT FROM AUTHOR]

Nanotechnology has helped for decades to transform the agricultural system to make it more effective and to ensure the worlds food supply is sustainable and secure. However, potential toxicological affects, interactions with the biotic or abiotic environment, high cost, unknown life cycles of nanomaterials, and their possible increased bioaccumulation effects are some of the barriers which needs to be addressed. This study evaluates development and potential agricultural application of niosomes nanoparticles loaded with Azadirachta indica seed oil. Niosomes are organic nanomaterials composed of non-ionic surfactants, which are inexpensive, have good encapsulation efficiency, more stability, relatively nontoxic, and biodegradable. Azadirachta indica seed oil loaded niosomes were prepared by thin-film hydration method utilizing different molar ratios of surfactant (Tween 80) and stabilizer (Soy lecithin). The characteristics of niosomes were determined by Fourier Transform Infra-Red Spectroscopy, Dynamic Light Scattering, Optical Microscope, and Transmission Electron Microscopy. Further, the prepared niosomes were statistically optimized using the Box-Behnken experimental design. The optimized condition displayed optimum particle size (<100 nm) and acceptable percentage entrapment efficiencies (> 80%). Anti-microbial efficacy of loaded niosomes were evaluated against plant pathogens Xanthomonas and Pantoea. The prepared niosomes displayed considerable antimicrobial properties and can be potentially used in agriculture for the targeted delivery of natural pesticides (Azadirachta indica seed oil) to combat agricultural diseases. [Display omitted] • Niosomes prepared from Tween 80 and lecithin can encapsulate neem oil. • Niosomes can be potentially used for the targeted delivery of natural pesticides. • Encapsulation of neem oil (NO) in niosomes could effectively delay the release of NO. • NO loaded niosomes showed antimicrobial efficacy against Xanthomonas and Pantoea. [ABSTRACT FROM AUTHOR]

[Display omitted] • To overcome the major challenges of global food security and environmental and social problems caused by the extensive use of chemical pesticides, developments of modern green sustainable management strategies are urgently needed for the control of crop diseases. • A deeper understanding of the principles of plant immunity and induced host immunity offers potentials for reducing the use of chemical pesticides and paves the way for sustainable agriculture. • The sustainable environment-friendly disease prevention and control technologies based on plant immunity inducers are comprehensively summarized in this review. • The significance of the application of plant immunity inducers for plant disease control is systematically summarized. • By focusing on research advances of plant immunity inducers, this review offers future perspectives of plant immunity inducers and provides a theoretical reference for researchers. Crops are constantly attacked by various pathogens. These pathogenic microorganisms, such as fungi, oomycetes, bacteria, viruses, and nematodes, threaten global food security by causing detrimental crop diseases that generate tremendous quality and yield losses worldwide. Chemical pesticides have undoubtedly reduced crop damage; however, in addition to increasing the cost of agricultural production, the extensive use of chemical pesticides comes with environmental and social costs. Therefore, it is necessary to vigorously develop sustainable disease prevention and control strategies to promote the transition from traditional chemical control to modern green technologies. Plants possess sophisticated and efficient defense mechanisms against a wide range of pathogens naturally. Immune induction technology based on plant immunity inducers can prime plant defense mechanisms and greatly decrease the occurrence and severity of plant diseases. Reducing the use of agrochemicals is an effective way to minimize environmental pollution and promote agricultural safety. The purpose of this work is to offer valuable insights into the current understanding and future research perspectives of plant immunity inducers and their uses in plant disease control, ecological and environmental protection, and sustainable development of agriculture. In this work, we have introduced the concepts of sustainable and environment-friendly concepts of green disease prevention and control technologies based on plant immunity inducers. This article comprehensively summarizes these recent advances, emphasizes the importance of sustainable disease prevention and control technologies for food security, and highlights the diverse functions of plant immunity inducers-mediated disease resistance. The challenges encountered in the potential applications of plant immunity inducers and future research orientation are also discussed. [ABSTRACT FROM AUTHOR]

With diseases and weeds alone accounting for approximately 30% of productivity losses in agriculture worldwide, plant diseases are a significant factor. Farmers frequently consult experts to diagnose their crops in an effort to reduce these losses. On the other hand, conventional visual assessment techniques focused on elements like leaf color, shape, and texture can be inefficient, time-consuming, and expensive. This study suggests using symptom traits like yellow leaves, black lesions, wilting, and plant mortality in crops like apple and grape to anticipate plant diseases, notably black rot. AlexNet, VGGNet, ResNet, and Inception are the four deep learning models examined in the study; VGGNet achieved the greatest accuracy of 98.19%. [ABSTRACT FROM AUTHOR]

Rubber is a crucial crop in the Philippines, but it faces the problem of pests and diseases that can significantly reduce yields, impacting rubber exports. To address this problem, a study used image processing techniques to enhance the identification of diseases in rubber trees. The researchers used background segmentation and deep learning to improve the accuracy of identifying anthracnose (leaf blight) and powdery mildew in rubber trees. The study found that using a background segmentation algorithm significantly improved the accuracy of all three architectures, DenseNet, Inception, and MobileNet, for both anthracnose and powdery mildew identification. Specifically, the DenseNet architecture had the highest accuracy rate of 97% for powdery mildew. Meanwhile, the MobileNet architecture achieved an accuracy rate of 98% for powdery mildew and 90% for anthracnose. Overall, the results suggest that using background segmentation algorithms can enhance the accuracy of identifying plant diseases in rubber trees, which could boost the economic growth of rubber exports in the country. [ABSTRACT FROM AUTHOR]

Detecting dynamic changes in small molecule concentrations in situ is one of the most novel strategies to obtain information on plant health in real time. A wearable electrochemical sensor is a novel tool for the dynamic detection of small molecules in plants. Small molecules exist on plant surfaces or within plants in vivo. The surface humidity can reflect plant transpiration intensity, and surface volatile organic compounds are closely related to plant diseases. In plants, small molecules such as glucose, salicylic acid, H 2 O 2 , and nitrate play important roles in respiration, drought stress, radiation stress, and assimilation, respectively. Among wearable electrochemical sensors, needle-type sensors have attracted a lot of attention due to their characteristics of causing less damage to plants, high monitoring sensitivity, and easy integration. Small molecules in plants – such as metabolites, phytohormones, reactive oxygen species (ROS), and inorganic ions – participate in the processes of plant growth and development, physiological metabolism, and stress response. Wearable electrochemical sensors, known for their fast response, high sensitivity, and minimal plant damage, serve as ideal tools for dynamically tracking these small molecules. Such sensors provide producers or agricultural researchers with noninvasive or minimally invasive means of obtaining plant signals. In this review we explore the applications of wearable electrochemical sensors in detecting plant small molecules, enabling scientific assessment of plant conditions, quantification of environmental stresses, and facilitation of plant health monitoring and disease prediction. [ABSTRACT FROM AUTHOR]

Global food security is seriously threatened by plant diseases and can cause severe economic losses to farmers. Automated detection of plant diseases using computer vision and machine learning techniques has become a popular research area due to its potential to provide faster and more accurate results than traditional methods. In this research, we propose a plant disease detection model based on transfer learning using the MobileNetV2. We evaluate the suggested method on a dataset consisting of 38 various kinds of diseases across 14 different plants. Our experimental findings indicate that the proposed method has a typical accuracy of 91.98% and outperforms additional cutting-edge CNN models for plant disease detection. The experimental findings support the suggested approach's validity and show that it effectively detects plant diseases. We also have put forth the evaluation metrics to investigate the model. The suggested method has potential applications in real-world scenarios and can help farmers detect diseases in their crops at an early stage, allowing them to take timely action to minimize crop losses. [ABSTRACT FROM AUTHOR]

As the main cause of destructive plant diseases, pathogenic oomycete in plant rhizosphere brings about enormous losses to agricultural production. Although chemical pesticides are still one of the most important prevention and control methods for phytopathogens, the usage of chemical pesticides was limited by the 3R (resistance, residue, and rampant) problem. In the early stage of our research, analysis and comparison of the metabolome of resistance to Phytophthora nicotianae and common strain suggested that naringenin might be a highly efficient potential biogenic antimicrobial agent to prevent and control soil rhizosphere diseases. Unfortunately, the bioactivity and absorption capacity of active ingredients in the environment made it unsuitable for field application; thus, for efficient field application of naringenin, the 24 nm-sized naringenin-loaded nano- star -shaped polymerized (NSPs) were prepared with good loading efficiency 37.3% for naringenin. The soil mobility test indicated that NSPs could effectively reduce the adsorption of active ingredients and enhance the mobility of active ingredients in soil. The bacteriostatic test proved that these NSPs had better antimicrobial activity than the naringenin used alone and could efficiently induce the expression of plant resistance phenylpropanoid compounds. Finally, pot and field experiments showed improved control efficiency of NSPs 41% loaded with naringenin. Transcriptome analysis found that a large number of energy-related genes were downregulated in NSPs nematodes, suggesting that disturbed energy-related genes might lead to the disturbance of energy synthesis and metabolism. Naringenin-loaded nano-carriers were used to prevent and control plant disease-causing pathogens in the rhizosphere, which is of great significance to improve the prevention and control effect and reduce the environmental load of these anti-pathogenic agents. [Display omitted] • The mechanism of biopesticide naringenin improving plant disease resistance was clarified. • Preparation of a low-cost nano-pesticide that can significantly improve the control effect of naringenin. • Nanocarriers improve the mobility of naringenin in soil and protect the active ingredient from degradation. • Nano-pesticides have good control effects on soil root diseases in the greenhouse or field. [ABSTRACT FROM AUTHOR]

Today, there is a visible reduction in productivity of common crops like rice and wheat in agricultural sector. At the same time, millet crops have emerged as an alternative to counter global food security issues because of their adaptability and nutritional value that can combat malnutrition. Also, because of its low moisture requirement and tolerance to extreme climatic conditions, it is perceived as a stable cereal. But its cultivation is affected by diseases like rust and blast, thereby causing harm to the farming economy. Conventional operational methods of disease detection require regular manual intervention which are also costly. The correctness of expert suggestions is also under scrutiny. Thus, a sustainable, robust and low cost modern approach for millet crop monitoring and disease detection is required. This research aims to develop a smart and sustainable framework by integrating Internet of things (IoT) and deep learning (DL). In the presented framework, a sensory module based automated crop health data gathering system with an improved deep learning-assisted intelligent disease recognition model is developed for the millet crop. Sensors collect data from millet farmland transfer the crop data readings to the cloud server for storage and Raspberry Pi for further detection. Any abnormality in reading leads to an alert notification communicated to the farmer. A hybrid predictive model, Customized Convolutional neural network (Customized-CNN) model works with the Raspberry Pi to predict the presence of blast and rust disease symptoms in millet. The proposed sustainable model is implemented, and it generates productive outcomes. The recorded accuracy, precision, recall, and f-score values with the customized CNN model were 98.8%, 98.2%, 97.4%, and 97.7%, respectively. The training and testing delays were only 67 s and 88 s, respectively. A sample of yearly sensor readings was generated to show the accuracy and reliability of the model's data collection. Also, the model proved to be scalable, as it gave noteworthy performance with diverse crops. The evaluation shows the reliability of the model, and it can be used by farming societies to enhance millet cereal yield in a cost-effective way. [ABSTRACT FROM AUTHOR]

• Extraction conditions influence the suppressiveness ability of compost extracts. • Aggressive temperatures promote a higher extraction of phenolic compounds. • Mild temperatures guarantee the preservation of enzymatic activities. • Protocols based on high temperatures reduce the abundance of actinobacteria. • Mesophilic conditions boost extracts' suppressiveness according to incubation time. The search for new sustainable alternatives for plant disease control has gained interest in the last decades. Compost extracts are nowadays considered a remarkable alternative to agrochemicals due to their biopesticidal properties. However, these properties could be affected by the different variables of extraction protocols and by starting compost. This work focused on the physicochemical and biological characterization of compost extracts obtained from a wide range of composted materials and different extraction protocols (CEP). CEP-1 and CEP-4 involved incubation at 20 °C for 48 h and 14 days, respectively; CEP-2 incubation for 24 h at 40 °C; while CEP-3 were incubated for 12 h at 70 °C. Electrical conductivity, pH, total organic carbon (TOC) and phenolic content were determined as well as the actinobacterial count and enzyme profiles related to plant pathogen suppression. Additionally, the influence of the different materials and protocols on the in vitro growth inhibition of Alternaria alternata and Botrytis cinerea was determined. The starting materials and extraction protocols significantly influenced the physicochemical and biological characteristics of extracts. Treatments based on long incubation times at 20 °C, as well as those based on short incubation times at 40 °C, resulted in extracts with increased suppressive properties. However, extracts derived from CEP-3 protocol were characterized by high phenolic and TOC content, low functional biodiversity, and a more discreet antagonistic capacity. Therefore, the development and optimization of suitable extraction protocols could lead to compost extracts with increased phytoprotective capacities, thus becoming an effective and sustainable alternative to chemical inputs. [ABSTRACT FROM AUTHOR]

The collar rot disease caused by the soil-borne fungus Sclerotium rolfsii (Sacc.) is a major constraint of chili (Capsicum annum L.) production in Pakistan. Considering the fact that biological control has low efficacy in field conditions, their encapsulation into biopolymers like alginate meets the essential criteria for bacteria viability, effectiveness, shelf life, stability, and controlled release. The current research was conducted to develop stable alginate beads of biocontrol bacteria (Ochrobactrum ciceri) and to check their efficacy in managing collar rot disease in C. annum. In vitro, antifungal bioassays indicated that increasing concentrations (16, 24, and 64%) of culture filtrate or the cell pellets significantly decreased fungal growth and enzyme activity (catalase: CAT, peroxidase: POX, polyphenol oxidase: PPO, and phenylalanine ammonia-lyase: PAL), hence increased offshoots hyphae, and caused distortion in sclerotia and hyphae. O. ciceri successfully developed stable alginate beads (AlgB-OC) as indicated by FTIR analysis, encapsulation efficiency (91.15%), moisture content (99.19%), swelling ratio (130%), particle size (wet: 2.11 mm; dry: 1.15 mm), film-forming time (48 h), and slow-release of entrapped bacteria till 30 days. AlgB-OC exhibited 76% antifungal potential in vitro and managed 70% of the collar rot disease in vivo. Moreover, the application of AlgB-OC significantly improved plant growth (length and biomass) and physio-chemical attributes (photosynthetic pigments, total protein content, activity of CAT, POX, PPO, and PAL), along with more lignin, phenolics, gel, and starch accumulation in roots. Multivariate analysis based on a total of 13 morpho-physiological indices of chili plants also identified AlgB-OC as the most effective treatment for disease management. It was concluded that the formulation of O. ciceri into alginate could be an effective alternative for managing collar rot disease in chili plants and obtaining better plant growth. Antifungal potential of alginate beads of Ochrobactrum ciceri (AlgB-OC) against Sclerotium rolfsii (SR). In vitro antifungal assay (A), FTIR of the AlgB-OC (B), Effect of AlgB-OC on collar rot disease in chili caused by SR (C) and PCA-based analysis on disease, biophysical, and biochemical attributes of chili (D). [Display omitted] • Ochrobactrum ciceri was capable of suppressing the growth of Sclerotium rolfsii by suppressing the sclerotial formation and deteriorating hypha on agar plates and broth medium. • Stable alginate beads of O. ciceri (AlgB-OC) prepared by the ionic gelation method and in vitro exhibited 76% antifungal activity against S. rolfsii. • Plants' morpho-physiological and histo-chemical responses improved by AlgB-OC due to the management of 70% of the collar rot disease in vivo. • Formulation of O. ciceri into alginate could be an effective alternative for managing collar rot disease in chili plants and obtaining better growth. [ABSTRACT FROM AUTHOR]

Production of crops is increasing day by day in agriculture sectors. The insecurity of food is a main reason of plant disease and is a main global issue that humans face these days. With the design of contemporary environmental agriculture, more focus is devised for yielding the crop and elevating its quality. The occurrence of crops has elevated in years and the kind of disease has become more and more complex. The disease in plants and the pernicious insects are the major risks in agriculture field. Thus, earlier discovery and treatment of this disease are imperative. The major design of Deep Learning (DL) model helped in detecting the plant disease and grants a dynamic tool with accurate results. This paper presents DL-assisted technique for detecting and classifying the tomato disease and used deep batch-normalized eLu Alex Net (DbneAlexnet) for classifying the tomato plant leaves. Initially, tomato plant leaf images are taken as an input from specific dataset represented and it is subjected to preprocessing phase to eliminate unwanted distortions using anisotropic filtering. Then, the segmentation is carried out using U-net, which is trained by Gradient-Golden search optimization (Gradient-GSO) Algorithm and it is incorporation of both Golden search optimization (GSO) and Gradient concept. Thereafter the segmented image is given to image augmentation process, where position augmentation and color augmentation are considered. Finally, the multiclass plant leaf disease is classified using DbneAlexnet and is trained using proposed Gradient Jaya- Golden search optimization (GJ-GSO). Here, the GJ-GSO is devised with the integration of Gradient concept, Jaya algorithm, and GSO algorithm. The proposed GJ-GSO-based DbneAlexnet outperformed highest accuracy of 92.4%, True positive rate (TPR) of 91.9%, True negative rate (TNR) of 92.2% and smallest False Positive Rate (FPR) of 0.078. Hence, the technique with unified segmentation and classification is effectual for identifying the plant disease and the empirical research verifies the benefits of the developed model. • DL-assisted technique is provided for classifying tomato plant leaf disease. • Pre-processing is done with anisotropic filter. • U-net with Gradient-Golden search optimization is used for segmentation. • Accuracy, TPR, and TNR of 92.4%, 91.9%, 92.2% and FPR of 0.078 by proposed model. • Provided effective performance with certain metrics. [ABSTRACT FROM AUTHOR]

[Display omitted] • A large-scale survey uncovered bacterial wilt suppressiveness (BWS) of compost. • BWS was linked to rhizosphere microbiome but not compost properties. • BWS of compost was regenerative. Composts are often suppressive to several plant diseases, including the devastating bacterial wilt caused by Ralstonia solanacearum. However, the underlying mechanisms are still unclear. Herein, we carried out an experiment with 38 composts collected from different factories in China to study the interlinking among bacterial wilt suppression, the physicochemical properties and bacterial community of the compost, and bacterial community in the rhizosphere of tomato fertilized by compost. Totally 26 composts were suppressive to bacterial wilt, while six composts stimulated the disease. The control efficiency was neither correlated with physicochemical properties (TC, TN, P and K, pH or GI) nor bacterial community of compost, but with rhizosphere bacterial community (r = 0.17, p = 0.016). The control efficiency was also positive correlated with taxa (Rhizobium, Aeromicrobium) known suppressive to R. solanacearum. The mushroom spent or cow manure, from which the two composts were 100% and 77% in control efficiencies against bacterial wilt respectively were subject to a pilot-scale composting reaction. The reproduced composts from mushroom spent or cow manure were only 57% and 23% effective on the control of bacterial wilt, respectively. The analysis of bacterial communities revealed that the relative abundances of R. solanacearum were 28.4% for the control, but only 7.8%-7.9% for compost fertilized tomatoes. The compost from mushroom spent also exerted a strong effect on rhizosphere bacterial community. Taken together, most composts were suppressive to bacterial wilt possibly also by modifying rhizosphere bacterial community towards inhibiting the colonization of R. solanacearum and selecting for beneficial genera of Proteobacteria, Bacteroidetes and Actinobacteria. [ABSTRACT FROM AUTHOR]

Oomycetes are a group of eukaryotic microorganisms with species that cause devastating diseases across crops, aquaculture, and natural ecosystems. Genome engineering of oomycetes has been challenging. However, clustered regularly interspaced short palindromic repeats (CRISPR)-Cas technologies now enable specific point mutations and gene disruption, replacement, and complementation. Further studies have addressed nuclease toxicity and delivery, expanding the use of CRISPR-Cas genome editing into a wider range of species. Editing rates in oomycetes are variable between different studies due to challenges in delivery, expression, and design of the CRISPR-Cas components, but the current gaps can be overcome by ongoing innovation. Oomycetes are a group of microorganisms that include pathogens responsible for devastating diseases in plants and animals worldwide. Despite their importance, the development of genome editing techniques for oomycetes has progressed more slowly than for model microorganisms. Here, we review recent breakthroughs in clustered regularly interspaced short palindromic repeats (CRISPR)-Cas technologies that are expanding the genome editing toolbox for oomycetes – from the original Cas9 study to Cas12a editing, ribonucleoprotein (RNP) delivery, and complementation. We also discuss some of the challenges to applying CRISPR-Cas in oomycetes and potential ways to overcome them. Advances in CRISPR-Cas technologies are being used to illuminate the biology of oomycetes, which ultimately can guide the development of tools for managing oomycete diseases. [ABSTRACT FROM AUTHOR]

Phytophthora is a dreaded pathogen and difficult to control by traditional methods. Nanotechnology is a potential alternative for the management of Phytophthora diseases. Nanomaterials can be applied for the detection and delivery of fungicides, and therapy of pathogens. The nano-based tools are less toxic, cost-effective, and without risk of plants developing resistance. Members of the Phytophthora genus are soil-dwelling pathogens responsible for diseases of several important plants. Among these, Phytophthora infestans causes late blight of potatoes, which was responsible for the Irish potato famine during the mid-19th century. Various strategies have been applied to control Phytophthora, including integrated management programs (IMPs) and quarantine, but without successful full management of the disease. Thus, there is a need to search for alternative tools. Here, we discuss the emerging role of nanomaterials in the detection and treatment of Phytophthora species, including slow delivery of agrochemicals (microbicides and pesticides). We propose integrating these tools into an IMP, which could lead to a reduction in pesticide use and provide more effective and sustainable control of Phytophthora pathogens. [ABSTRACT FROM AUTHOR]

• We propose a novel hybrid deep learning model, PLDPNet, for automatic segmentation and classification of potato leaf diseases. • The PLDPNet utilizes auto-segmentation and deep feature ensemble fusion modules to enhance disease prediction accuracy. • The end-to-end performance of the proposed PLDPNet achieves a recorded accuracy of 98.66%. • A comprehensive ablation study is conducted using two additional plant leaf diseases: Apple (4 classes) and Tomato datasets (10 classes). Agricultural productivity plays a vital role in global economic development and growth. When crops are affected by diseases, it adversely impacts a nation's economic resources and agricultural output. Early detection of crop diseases can minimize losses for farmers and enhance production. In this study, we propose a new hybrid deep learning model, PLDPNet, designed to automatically predict potato leaf diseases. The PLDPNet framework encompasses image collection, pre-processing, segmentation, feature extraction and fusion, and classification. We employ an ensemble approach by combining deep features from two well-established models (VGG19 and Inception-V3) to generate more powerful features. The hybrid approach leverages the concept of vision transformers for final prediction. To train and evaluate PLDPNet, we utilize the public potato leaf dataset: early blight, late blight, and healthy leaves. Utilizing the strength of segmentation and fusion feature, the proposed approach achieves an overall accuracy of 98.66%, and F1-score of 96.33%. A comprehensive validation study is conducted using Apple (4 classes) and tomato (10 classes) datasets achieving impressive accuracies of 96.42% and 94.25%, respectively. These experimental findings confirm that the proposed hybrid framework provides more effective and accurate detection and prediction of potato crop diseases, making it a promising candidate for practical applications. [ABSTRACT FROM AUTHOR]

Plant diseases can significantly reduce food and agricultural production, leading to major quality, quantity, and economic losses. Plant disease deficits are usually reduced by early diagnosis through visual observation. Significant plant species grown in specific parts of the world include olives. Depending on the place where an olive tree is produced, many diseases can affect it. Traditional plant disease detection is ineffective and time-consuming. Therefore, this paper developed an in-depth evaluation of RNN architecture with an Ant Colony Optimization algorithm for disease identification in olive trees. It consists of a dataset that contains 3300 images of healthy and diseased leaves. The images are gathered in a dataset and then pro-processed. After pre-processing the images, the segmentation process is done using Wavelet transform. Then, the Ant Colony Optimization algorithm is employed for extracting the features. Lastly, categorization is done by applying RNN. The results suggest the optimal model for creating an efficient disease detector. The developed method attains higher performance when compared with other existing techniques. Hence, the outcomes demonstrate that the ACO-RNN technique has a reliable potential for identifying plant infections. [ABSTRACT FROM AUTHOR]

• O 3 risk assessment for Cupressus sempervirens was explored for the first time. • Stomatal O 3 uptake model was parameterized. • Flux-based index (POD 2) was the best approach to assess O 3 risk in C. sempervirens. • A critical level of 2.51 mmol/m2 POD 2 is recommended for C. sempervirens. • Seiridium cardinale infection altered O 3 risk assessment. Cupressus sempervirens is a relevant species in the Mediterranean for its cultural, economic and landscape value. This species is threatened by Seiridium cardinale , the causal agent of the cypress canker disease (CCD). The effects of biotic stressors on O 3 risk assessment are unknown and a comprehensive O 3 risk assessment in C. sempervirens is missing. To fill these gaps, two clones of C. sempervirens , one resistant (Clone R) and one susceptible to CCD (Clone S), were subjected to three levels of O 3 (Ambient Air - AA; 1.5 × AA; 2.0 × AA) for two consecutive years in an O 3 -free-air controlled exposure facility and artificially inoculated with S. cardinale. Both the exposure- (AOT40) and flux-based (PODy) indices were tested. We found that PODy performed better than AOT40 to assess O 3 effects on biomass and the critical level for a 4% biomass loss was 2.51 mmol/m2 POD 2. However, significant O 3 dose-response relationships were not found for the inoculated cypresses because the combination of middle level O 3 (1.5 × AA) and inoculation stimulated a biomass growth in Clone S as hormetic response. Moreover, we found a different inter-clonal response to both stressors with a statistically significant reduction of total and belowground biomass following O 3 , and lower root biomass in Clone S than in Clone R following pathogen infection. In summary, Clone R was more resistant to O 3 , and inoculation altered O 3 risk via an hormetic effect on biomass. These results warrant further studies on how biotic stressors affect O 3 responses and risk assessment. [ABSTRACT FROM AUTHOR]

Viral diseases of plants are exceptionally difficult to control in agriculture production. Recently, Gao et al. discovered that engineered site-selective nanoparticles (NPs), incorporating metal ion-based proteolytic activity and nanoscale chirality, can be used as potent, nontoxic, and environmentally friendly antiviral agents to kill plant viruses. [ABSTRACT FROM AUTHOR]

Application of disease-resistant varieties is the most effective and environmentally friendly way to control crop diseases. However, there is often a trade-off between disease resistance and yield. Several recent studies have demonstrated that genome-editing technology brings a new strategy for generating disease-resistant crops without yield penalties. [ABSTRACT FROM AUTHOR]

This research work involves data access and control of Unmanned Ground Vehicles (UGVs) which is designed for health monitoring of paddy crops as an exception from traditional monitoring system. The proposed scheme of autonomous navigation of drones is planned to be carried out as per the six milestones for disease detection and control in paddy crops. Drones serve as Unmanned Robotic Vehicles (URV) capable of performing desired tasks in unstructured, uncertain and potentially hostile environments and are remotely-operated without human intervention. URVs function completely as autonomous entities in diversified environments. Current UGVs adhere to different levels of automaticity. Typically the vehicle follows high level waypoints spaced for few hundred meters of distance to provide monitoring of agricultural fields and early detection of the various diseases that may occur in the paddy fields in a polyhouse. To increase the vehicle's abilities, tracking efficiency, obstacle avoidance, path planning or lead and follow up augmented control with Fuzzy Logic Controller (FLC) is incorporated. The distributed autonomous system for information gathering related to the paddy crops in polyhouse is enabled using different sensors, which is a data-intensive task. To increase the robustness of the system, fuzzy controllers are proposed to control the navigation of the proposed UGV in "All terrain conditions". They are needed to offer problem specific heuristic control knowledge for the Inference Engine Design which occurs due to imprecision and uncertainty of the sensor readings. It also requires low computation time which favours the polyhouse situations. The navigation of UGV and the FLC action will in turn depend on "All terrain traversability" and "dead zone" monitoring. The proposed UGV model is capable of measuring the parameters associated with the paddy crops inside a polyhouse. The various diseases in paddy crops are False Smut (FS), Sheath Blight (SB), Rice Blast (RB), Leaf Scald (LS), Brown Spot (BS), Bacterial Leaf Blight (BLB) and Bakane (BE) which are detected using Convolutional Neural Network (CNN). [ABSTRACT FROM AUTHOR]

• The genus Vepris has 80+ species endemic to tropical regions of Africa and Asia. • Ethnomedicinally, Vepris plants manage diseases such as rheumatism and malaria. • Alkaloids comprise 64.9% of the 208 reported compounds in this genus (as of Aug. 2022). • 93.8% of Vepris compounds obey Lipinski's rule of 5, based on predicted drug-likeness. The natural product remains a significant origin of bioactive compounds and therapeutic candidates due to their distinctive chemical structures and generally favorable metabolic and pharmacokinetic characteristics. In recent years, there has been a significant increase in the number of review articles detailing plants' phytochemistry and pharmacological properties within a certain genus. Additionally, databases for natural products have been increasing exponentially. However, the systematized ADME/Tox (absorption, distribution, metabolism, excretion, and toxicity) profile has only been discussed on a limited basis. The in-silico evaluation of the drug-like characteristics of isolated compounds from the genus Vepris is discussed in this review. The genus Vepris consists of over 80 species endemic in tropical regions of Africa and Asia. These plants are used ethnomedicinally for managing rheumatism, malaria, eye problem, pneumonia, headache, influenza, coughs, cardiac pains, leishmaniasis, and infertility. As a result of this broad usage, scientists have tried to explore the chemistry of different species within this genus, with 208 compounds reported as of August 2022. This genus is endowed with alkaloids at 64.9% of the reported compounds. Other compounds include terpenoids (17.8%), Flavonoids (7.7%), steroids (6.4%), coumarins (1%), and other miscellaneous compounds accounting for 6.3%. Analysis of predicted drug-likeness indicates that 93.8% of the compounds obey Lipinski's rule of 5, 92.8% are within the veber's rule, and 76.9% obey Ghose's rule. The ADME/Tox profile of these compounds shows that most of them have an acceptable profile and therefore have a high potential to be leads or drugs. [ABSTRACT FROM AUTHOR]

Agriculture and its allied sector provide the livelihoods of vast rural areas in India. It also plays a vital role in the growth of developing countries like India. Rice, one of the primary components of Indian agriculture plays a significant role in the food security of the country and the world. However, it is stricken by many diseases that affect the quality and quantity of rice yield. This paper proposes a custom CNN architecture for detecting and classifying common diseases found in rice plants by reducing the number of parameters associated with the network. The proposed CNN architecture has been trained using a dataset of four types of common rice plant diseases. In addition, 1400 on-field images of a healthy rice leaf image dataset have been introduced in the paper for the detection of disease-free plants as well. Independent experiments were carried out with and without the inclusion of the healthy leaf image dataset. The performance of the proposed model is evaluated with the use of Stochastic Gradient Descent with Momentum (SGDM) and Adaptive Moment Estimation (Adam) optimization techniques using several performance matrices. Experimental results from the dataset for the classification of four rice plant diseases show that the model using SGDM optimization gives maximum accuracy of 99.66% and the model using the Adam optimization gives maximum accuracy of 99.83% on the test set in the 7th epoch respectively. However, the model with Adam optimizer performed better than the model with SGDM optimizer when the healthy leaf image dataset is included giving a maximum accuracy of 99.66% and 97.61% in the 7th epoch respectively. [ABSTRACT FROM AUTHOR]

Detecting weeds, diseases, and pests in the agricultural field is a crucial concern for farmers. Weeds compete for light, nutrients, water, space and crop damage is caused by pests such as insects, mice, and birds and crop diseases pose a significant risk to food security. Farmers have relied on traditional methods to boost yield production in the past. The advancement of deep learning approaches helps to classify various types of photos in practice. For plant leaf disease detection, pest detection, and weed detection, we acquired many images of plant disease pairs, pest and weed images from varied crops. The data augmentation process is carried out because Deep Learning works effectively with larger data sets. The neural model was created using multiple types of DCNN architecture, and the models were interpreted based on their accuracy and performance. By fine-tuning the hyperparameters and layers of the DenseNet201, Mobilenet, VGG16, Hyperparameter Search and InceptionV3 on agricultural image data. The fine-tuned InceptionV3 model outperformed by 87.85% when comparing the final value. Mobilenet and VGG16, on the other hand, achieved accuracy of 91.85% and 78.71% respectively, DenseNet model performs well with 99.62% accuracy whereas the Hyperparameter Search with 71.07% accuracy. The model's effective performance in weed,plant disease and pest disease classification is described by the produced resultant value. [ABSTRACT FROM AUTHOR]

India is among the biggest tea exporter in the world. However, tea leaf diseases caused by persistent pathogen exposure result in considerable crop yield losses around the world. Detection of the disease of tea leaves at early stages can reduce the damage of tea output. Detecting the disease with the naked eye can be inefficient and counterproductive. Convolutional Neural Networks (CNNs) are commonly used to implement an effective method for the image classification. In detection of plant disease, the use of CNN is widespread. Therefore, in the proposed work, a Deep CNN having multiple hidden layers is considered for the classification of diseased tea leaves into different categories. This helps the network in detecting more number of features and thereby improving the accuracy in disease detection. The classification is done consisting of the following categories of leaves; Gray Blight, Algal Spot, Brown Blight, Helopeltis, Healthy Leaves and Red Spot. Further, a labeled dataset consisting of 5867 diseased and healthy tea leaf images have been created and uploaded on Kaggle. The suggested method demonstrates that the model is able to accurately detect the kind of persistent tea leaf disease with a 96.56% accuracy. The accuracy of the following disease classes are as follows, Algal Spot has an accuracy of 98.23%, Brown Blight has an accuracy of 97.98%, Gray Blight has an accuracy of 93.46%, Healthy classes of leaves has an accuracy of 99.10%, the Helopeltis disease class has an accuracy of 98.98% and Red Spot has an accuracy of 92% The model that is proposed in this literature is far superior than the existing methods in terms of accuracy. Furthermore this model can be adopted to work with various IoT devices to deploy in real world applications and the architecture of this model can be used to train different crop images to classify their diseases. [ABSTRACT FROM AUTHOR]

The agricultural sector plays a crucial role in the majority of developing countries like India. But in recent times agriculture production is following a downward trend due to various plant diseases with an increase in investment costs. This work conducts a survey on deep learning techniques training issues related to a Chili leaf diseases dataset. Especially the work focused on the viability of the Squeeze-Net training architecture on the Chili leaves to train the two classes of diseased leaves namely Geminivirus and Mosaic. The dataset comprised of 160 Chili diseased photographs deployed from the Kaggle public domain is subjected to the Squeeze-Net convolutional neural network (CNN) to test the training accuracy. The obtained training accuracy ranges from 50% to 100% by considering various training properties like CNN optimizers SGDM, ADAM, and RMSPROP w.r.t Max_Epoches and assigning Dropout probability, Strides, Dilation factor, and padding values as constants. From the simulation is observed that the Squeeze-Net CNN architecture is achieving 100% accuracy in ADAM, and RMSPROP, where Max_Epoches are 40 and 35 respectively. But it suggested that the applicability of RMSPROP is good for training the Chili Dataset, where Max_Epoches are very less compared to the ADAM and SGDM. [ABSTRACT FROM AUTHOR]

Crop disease in the plant is a significant issue in the agriculture sector, and it is currently very difficult to detect these illnesses in crop leaves. The foundation of the global economy is agriculture. India ranks second in the production of tomatoes worldwide. The tomato crop is affected by various diseases which lead to a reduction in product quality and quantity. The advancement in computer vision and deep learning opens up the door for predicting diseases that appear in the crops. The aim of this paper is classification among 10 different categories of tomato plant leaves using the proposed novel TomConv model which deploys an improved Convolutional Neural Network (CNN). For this purpose, the publicly available dataset called PlantVillage comprising of more than 16000 images of tomato leaves, both diseased and healthy was used for the experimentation purpose. The proposed model is the simplest model among all the available state-of-the-art models. The tomato leaf images were preprocessed for reducing the size in 150 × 150 dimension. The model constitutes four layered CNN followed by a max pooling layer. The model splits the corpus into training and validation datasets in 80:20 ratio, is trained under 105 epochs for tomato leaf images, and achieved an accuracy of 98.19%. The proposed model is compared with existing models under different parameters such as no. of classes, no. of layers, and accuracy. The results are promising as they outperform all the available state-of-the-art models. [ABSTRACT FROM AUTHOR]

Maize is the most versatile crop among cereals, regarding its adaptability, types, and uses. Annually, approximately 1150 million metric tons of maize are produced globally. The diseases are the main threat to the production of maize plants which also disturbs the quality and quantity of the produce. Leaves can be utilized to recognize these diseases easily. Hence, to overcome this issue, an efficient solution for diagnosing maize plant diseases has been proposed in this work. A customized convolutional neural network (CNN) based Maize Plant Disease Identification model is presented along with various combinations of preprocessing techniques, namely Contrast Limiting Adaptive Histogram Equalization (CLAHE) on each RGB (Red, Green, and Blue) channel, log transformation, and RGB to HSV (Hue, Saturation, and Variance) conversion of images. After preprocessing, these trained models are compared to the CNN and Support Vector-Machine (SV-M) models, trained without any preprocessing techniques. To estimate the effectiveness of the proposed work, the experiments were performed over PlantVillage Maize Crop Dataset. In this proposed work, a maximum accuracy of 96.76% is achieved. [ABSTRACT FROM AUTHOR]

Automation in the agricultural field is a requirement of all the countries. Usually, plant diseases are observed in the form of visual symptoms and many deep learning-based models have achieved outstanding results in the classification of plant leaf diseases in recent years. Beans plant diseases like bean rust disease and angular leaf spot disease reduce the bean crop yield. To treat the problem at an early stage, an appropriate diagnosis for this crop disease is required. In this paper, three deep learning-based pre-trained models namely MobileNetV2, EfficientNetB6, and NasNet were used to perform transfer learning on the Beans Leaf image dataset containing 1295 images with three different classes. Furthermore, different optimization techniques were also used to highlight the variation in performance of different Convolutional Neural Network (CNN) models. The analysis of experimental results shows that EfficientNetB6 performs better with 91.74% accuracy than other models. This study would be helpful to understand the role of different optimizers on the CNN models. Furthermore, agricultural scientists could employ a real-time-based application of the best-suited model for the farmers to adopt prevention measures in disease-vulnerable areas. As a result, prompt action would aid in minimizing plant productivity loss. It will further revenue the economic growth and agricultural productivity. [ABSTRACT FROM AUTHOR]

Microorganisms are associated with all plants, recently leading to the hologenome concept. We reviewed the assembly processes of plant microbiota and analyzed its structure during the emergence of dysbioses. In particular, we discussed the Anna Karenina Principle (AKP) based on Leo Tolstoy's assertion applied to plant microbiota: 'All healthy microbiota are alike; each disease-associated microbiota is sick in its own way.' We propose the AKP to explain how stochastic processes in plant microbiota assembly due to several external stressors could lead to plant diseases. Finally, we propose the AKP to conceptualize plant dysbioses as a transitory loss of host capacity to regulate its microbiota, implying a loss of function that leads to a reduction of the host's fitness. The involvement of plant microbiota in many plant functions, including resistance to abiotic or biotic stressors, water and nutrient intake, recently led to the holobiont concept. Shifts in microbiota composition linked to stress or disease is termed 'dysbiosis', but whether the shifts are a cause or a consequence of the stress or disease remains unknown. The Anna Karenina Principle (AKP) describes how microbiota are affected by external stressors and asserts that dysbiotic microbiota vary more among themselves than healthy microbiota. From this AKP and related new conceptualization, different dysbiosis scenarios can be identified by analyzing the β-diversity and evaluating the proportion of stochasticity in the community assembly. In response to external stress, the plant holobiont can either resist or recruit beneficial microorganisms from its environment. This latter mechanism emphasizes plant holobiont resilience. [ABSTRACT FROM AUTHOR]

Although ε-poly- l -lysine (ε-PL) has a good potential as a green fungicide, high concentration is usually required during its controlling of plant disease. On the other hand, same problems also appeared in the study of CuONP based nano pesticides. In this manuscript, a new composite alginate nanogel (ALGNP) that containing CuONP and ε-PL was fabricated via in situ reduction of CuONP in nanogel and ε-PL surface coating. Based on the chelation of amide bond of ε-PL and Cu2+ released by CuONP, the synergy effect between Cu2+ and ε-PL layer of the nanogel make the nanogel (CuONP@ALGNP@PL) performed high anti-fungal activity under low Cu2+ and ε-PL concentration (Cu concentration was 40.09 μg/mL, ε-PL concentration was 11.90 μg/mL). Study showed that the nanogel could more significantly destroy the fungal cell membrane than CuONP@ALGNP and ALGNP@PL, also better than commercial fungicide CuCaSO 4 (Cu concentration was 120 μg/mL). Furthermore, CuONP@ALGNP@PL could seriously affect the spore production, spore germination rate and bud tube elongation length of Alternaria alternate. Moreover, CuONP@ALGNP@PL also inhibit Botrytis cinerea , Phytophthora , Thanatephorus cucumeris and Fusarium graminearum. These results showed that composite of CuONP and ε-PL based on nanogel can decrease the raw materials application amount, and achieve a high disease controlling ability, which provides a new perspective for preventing fungal diseases. [ABSTRACT FROM AUTHOR]

[Display omitted] • NGS was used to precisely describe the microbiome of three strawberry cultivars. • Plant organs and genotype shape the composition of the microbiomes. • Strawberry microbiome delivers several beneficial functions to the plant. • Functional microbiome diversity correlates with cultivars' phenotypical differences. • Microbiome manipulation may enhance agricultural performances. Specific microbial communities are associated to host plants, influencing their phenotype and fitness. Despite the rising interest in plant microbiome, the role of microbial communities associated with perennial fruit plants remains overlooked. This work provides the first comprehensive description of the taxonomical and functional bacterial and fungal microbiota of below- and above-ground organs of three commercially important strawberry genotypes under cultural conditions. Strawberry-associated fungal and bacterial microbiomes were characterised by Next-Generation Sequencing and the potential functions expressed by the bacterial microbiome were analysed by both in silico and in vitro characterisation of plant growth-promoting abilities of native bacteria. Additionally, the association between the strawberry microbiome, plant disease tolerance, plant mineral nutrient content, and fruit quality was investigated. Results showed that the strawberry core microbiome included 24 bacteria and 15 fungal operational taxonomic units (OTUs). However, plant organ and genotype had a significant role in determining the taxonomical and functional composition of microbial communities. Interestingly, the cultivar with the highest tolerance against powdery mildew and leaf spot and the highest fruit productivity was the only one able to ubiquitously recruit the beneficial bacterium, Pseudomonas fluorescens , and to establish a mutualistic symbiosis with the arbuscular mycorrhiza Rhizophagus irregularis. This work sheds light on the interaction of cultivated strawberry genotypes with a variety of microbes and highlights the importance of their applications to increase the sustainability of fruit crop production. [ABSTRACT FROM AUTHOR]

[Display omitted] • Application of curdlan was improved by carboxymethylation. • CMCD OS was first prepared through enzymatic method. • CMCD OS has a promising application prospect on plant disease control in agriculture. Curdlan is produced by fermentation of microorganisms, which is an insoluble β-(1 → 3)-D-glucans. To better effectively utilize native curdlan, firstly, a derivative from curdlan, carboxymethylated curdlan (CMCD), with different degrees of substitution (DS) DS ∼ 0.20, DS ∼ 0.43 and DS ∼ 0.82 were prepared in this study. Carboxymethylation increases solubility in water more than native curdlan. Moreover, CMCDs were investigated to be hydrolyzed by Cc GluE, an endo -β-1 → 3-glucanase and generated the degradation products were oligosaccharides with degrees of polymerization (DP) mainly ranging from 2 to 7. Cc GluE also showed high thermal and pH stability when CMCD ∼ 0.43 was used as a substrate. Then these oligosaccharides generated by different CMCDs were applied to Arabidopsis and the activity in inducing defense responses were detected after being treated by the pathogen of Pseudomonas syringae pv tomato DC3000 (Pst DC3000). CMCD (DS ∼ 0.20) degradation oligosaccharide (CMCD ∼ 0.20 OS) pre-treatment was the just one that significantly enhanced the disease resistance to Pst DC3000, which is mediated by the salicylic acid (SA) signaling pathway in plants. The findings offer new insights into the application of curdlan, demonstrating that carboxymethylation enhances its solubility in water. Additionally, the oligosaccharide products derived from CMCD degradation show promising prospects for controlling plant diseases in agriculture. [ABSTRACT FROM AUTHOR]

Microbial seed coatings serve as effective, labor-saving, and ecofriendly means of controlling soil-borne plant diseases. However, the survival of microbial agents on seed surfaces and in the rhizosphere remains a crucial challenge. In this work, we embedded a biocontrol bacteria (Bacillus subtilis ZF71) in sodium alginate (SA)/pectin (PC) hydrogel as a seed coating agent to control Fusarium root rot in cucumber. The formula of SA/PC hydrogel was optimized with the highest coating uniformity of 90 % in cucumber seeds. SA/PC hydrogel was characterized using rheological, gel content, and water content tests, thermal gravimetric analysis, and Fourier transform infrared spectroscopy. Bacillus subtilis ZF71 within the SA/PC hydrogel network formed a biofilm-like structure with a high viable cell content (8.30 log CFU/seed). After 37 days of storage, there was still a high number of Bacillus subtilis ZF71 cells (7.23 log CFU/seed) surviving on the surface of cucumber seeds. Pot experiments revealed a higher control efficiency against Fusarium root rot in ZF71-SA/PC cucumber seeds (53.26 %) compared with roots irrigated with a ZF71 suspension. Overall, this study introduced a promising microbial seed coating strategy based on biofilm formation that improved performance against soil-borne plant diseases. [ABSTRACT FROM AUTHOR]

Fire blight, caused by the bacterium Erwinia amylovora , is a rapidly progressing and highly contagious disease of apple and pear, which leads to great economic losses with high recovery fees. To control fire blight, the infected trees are either treated with antibiotics or, in some countries, buried. To minimize the damage caused by E. amylovora and to manage its spread to other trees and orchards, early diagnosis of fire blight is critical. The direct extraction of E. amylovora cells or DNA from infected tissues is a laborious and time-consuming process, and a non-destructive diagnosis method has not yet been applied. In this study, we employed receptonics, a nanotechnology platform equipped with a receptor protein that perceives airborne bacterial signals and converts them into electronic signals, for onsite fire blight diagnosis. Two bacterial volatiles, 2,3-butanediol and 2-phenylethyl alcohol, were used as E. amylovora biomarkers both in vitro and in situ. To design receptor protein for identifying the target biomarkers, the bioprobe, nanodiscs human olfactory receptor family 1 subfamily D member 5 (hOR1D5), and Drosophila melanogaster or9a were engineered and fabricated. Our portable receptonics platform was able to detect both bacterial volatiles at concentrations as low as 10 pM within 10 s under field conditions. Thus, our results suggest that the receptonics platform could be used as a non-destructive diagnostic tool for the onsite, rapid, and accurate identification of plant diseases. • Fire blight is one of the major concerns in the agriculture field. • Early detection of the causing bacteria (E. amylovora) is important for Fire blight prevention. • A portable receptonics for on-site utilizing was developed. • The portable receptonics were demonstrated by monitoring in orchards. [ABSTRACT FROM AUTHOR]