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The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperforms the current methods in terms of accuracy, precision, and F1-score. The process of detecting sickness involves several consecutive steps. Capturing images, segmenting them, detecting edges, and determining the infection's severity are all steps in this process. To accomplish the goal of recognising and categorising different types of diseases that might impact tomato plants, the method of transfer learning is employed. As soon as the problem is identified, it is recommended to take proactive measures to help individuals and organisations involved in agriculture address the effects of these disorders using appropriate measures. [ABSTRACT FROM AUTHOR]

The interplay between plant hosts, phytopathogenic bacteria, and enteric human pathogens in the phyllosphere has consequences for human health. Salmonella enterica has been known to take advantage of phytobacterial infection to increase its success on plants, but there is little knowledge of additional factors that may influence the relationship between enteric pathogens and plant disease. In this study, we investigated the role of humidity and the extent of plant disease progression on S. enterica colonization of plants. We found that high humidity was necessary for the replication of S. enterica on diseased lettuce, but not required for S. enterica ingress into the UV-protected apoplast. Additionally, the Xanthomonas hortorum pv. vitians (hereafter, X. vitians)-infected lettuce host was found to be a relatively hostile environment for S. enterica when it arrived prior to the development of watersoaking or following necrosis onset, supporting the existence of an ideal window during X. vitians infection progress that maximizes S. enterica survival. In vitro growth studies in sucrose media suggest that X. vitians may allow S. enterica to benefit from cross-feeding during plant infection. Overall, this study emphasizes the role of phytobacterial disease as a driver of S. enterica success in the phyllosphere, demonstrates how the time of arrival during disease progress can influence S. enterica's fate in the apoplast, and highlights the potential for humidity to transform an infected apoplast into a growth-promoting environment for bacterial colonizers. [ABSTRACT FROM AUTHOR]

Plant leaf infections are a common threat to global production in both the long and short terms, affecting not only many farmers but also consumers around the world. Early detection and treatment of plant leaf diseases are essential to promote healthy plant growth in agriculture and ensure sufficient supply and health security for the world’s geometric (population) growth. Detection of plant leaf diseases using computer-aided technologies is widespread today. In the first part of this thesis, we describe an artificial intelligence (AI) model that enables image analysis to facilitate disease detection and then present its contribution at the societal level. We used images of maize leaves and images of apples in fields from the standard PlantVillage repository for object localization. An efficient deep learning (DL) modified mask region convolutional neural network (Mask R-CNN) is proposed for autonomous segmentation and detection of the object to be analyzed in this research. The proposed work exploited the features learned by the Mask R-CNN model at various processing hierarchies. We achieved improved code generation of singular images of the detected objects and an overall accuracy of 98.89% on the validation sets. In the rest of our research, we wanted to show the impact of our solution at a social level while highlighting the important aspects that characterize good development. The specificity of this approach is to present the different AI solutions used for the analysis of agricultural crops, with the aim of highlighting their benefits and their impact on human activities. [ABSTRACT FROM AUTHOR]

Opinion leaders play a significant role in promoting innovation and technology adoption through effective information dissemination to potential adopters. However, personal traits that define effective opinion leaders, as identified in existing literature, may not fully align with followers' desired attributes of an opinion leader. Additionally, little is known about how varying degrees of influence may impact the spread of knowledge. To bridge the gap, first, we conducted focus group discussions in 10 villages within the banana farming community in Rwanda to assess the critical attributes that farmers seek in opinion leaders. Results indicate that honesty, commitment, social, knowledge, and leading by example are the top five attributes that farmers value the most from an opinion leader for the control of Banana Xanthomonas Wilt – a major banana disease. Second, we collected data from 100 nominated opinion leaders, including 10 farmer promoters, using a structured questionnaire to determine how they transmit knowledge about disease management to farmers. The opinion leaders were categorised into low, medium, and high levels of influence based on pairwise comparison. We found that honesty, leading by example, and justice are significant attributes that distinguish opinion leaders with a higher level of influence and who are more effective in transmitting knowledge. Farmer promoters are not distinct from other opinion leaders in terms of access to farmers but are more active in knowledge transmission than any other opinion leader. Our results suggest that having access to farmers does not indicate influence. Therefore, we recommend engaging those farmer promoters and opinion leaders with a higher level of influence to disseminate information to farmers at the village level. [ABSTRACT FROM AUTHOR]

Agriculture plays a fundamental role in ensuring global food security, yet plant diseases remain a significant threat to crop production. Traditional methods to manage plant diseases have been extensively used, but they face significant drawbacks, such as environmental pollution, health risks and pathogen resistance. Similarly, biopesticides are eco-friendly, but are limited by their specificity and stability issues. This has led to the exploration of novel biotechnological approaches, such as the development of synthetic proteins, which aim to mitigate these drawbacks by offering more targeted and sustainable solutions. Similarly, recent advances in genome editing techniques—such as meganucleases (MegNs), zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats (CRISPR)—are precise approaches in disease management, but are limited by technical challenges and regulatory concerns. In this realm, nanotechnology has emerged as a promising frontier that offers novel solutions for plant disease management. This review examines the role of nanoparticles (NPs), including organic NPs, inorganic NPs, polymeric NPs and carbon NPs, in enhancing disease resistance and improving pesticide delivery, and gives an overview of the current state of nanotechnology in managing plant diseases, including its advantages, practical applications and obstacles that must be overcome to fully harness its potential. By understanding these aspects, we can better appreciate the transformative impact of nanotechnology on modern agriculture and can develop sustainable and effective strategies to mitigate plant diseases, ensuring enhanced agricultural productivity. [ABSTRACT FROM AUTHOR]

The rapid and accurate classification of Panax notoginseng leaf diseases is vital for timely disease control and reducing economic losses. Recently, image classification algorithms have shown great promise for plant disease diagnosis, but dataset quantity and quality are crucial. Moreover, classifying P. notoginseng leaf diseases faces severe challenges, including the small features of anthrax and the strong similarity between round spot and melasma diseases. In order to address these problems, we have proposed an ECA-based diffusion model and Inception-SSNet for the classification of the six major P. notoginseng leaf diseases, namely gray mold, powdery mildew, virus infection, anthrax, melasma, and round spot. Specifically, we propose an image generation scheme, in which the lightweight attention mechanism, ECA, is used to capture the dependencies between channels for improving the dataset quantity and quality. To extract disease features more accurately, we developed an Inception-SSNet hybrid model with skip connection, attention feature fusion, and self-calibrated convolutional. These innovative methods enable the model to make better use of local and global information, especially when dealing with diseases with similar features and small targets. The experimental results show that our proposed ECA-based diffusion model FID reaches 42.73, compared with the baseline model, which improved by 74.71%. Further, we tested the classification model using the data set of P. notoginseng leaf disease generation, and the accuracy of 11 mainstream classification models was improved. Our proposed Inception-SSNet classification model achieves an accuracy of 97.04% on the non-generated dataset, which is an improvement of 0.11% compared with the baseline model. On the generated dataset, the accuracy reached 99.44%, which is an improvement of 1.02% compared to the baseline model. This study provides an effective solution for the monitoring of Panax notoginseng diseases. [ABSTRACT FROM AUTHOR]

The high performance of deep learning networks relies on large datasets and powerful computational resources. However, collecting enough diseased training samples is a daunting challenge. In addition, existing few-shot learning models tend to suffer from large size, which makes their deployment on edge devices difficult. To address these issues, this study proposes a pruning-based lightweight few-shot learning (Prune-FSL) approach, which aims to utilize a very small number of labeled samples to identify unknown classes of crop diseases and achieve lightweighting of the model. First, the disease few-shot learning model was built through a metric-based meta-learning framework to address the problem of sample scarcity. Second, a slimming pruning method was used to trim the network channels by the γ coefficients of the BN layer to achieve efficient network compression. Finally, a meta-learning pruning strategy was designed to enhance the generalization ability of the model. The experimental results show that with 80% parameter reduction, the Prune-FSL method reduces the Macs computation from 3.52 G to 0.14 G, and the model achieved an accuracy of 77.97% and 90.70% in 5-way 1-shot and 5-way 5-shot, respectively. The performance of the pruned model was also compared with other representative lightweight models, yielding a result that outperforms those of five mainstream lightweight networks, such as Shufflenet. It also achieves 18-year model performance with one-fifth the number of parameters. In addition, this study demonstrated that pruning after sparse pre-training was superior to the strategy of pruning after meta-learning, and this advantage becomes more significant as the network parameters are reduced. In addition, the experiments also showed that the performance of the model decreases as the number of ways increases and increases as the number of shots increases. Overall, this study presents a few-shot learning method for crop disease recognition for edge devices. The method not only has a lower number of parameters and higher performance but also outperforms existing related studies. It provides a feasible technical route for future small-sample disease recognition under edge device conditions. [ABSTRACT FROM AUTHOR]

This paper discusses the mechanisms by which fungi manipulate plant physiology and suppress plant defense responses by producing effectors that can target various host proteins. Effector-triggered immunity and effector-triggered susceptibility are pivotal elements in the complex molecular dialogue underlying plant–pathogen interactions. Pathogen-produced effector molecules possess the ability to mimic pathogen-associated molecular patterns or hinder the binding of pattern recognition receptors. Effectors can directly target nucleotide-binding domain, leucine-rich repeat receptors, or manipulate downstream signaling components to suppress plant defense. Interactions between these effectors and receptor-like kinases in host plants are critical in this process. Biotrophic fungi adeptly exploit the signaling networks of key plant hormones, including salicylic acid, jasmonic acid, abscisic acid, and ethylene, to establish a compatible interaction with their plant hosts. Overall, the paper highlights the importance of understanding the complex interplay between plant defense mechanisms and fungal effectors to develop effective strategies for plant disease management. [ABSTRACT FROM AUTHOR]

Plant activators have emerged as promising alternatives to conventional crop protection chemicals for managing crop diseases due to their unique mode of action. By priming the plant's innate immune system, these compounds can induce disease resistance against a broad spectrum of pathogens without directly inhibiting their proliferation. Key advantages of plant activators include prolonged defense activity, lower effective dosages, and negligible risk of pathogen resistance development. Among the various defensive pathways targeted, the salicylic acid (SA) signaling cascade has been extensively explored, leading to the successful development of commercial activators of systemic acquired resistance, such as benzothiadiazole, for widespread application in crop protection. While the action sites of many SA-targeting activators have been preliminarily mapped to different steps along the pathway, a comprehensive understanding of their precise mechanisms remains elusive. This review provides a historical perspective on plant activator development and outlines diverse screening strategies employed, from whole-plant bioassays to molecular and transgenic approaches. We elaborate on the various components, biological significance, and regulatory circuits governing the SA pathway while critically examining the structural features, bioactivities, and proposed modes of action of classical activators such as benzothiadiazole derivatives, salicylic acid analogs, and other small molecules. Insights from field trials assessing the practical applicability of such activators are also discussed. Furthermore, we highlight the current status, challenges, and future prospects in the realm of SA-targeting activator development globally, with a focus on recent endeavors in China. Collectively, this comprehensive review aims to describe existing knowledge and provide a roadmap for future research toward developing more potent plant activators that enhance crop health. [ABSTRACT FROM AUTHOR]

Crop disease often leads to field epidemics with serious threats to yield. Early symptoms are sometimes difficult to identify, so the origin of primary inoculum is a critical focal point in the study of plant diseases, as it can help design management strategies to reduce crop losses. Here, we investigated whether anthracnose of water yams (Dioscorea alata L.) caused by the species complex Colletotrichum gloeosporioides can start from infected seed tubers from the previous harvest. Over two years, we collected tubers with varying pathogen prevalence in the field directly from producers and conducted fungal isolations in the lab to sample C. gloeosporioides. We also proceeded to artificially inoculate tubers before planting and monitored disease development. Finally, we genotyped isolates from leaves in the fields and assessed fixation indices between plots based on plot ownership (plots with a common seed tuber origin from a single farmer) vs. samples in plots from unrelated producers in Guadeloupe, Martinique, and Barbados. We were unable to isolate the fungus from harvested tubers in either sampling survey nor did any plants grown from inoculated tubers develop any disease symptoms during growth. Also, the genetic structure of samples within each plot was independent of plot ownership, though this occurred with varying levels in the different islands. These results suggest that contaminated planting material from seed tubers is not the primary source of the disease, which is in contrast to the common perception of yam anthracnose prevalence in the Antilles. [ABSTRACT FROM AUTHOR]

Late-season dehydration (LSDN) is a physiological disorder affecting grape berry water content, resulting in dehydration. Vineyards in the Aegean Region of western Türkiye have experienced problems with LSDN, particularly during periods of high temperatures. This research examines how partial canopy covering materials affect temperature differentials inside and outside the canopy, including the determination of LSDN grape berries of Sultan 7 (Vitis vinifera L.). A partial shading net (PS) was employed to prevent LSDN in the grape berries, and shading net and polyethylene material (PSP) were deployed to assess the impact of increasing canopy temperatures on the occurrence of LSDN in grape berries. Although partial covering materials did not substantially affect grapevine yield, the control group produced the largest and the heaviest berries. In the second year, warmer conditions led to more clusters with LSDN-affected berries and increased sunburn damage on clusters. PS showed a high healthy cluster rate of 72.50%, while PSP and control showed lower rates of 63.60% and 58.10%, respectively. Throughout the study period, PS exhibited 9.02% LSDN berries, while the control and PSP showed 17.10% and 16.70% clusters with LSDN berries in the total harvested clusters, respectively. The study showed that PS treatment alleviated LSDN symptoms in clusters. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) is a devastating plant disease caused by the pinewood nematode (PWN, Bursaphelenchus xylophilus) that is transmitted by several beetle species in the genus, Monochamus. Once present, the disease is difficult to control. Prevention rather than control is regarded as an effective strategy for PWD management. Central to this prevention strategy is the ability to predict the potential distribution of the disease. Here, we employed an integrated MaxEnt and CLIMEX approach to model the potential distribution of PWD under various climate‐change scenarios. Our results indicate that rising temperatures and lower humidity under climate change will render some of the northern regions of China more suitable for the nematode and these beetles, causing the gradual northward movement of PWD. Furthermore, suitable habitats for three pine species, Pinus massoniana, P. taiwanensis and P. shurbergia, overlap with PWN and Monochamus, suggesting that these three species are potentially at high risk of PWD. Thus, PWD management should target the northern regions of China and the three pine species that are most susceptible to PWD. [ABSTRACT FROM AUTHOR]

Cabbage (Brassica oleracea var. capitata L.) is a major cole crop in India. Cabbage leaf blight caused by Alternaria sp. is one of the serious foliar diseases responsible for low production and productivity. The disease is challenging to manage alone with fungicides or bioagents. Hence, the present study was undertaken to manage the disease effectively by utilizing bioagents, fungicides, and their combinations. The field trial was carried out in the Horticultural Research Farm, School of Agricultural Sciences, Nagaland University, Medziphema, Nagaland, during Rabi, 2022-2023 and in vitro evaluation of fungicides and bioagents conducted in the Department of Plant Pathology, School of Agricultural Sciences, Nagaland University, Medziphema, Nagaland. Management of leaf blight of cabbage was conducted with seven treatments (T1-T7). Among the different treatments tested in field condition, the minimum severity of Alternaria leaf blight (37.69%) coupled with maximum yield (23.39 t/ha) with a higher-cost benefit ratio of 2.72 were recorded in T6 (seed treatment with captan @ 3 g/kg + four foliar sprays of azoxystrobin 4.8% + chlorothalonil @ 0.25%). Among the six treatments, In vitro evaluation of bioagents Trichoderma sp. (TTV-2; T5) showed maximum inhibition with 69.44%, followed by Bacillus subtilis (CRB-7; T6) with 31.11% inhibition and among the chemical fungicides, azoxystrobin (amistar) showed the highest inhibition of 76.67% as compared to control. These treatments can effectively manage cabbage leaf blight disease and ensure sustained yields, particularly in the foothills of Nagaland. [ABSTRACT FROM AUTHOR]

Agriculture has undergone a remarkable transformation, transitioning from traditional methods that were used for centuries to technology-driven practices. The advent of image processing and computational intelligence has revolutionized crop production and plant health monitoring. From drones capturing detailed crop growth data to sensors meticulously measuring soil moisture levels, the possibilities are boundless. This review delves into the cutting-edge research advancements in the application of image processing and computational intelligence techniques for botanical fields, with a particular focus on plant health monitoring. First, it provides a comprehensive overview of the diverse imaging sensors employed in agriculture, including visible, near-infrared, thermal, and hyperspectral imaging. Subsequently, it carefully analyzes the advantages and limitations of each sensor type, along with illustrative examples of their utilization in plant health monitoring. The review further explores the application of machine learning and deep learning for automated plant disease identification, highlighting the critical need for standardized datasets, benchmarking protocols, and domain-specific knowledge for effective implementation. In conclusion, the review emphasizes the future challenges and trends in this rapidly evolving field. It serves as a valuable resource, providing insights into the latest trends in computer vision-based plant disease monitoring and identifying gaps that demand further attention from the scientific community. [ABSTRACT FROM AUTHOR]

Plant pests and diseases can have a major impact on the operation of botanic gardens and the conservation of plants more broadly. Botanic Gardens of Sydney have a wide range of strategic initiatives to manage biosecurity to ensure existing problems are managed, new incursions prevented, and that pests and diseases do not spread from our gardens. Action on the conservation of plant species threatened by exotic pests and diseases also requires integrated inter-disciplinary science to manage these threats that includes conservation horticulture, seed banking and cryopreservation, genomics and disease detection and management. [ABSTRACT FROM AUTHOR]

The article titled "Mixed viral infections in vegetable crops: biochemical and molecular aspects" analyzes the co-infections of viruses in vegetable plants, which cause exacerbated symptoms in leaves and fruits. These co-infections can have synergistic, antagonistic, or neutral interactions that affect the severity of symptoms and the spread of infection. The importance of paying attention to possible changes in the transmission dynamics and prevalence of viral diseases in crops due to anthropogenic and natural factors is highlighted. Additionally, the synergistic, antagonistic, and neutral effects that can occur in viral co-infections are mentioned. The article focuses on agro-industrial vegetables such as Capsicum spp., Solanum lycopersicum, and other Cucurbitaceae, and explores the biochemical and molecular aspects of these mixed infections. [Extracted from the article]

A study conducted in Montecillo, Texcoco, Mexico, identified two viruses associated with symptoms of viral diseases in bean crops. The detected viruses were Orthotospovirus impatiensnecromaculae and two isolates of Potyvirus phaseoluteum. These viruses present significant differences from other viruses reported in different plant species. This finding highlights the importance of accurate identification for the management and control of these viral infections in bean crops in Mexico. [Extracted from the article]

Fungal infections are among the most common diseases of crop plants. Various species of the Fusarium spp. are naturally prevalent and globally cause the qualitative and quantitative losses of farming commodities, mainly cereals, fruits, and vegetables. In addition, Fusarium spp. can synthesize toxic secondary metabolites—mycotoxins under high temperature and humidity conditions. Among the strategies against Fusarium spp. incidence and mycotoxins biosynthesis, the application of biological control, specifically natural plant extracts, has proved to be one of the solutions as an alternative to chemical treatments. Notably, rowanberries taken from Sorbus aucuparia are a rich source of phytochemicals, such as vitamins, carotenoids, flavonoids, and phenolic acids, as well as minerals, including iron, potassium, and magnesium, making them promising candidates for biological control strategies. The study aimed to investigate the effect of rowanberry extracts obtained by supercritical fluid extraction (SFE) under different conditions on the growth of Fusarium (F. culmorum and F. proliferatum) and mycotoxin biosynthesis. The results showed that various extracts had different effects on Fusarium growth as well as ergosterol content and mycotoxin biosynthesis. These findings suggest that rowanberry extracts obtained by the SFE method could be a natural alternative to synthetic fungicides for eradicating Fusarium pathogens in crops, particularly cereal grains. However, more research is necessary to evaluate their efficacy against other Fusarium species and in vivo applications. [ABSTRACT FROM AUTHOR]

Rhizoctonia solani is an important plant pathogen which is responsible for causing a number of plant diseases in a variety of cropping plants. The current studies were carried out to explore antifungal potential of bacteria isolated from rhizospheric region of Daucus carota against Rhizoctonia. Of the total 21 bacterial isolates isolated, only 3 isolates namely CVO-3, CVO-4 and CRY-O-2 were shown antifungal potential against Rhizoctonia. Besides having antifungal potential these isolates also have some plant growth promoting attributes that will be an extra benefit of using these isolates as a biofungicides against R. solani. Thus, these isolates could be used as biofungicides against Rhizoctonia to prevent a variety of plant diseases like sheath blight in Oryza sativa, stem canker and black scurf in Solanum tuberosum and seedling diseases in Brassica napus. The isolates CVO-3, CVO-4 and CRY-O-2 were also characterized biochemical level by using methods based on Determinative Bacteriology given in Bergey's manual and at molecular level using 16S rRNA sequencing and sequences of 16S rDNA segment were submitted to NCBI in GenBank database with the accession numbers OR775574 (Lysinibacillus boronitolerans ss3cvo3), OR775575 (Lysinibacillus boronitolerans ss4cvo4) and OR708523 (Shouchella rhizosphaerae ss1cryo2). The present investigation is an approach towards the use of these bacterial isolates from carrot rhizosphere to control the activities in the rhizosphere of other cropping plants. [ABSTRACT FROM AUTHOR]

Banana is one of the primary fruits cultivated in Malaysia and currently decimated by the emergence of a disease, known as banana blood disease (BBD) which caused by Ralstonia syzygii subsp. celebesensis (Rsc). The BBD has significantly affected the yield and profits of the worldwide banana industry. To date, various approaches including chemical and biological controls have been attempted to manage this disease but none of them succeed in controlling the disease. The uses of lactic acid bacteria (LAB) in managing plant diseases have been reported earlier but little information is available. Therefore, this project is designed to identify and investigate potential plant-associated LAB as biological control agent (BCA) against Rsc by using agar well diffusion method. The inhibition zones of each well were observed after 72h and the isolated LAB that showed inhibition zones were proceed for molecular characterization using PCR amplification followed by gel electrophoresis. The sequences were used for phylogenetic analysis. In addition, each of the potential LAB were used to identify their morphological characterizations and biochemical testing. Throughout the study, the highest inhibition zones of LAB from kimchi and fermented milk achieved a diameter of 21.30 mm and 28.70 mm, respectively. Kimchi isolates showed the highest similarity which is 97% as Lactiplantibacillus plantarum species. Among the fermented milk isolates, the highest similarity which is 98% identified as Lacticaseibacillus paracasei. [ABSTRACT FROM AUTHOR]

The impact of climate change on plant growth and productivity is profound, resulting in significant stresses such as biotic and abiotic factors. Among the abiotic stresses, temperature extremes, nutrient imbalances, heavy metals, drought, and salinity stand out as prominent contributors to substantial crop yield losses globally, exacerbating food insecurity concerns. Various methods are currently being explored to enhance plants' resilience to these stresses. We undertook an extensive literature review to assess the current landscape of knowledge in Nano biotechnology and identify areas where existing research is deficient or underexplored. This examination focused on identifying gaps related to methodology, scope, applications, and specific challenges within nanobiotechnology. We also evaluated recent developments and emerging trends in the field, identifying areas where research interest is expanding but remains relatively uncharted. By narrowing our research scope, we aimed to pinpoint specific aspects of Nano biotechnology requiring further investigation or that have not been sufficiently addressed. One promising approach involves the use of nanoparticles, which have shown to positively influence plant performance under stressful conditions. Nanoparticles facilitate nutrient uptake by plants, combat plant diseases and pathogens, and enable the detection and monitoring of trace components in soil through signal absorption. Understanding the mechanisms by which nanoparticles assist plants in mitigating abiotic challenges holds great promise for developing enduring solutions. However, urgent action is needed to implement rapid strategies that alleviate abiotic stress and enhance agricultural productivity. [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]

Potassium (K) fertilisation has frequently been shown to enhance plant resistance against pathogens, though the mechanisms remain elusive. This study investigates the interaction dynamics between Nicotiana benthamiana and the pathogen Alternaria longipes under different planta K levels. On the host side, adding K activated the expressions of three NLR (nucleotide‐binding domain and leucine‐rich repeat‐containing proteins) resistance genes, including NbRPM1, NbR1B23 and NbNBS12. Silencing these NLRs attenuated resistance in high‐K (HK, 40.8 g/kg) plant, whereas their overexpression strengthened resistance in low‐K (LK, 23.9 g/kg) plant. Typically, these NLRs mainly strengthened plant resistance via promoting the expression of pathogenesis‐related genes (PRs), ROS burst and synthesis of antifungal metabolites in HK plant. On the pathogen side, the expression of effectors HKCSP1, HKCSP2 and LKCSP were shown to be related to planta K content. A. longipes mainly expressed effectors HKCSP1 and HKCSP2 in HK plant to interfere host resistance. HKCSP1 physically interacted with NbRPM1 to promote the degradation of NbRPM1, then attenuated related resistance in HK N. benthamiana. Meanwhile, HKCSP2 directly interacted with NbPR5 to suppress resistance in HK plant. In LK plant, A. longipes mainly deployed LKCSP that interacted with NbR1B23 to interfere reduce resistance in N. benthamiana. Overall, our research insights that both pathogen and host mobilise distinct strategies to outcompete each other during interactions in different K nutrient environments. Summary statement: The present study demonstrated that changes in planta K content could mediate the interaction pattern between host plants and pathogens, eventually leading different outcome of plant disease. Although the pathogen tries to suppress host immune by deploying different virulence factors, the host can benefit much more due to its sufficient resistance events under high K condition. Our results add to evidence that K fertilisation can be adjusted to optimise suppression of fungal diseases and thus has value as a disease management tool. [ABSTRACT FROM AUTHOR]

Crop diseases are among the major natural disasters in agricultural production that seriously restrict the growth and development of crops, threatening food security. Timely classification, accurate identification, and the application of methods suitable for the situation can effectively prevent and control crop diseases, improving the quality of agricultural products. Considering the huge variety of crops, diseases, and differences in the characteristics of diseases during each stage, the current convolutional neural network models based on deep learning need to meet the higher requirement of classifying crop diseases accurately. It is necessary to introduce a new architecture scheme to improve the recognition effect. Therefore, in this study, we optimized the deep learning-based classification model for multiple crop leaf diseases using combined transfer learning and the attention mechanism, the modified model was deployed in the smartphone for testing. Dataset that containing 10 types of crops, 61 types of diseases, and different degrees was established, the algorithm structure based on ResNet50 was designed using transfer learning and the SE attention mechanism. The classification performances of different improvement methods were compared by model training. Result indicates that the average accuracy of the proposed TL-SE-ResNet50 model is increased by 7.7%, reaching 96.32%. The model was also integrated and implemented in the smartphone and the test result of the application reaches 94.8%, and the average response time is 882 ms. The improved model proposed has a good effect on the identification of diseases and their condition of multiple crops, and the application can meet the portable usage needs of farmers. This study can provide reference for more crop disease management research in agricultural production. [ABSTRACT FROM AUTHOR]

Ever since the first crops were domesticated over 10,000 years ago, pests and pathogens have wreaked havoc on crop production. Despite diverse threats from longstanding and emerging diseases, plants continue to play a central role in meeting humanity's requirements for calories and nutrients. In this review, we discuss historical advances made by plant pathologists and breeders that have helped keep many plant diseases at bay. We focus predominantly on fungal diseases of five food crops—rice, wheat, maize, soybean and potato—that provide the bulk of calories consumed globally. We cover such historical triumphs in plant pathology as (i) advanced knowledge of fungal biology revolutionizing disease management strategies, (ii) integration of genetics by the 'wheat wizard' Rowland Biffen and others transforming the landscape of resistance breeding and (iii) the advent of modern fungicides—from a sinking ship in the 1660s—leading to dramatic yield enhancements. We also discuss (iv) the potential for emerging biocontrol agents to holistically promote plant health and yield. Today, the high homogeneity of crops in modern agricultural systems exacerbates yield losses due to disease by increasing pest/pathogen transmission rates and eroding natural spatial barriers to pest/pathogen dispersal. Meanwhile, climate change is altering the compositions of global pest and pathogen populations and expanding pest/pathogen territories with unpredictable consequences. As we look to the future, the strength of the plant pathology community remains central to developing innovative solutions to tackle these complex and expanding threats to plant health and secure a plentiful food supply. [ABSTRACT FROM AUTHOR]

Bud rot (BR), caused by Phytophthora palmivora, limits growth and development of oil palms in the American continent, particularly in Colombia. Due to the absence of systematic epidemiological analyses and determination of risk factors associated with BR, this study aimed to employ deterministic mathematical models and frequentist and Bayesian statistical methods to quantify the genetic response and edaphoclimatic variables as risk indicators of BR. From 2011 to 2014, the severity of BR in oil palm crops was recorded monthly in two locations: Tumaco (hot spot) and Villavicencio (cold spot), determining the edaphoclimatic variables at each site. Using the area under the disease progress stairs curve, temporal models were applied to determine the rate of disease development (R0) to quantify risk at locality and genotype levels. Subsequently, the observed intensity and severity levels of BR were adjusted to models such as survival curves, Cox proportional hazard risk and transition probabilities, or Markov states, with the aim of quantifying and characterizing risk factors associated with genotype and edaphoclimatic variables at each location. The risk in Tumaco was three times higher than in Villavicencio, and the R0 values were differential by genotype, with Tenera more susceptible than Hybrids. Moreover, the risk of BR increased when periods of 2–3 months occurred with successive instances of precipitation and relative humidity greater than 150 mm/month and 90%, respectively, and when manganese and zinc levels were below optimal. This approach allowed us to characterize epidemiological factors that cause plant diseases, allowing quantification of the risk of BR. [ABSTRACT FROM AUTHOR]

Plant disease control faces a lot of challenges due to its overdependence on chemicals that have strict restrictions and regulatory requirements. With the increase in drug‐resistant pathogens and continual crop losses due to disease outbreaks, much attention has been brought to a new set of emerging antibiotics called antimicrobial peptides (AMPs). AMPs are a group of multifunctional, short‐sequence peptides that are usually cationic in nature and found in all living organisms. They are part of the innate immune system of different organisms and exhibit a wide range of inhibitory effects on microorganisms, making them potential therapeutic factors efficacious as an alternate resource for plant disease management. AMPs interact with the anionic cell membrane of the pathogen and cause cell lysis or inhibit crucial intracellular targets. AMPs can be isolated from almost all life forms ranging from microbes to mammals. In addition to these sources, AMPs are also being synthesized using recombinant methods with the goal of overcoming the constraints of natural AMPs with regard to stability, activity and toxicity. Recent advancements have been made to develop transgenic plants expressing AMPs that has proved to perform better than the use of antibiotics. This review highlights the different kinds of AMPs produced by plants and microorganisms along with their mode of action, target pathogens, structural characteristics and advancements in this field, which include isolation methods, synthetic AMPs and transgenic plants expressing AMPs. [ABSTRACT FROM AUTHOR]

Zinc oxide nanoparticles (ZnO NPs) are inorganic compounds listed as "generally recognized as safe" (GRAS) materials and have been used in plant production as well as for plant disease control. This study investigated the antibacterial efficacy of ZnO NPs with various surface areas against Xanthomonas campestris pv. campestris, assessed the effectiveness of ZnO NPs in controlling black rot disease in Chinese kale, and examined the influence of ZnO NPs application on soil bacterial communities. The results showed that ZnO NPs with high surface area effectively inhibited X. campestris pv. campestris by restraining growth and causing cell damage. Seed treatment and foliar spray application of high surface area ZnO NPs at 250 μg/mL significantly reduced the disease severity of black rot. Furthermore, in the greenhouse experiment, the soil bacterial communities in the treatment of plants applied with ZnO NPs did not differ from those in soil of nontreated plants. Therefore, ZnO NPs have the potential to serve as an alternative substance for plant disease management. [ABSTRACT FROM AUTHOR]

The walnut industry in Argentina has grown in recent years, especially in northern Patagonia. However, this expansion has also brought new challenges, including the emergence of diseases threatening the health and productivity of walnut plantations. One of the most important diseases affecting walnut crops worldwide is 'ink disease' or 'walnut dieback'. The aim of this study was to characterize ink disease, identify the aetiological agents, and estimate its incidence in walnut orchards of northern Patagonia, Argentina. Symptomatic trees were observed in the seven surveyed walnut orchards, showing a range of symptoms, including chlorosis and wilting, stem cankers and desiccation. In vivo assay and molecular analyses revealed that not only Phytophthora (P.) but also Phytopythium (Ph.) species are the causal agents of the disease, with P. drechsleri, Ph. mercuriale and Ph. vexans being the most virulent over the commonly used rootstock Juglans hindsii. Notably, Ph. vexans was the most frequently isolated pathogen, suggesting its prevalence and potential impact on the regional walnut industry. Overall, this study provides critical insights into the prevalence and impact of Phytophthora and Phytopythium species on walnut health in northern Patagonia. The presence of multiple pathogenic oomycete species raises concerns about the health of walnut orchards, as well as the potential risk of pathogen transmission to other fruit crops in the region. The findings will help growers to develop better cultural practices and implement measures to guarantee the sustainability of the walnut industry in Patagonia. [ABSTRACT FROM AUTHOR]

Tebuconazole (TEB) is a common triazole sterol demethylation inhibitor fungicide utilized to manage a variety of diseases in crops like cereals, fruits, and vegetables. The aim of this work was to assess the effects of TEB on the structure of the cerebellum in adult albino rats and possible protective impact of co-administration of Gallic acid (GA). Four groups of forty adult male albino rats were randomly selected, and the rats in group I received corn oil through daily gavage for 4 weeks. Group II received GA dissolved in the normal saline at a dose of 100 mg/kg through daily gavage for 4 weeks, group III administered with TEB dissolved in corn oil at its acceptable daily intake dose (0.02 mg/kg body weight) through daily gavage for 4 weeks, group IV rats received both TEB and GA. For light microscopic, ultrastructural, and immunohistochemical investigations, cerebellar specimens were prepared. TEB exposure led to neuronal damage in the form of degenerated Purkinje cells with vacuolated cytoplasm, areas of lost Purkinje cells, the basket cells appeared vacuolated with degenerated neuropil, the granule cells clumped with congested areas between them, dilated cerebellar islands, weak positive bcl2 immunoreactions in the Purkinje cells, and numerous GFAP-positive astrocytes. GA mitigated TEB-mediated histological changes in the cerebellar cortex. We concluded that TEB caused Purkinje neurons in the rat cerebellar cortex to degenerate and undergo apoptosis. GA had a neuroprotective benefit against TEB toxicity in the rat cerebellar cortex. [ABSTRACT FROM AUTHOR]

The growing population increases the demand for agricultural production. Owing to some plant diseases, this production is continuously decreasing. Detecting and recognising these diseases at the initial stage is crucial for the sake of high yields from crops and plants. The conventional methods involve manual detection, which can be error-prone and laborious. Automatic recognition paves a way for the regular monitoring of huge areas of crops in less time. The deep learning-based approaches have entered in a big way in the detection of diseases in plants and crops because of their ability to overcome the limitation of regular manual monitoring. The use of colour space models helps demarcate the affected area ofimages of plants and crops prior to inputting them to the CNN model so as to improve the detection accuracy. For this, different colour space models that include RGB, HSL, HSV, LAB, LUV, XYZ and YUV are investigated. However, HSL-CNN joint model performs the best by achieving 98.97% accuracy, 98.61% precision, and 99.28% F1-score. [ABSTRACT FROM AUTHOR]

This study reviews the impact of pest and disease attacks on shallot plants (Allium ascalonium L.) and control strategies to increase plant production. Pests such as Spodoptera exigua (onion caterpillar), Thrips tabaci (thrips), Spodoptera ltura F. (armyworm), Liriomyza spp. (leaf slitting fly), and Gryllotalpa spp. (orong-orong), as well as diseases such as trotol or purple spot (Purple blotch), anthracnose (Antracnose), downy mildew, moler or fusarium wilt (Twisting disease), leaf blight (Stemphylium leaf blight), and leaf spot (Cercospora leaf spot) cause a significant decrease in production hasl. Pest and disease control still relies heavily on chemical pesticides, which pose a risk to the environment and health. Alternative sustainable control such as the use of biopesticides from mahogany seed extract (Swietenia mahagoni), Japanese papaya leaf extract (Cnidoscolus aconitifolius), and legundi weed extract (Vitex trifolia L.), as well as the application of Plant Growth Promoting Microorganisms (PGPM) from bamboo root can have a very good influence on controlling pests and diseases of shallot plants and can help in increasing plant productivity. [ABSTRACT FROM AUTHOR]

Pesticide spraying is a cost-effective way to control crop pests and diseases. The effectiveness of this method relies on the deposition and distribution of the spray droplets within the targeted application area. There is a critical need for an accurate and stable detection algorithm to evaluate the liquid droplet deposition parameters on the water-sensitive paper (WSP) and reduce the impact of image noise. This study acquired 90 WSP samples with diverse coverage through field spraying experiments. The droplets on the WSP were subsequently isolated, and the coverage and density were computed, employing the fixed threshold method, the Otsu threshold method, and our Genetic-Otsu threshold method. Based on the benchmark of manually measured data, an error analysis was conducted on the accuracy of three methods, and a comprehensive evaluation was carried out. The relative error results indicate that the Genetic-Otsu method proposed in this research demonstrates superior performance in detecting droplet coverage and density. The relative errors of droplet density in the sparse, medium, and dense droplet groups are 2.7%, 1.5%, and 2.0%, respectively. The relative errors of droplet coverage are 1.5%, 0.88%, and 1.2%, respectively. These results demonstrate that the Genetic-Otsu algorithm outperforms the other two algorithms. The proposed algorithm effectively identifies small-sized droplets and accurately distinguishes the multiple independent contours of adjacent droplets even in dense droplet groups, demonstrating excellent performance. Overall, the Genetic-Otsu algorithm offered a reliable solution for detecting droplet deposition parameters on WSP, providing an efficient tool for evaluating droplet deposition parameters in UAV pesticide spraying applications. [ABSTRACT FROM AUTHOR]

The synergy between artificial intelligence (AI) and hyperspectral imaging (HSI) holds tremendous potential across a wide array of fields. By leveraging AI, the processing and interpretation of the vast and complex data generated by HSI are significantly enhanced, allowing for more accurate, efficient, and insightful analysis. This powerful combination has the potential to revolutionize key areas such as agriculture, environmental monitoring, and medical diagnostics by providing precise, real-time insights that were previously unattainable. In agriculture, for instance, AI-driven HSI can enable more precise crop monitoring and disease detection, optimizing yields and reducing waste. In environmental monitoring, this technology can track changes in ecosystems with unprecedented detail, aiding in conservation efforts and disaster response. In medical diagnostics, AI-HSI could enable earlier and more accurate disease detection, improving patient outcomes. As AI algorithms advance, their integration with HSI is expected to drive innovations and enhance decision-making across various sectors. The continued development of these technologies is likely to open new frontiers in scientific research and practical applications, providing more powerful and accessible tools for a wider range of users. [ABSTRACT FROM AUTHOR]

Early blight (Alternaria solani) is one of important diseases in potato in Indonesia. In previous study, a microbial consortium (Trichoderma harzianum, Bacillus subtilis and Pseudomonas sp.) suppressed the bacterial wilt disease in potato. For application, the microbial consortium was mixed with compost (10%). As the microbial consortium is originally intended for controlling soil-borne diseases, this study examined the ability of the microbial consortium to suppress early blight disease and the effects of application methods on its efficacy. The application methods examined were the methods that targeted for soil-borne as well as airborne diseases. The experiment was conducted in a greenhouse at the Department of Plants and Diseases, Faculty of Agriculture, Universitas Padjadjaran, Sumedang, West Java, Indonesia. The experiment used Randomized Complete Block Design with 10 treatments and three replications. The treatments included application of microbes mixed with compost in planting hole solely or in combination with soaking the potato tubers in suspension of the microbes, drenching water extract of the mixture at 2, 4, 6 weeks after planting (WAP) with or without foliar application of the water extract. The result showed that application method influenced the abilities of the microbial mixture in suppressing early blight disease and supporting the potato growth. Even though the application of microbe-compost in planting medium inhibited the development of early blight disease by 37.8-39.5%, the regular foliar spray of the water extract of the mixture enhanced the disease suppression. Combination between application of the microbe-compost in the planting hole, drenching its water extract at 2, 4, 6 WAP and weekly foliar spray of the water extract suppressed early blight disease by 80.8%. This combination of application methods also supported better plant height, numbers of potato leaflets, roots fresh weight and production of potato tuber. [ABSTRACT FROM AUTHOR]

Potato crop has become integral part of our diet due to its wide use in variety of dishes, making it an important food crop. Its importance also stems from the fact that it is one of the cheapest vegetables available throughout the year. This makes it crucial to keep potato prices affordable for developing countries where the majority of the population falls under the middle-income bracket. Consequently, there is a need to develop a robust, effective, and portable technique to detect diseases in potato plant leaves. In this work, an attention-based disease detection technique is proposed. This technique selectively focuses on specific parts of an image which reveal the disease. This technique leverages transfer learning combined with two attention modules: the channel attention module and spatial attention module. By focusing on specific parts of the images, the proposed technique is able to achieve almost similar accuracy with significantly fewer parameters. The proposed technique has been validated using four pre-trained models: DenseNet169, XceptionNet, MobileNet, and VGG16. All of these models are able to achieve almost the same level of training and validation accuracy, around 90–97%, even after reducing the number of parameters by 40–50%. It shows that the proposed technique effectively reduces model complexity without compromising performance. [ABSTRACT FROM AUTHOR]

Asparagus stem blight, also known as "asparagus cancer", is a serious plant disease with a regional distribution. The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production. To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test, a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed. The algorithm aims to achieve rapid detection of phenotypic images of asparagus stem blight and to provide effective assistance in the control of asparagus stem blight. To enhance the model's capacity to capture subtle lesion features, the Convolutional Block Attention Module (CBAM) is added after C2f in the head. Simultaneously, the original CIoU loss function in YOLOv8 was replaced with the Focal-EIoU loss function, ensuring that the updated loss function emphasizes higher-quality bounding boxes. The YOLOv8-CBAM algorithm can effectively detect asparagus stem blight phenotypic images with a mean average precision (mAP) of 95.51%, which is 0.22%, 14.99%, 1.77%, and 5.71% higher than the YOLOv5, YOLOv7, YOLOv8, and Mask R-CNN models, respectively. This greatly enhances the efficiency of asparagus growers in identifying asparagus stem blight, aids in improving the prevention and control of asparagus stem blight, and is crucial for the application of computer vision in agriculture. [ABSTRACT FROM AUTHOR]

The existence of crop disease leads to a substantial loss in quantity and quality in agricultural production. The regular monitoring of plants helps in preventing leaf disease at the early stages and is one of the beneficial acts in preserving the plant. This research focuses on developing an automated approach to classify plant leaf disease using Efficient CapsNet with Self-Attention Routing (EC-SAR). The raw data is obtained from plant village and CGIAR dataset which is pre-processed by color enhancement and data augmentation. Next to pre-processing, segmentation is performed using K-means clustering and Otsu threshold techniques which effectively segment the partitions of leaves. The feature extraction is performed using Gray Level Co-Occurrence Matrix (GLCM), Local Ternary Pattern (LTP) and Histogram Oriented Gradients (HoG) techniques. After this, the classification is performed using the proposed EC-SAR which is comprised of an efficient capsule network that adapts itself to the large input data. The self-attention layer routing is performed to choose a better capsule that is suitable to generate feature representation based on actual data. The experimental outcome shows that the suggested framework achieved an accuracy of 99.98% for the plant village dataset which is comparatively higher than the existing few-shot learning approach, Convolutional Block Attention Module and Efficient Channel Attention (CBAM-ECA) and Loss Fused Convolutional Neural Network (LFCNN) with accuracies of 93.45%, 99.74% and 98.93% respectively. [ABSTRACT FROM AUTHOR]

Disease management in grapevine has become one of the basic issues farmers face. Diseases can greatly affect the performance and life of this plant. With increasing environmental pressures and climate changes, the need for new and smart methods of managing diseases and pests has become inevitable. This research aims to diagnose and classify grape plant leaf diseases named Black Rot, Black Measles, and Leaf Blight. After removing the background from the images of the leaves and extracting the features of texture, color, and shape from the images, a combination of support vector machine classifier and butterfly optimization algorithm was used to select the most important features in the diagnosis of grape plant leaf disease. The precision results for black rot, Black Measles, and Leaf Blight diseases and healthy leaves were 100, 100, 100, and 95%, respectively, and the classification accuracy for diagnosing the diseases and healthy group was 98.75%. Also, 15 texture, color, and shape characteristics were introduced to plant pathology and data science researchers with the help of a butterfly optimization feature selection algorithm. The classification results showed that image processing and machine learning are highly able to diagnose and classify plant diseases. [ABSTRACT FROM AUTHOR]

In agricultural production, soil transmitted diseases pose significant challenges, resulting in reduced crop productivity, increased production costs, and diminished yields. Environmental factors such as excessive moisture and nitrogen availability can give birth to and facilitate the spread of soil-borne diseases. Agricultural production can be significantly hindered by soilborne illnesses, particularly in the context of nursery operations. Even when employing conventional methods, they often provide challenges in terms of management. Furthermore, apart from viruses and plant parasitic nematodes, soil transmitted diseases can also be caused by fungal and bacterial pathogens, including oomycete and plasmodiophorid. Major soilborne fungal infections include the species Rhizoctonia, Sclerotinia, Fusarium, and V erticillium. Oomycete pathogens, namely Phytophthora and Pythium, play a vital role. Resilient continuation structures such as chlamydospores, melanized hyphae, sclerotia, and oospores enable many soil-borne fungus to retain soil for extended durations. Ralstonia, Pectobacterium, Agrobacterium, and Streptomyces are important soil-dwelling bacterial pathogens. Soil-borne diseases often endure for an extended period on the biological remains of the host plant, either as organic matter within the soil or as autonomous organisms. Different soil parameters, such as soil texture, type, moisture, pH, temperature, ecology, and nutrient levels, influence the activity of soil-borne diseases. This review article provides a concise overview of the primary soil borne pathogens, diseases, management strategies, and approaches to soil borne diseases in large-scale crop production. This includes the system-based application of asepsis practices, legal techniques, anaerobic soil disinfestation, resistant cultivars and varieties, grafting, soil solarization, bio fumigants, soil amendments, soil steam sterilization, plant nutrients, soil fertility, soilless culture, and biological and chemical control. [ABSTRACT FROM AUTHOR]

Over the past few decades, the use of different biocontrol agents in agricultural methods has resulted in significant improvements in the safety and nutritional quality of food products. Consequently, there has been an increasing interest in finding effective alternative approaches to reduce abiotic stress pressures that also promote plant growth. Trichoderma harzianum is a biocontrol agent that has attracted scientific attention due to its remarkable capacity to combat various abiotic influences. The multifaceted mechanisms of disease prevention and crop growth acceleration exhibited by the filamentous fungus T. harzianum have rendered it a highly useful biocontrol agent. Trichoderma spp. positively influence several physiological cellular processes in plants, such as photosynthesis, stomatal conductance, gas exchange, nutrient absorption and assimilation, and water expenditure efficiency. Trichoderma species promoted optimal root development and improved the absorption of mineral nutrients from the soil. In summary, the fungus Trichoderma shows significant potential as a biocontrol agent for the sustainable protection of crops and the stimulation of plant growth. The varied mechanisms of Trichoderma species make them indispensable for the management of plant diseases. The utilization of Trichoderma potential is a significant avenue for achieving robust and ecologically sound crop production, particularly in challenging circumstances, as the worldwide agricultural sector strives for substitutes to harmful pesticides and excessive use of fertilizers. [ABSTRACT FROM AUTHOR]

Diseases and pests in plants/crops are major causes of significant agricultural losses with economic, social and ecological impacts. Therefore, there is a need for early identification of plant diseases and pests through automated systems. Recently, machine learning-based methods have become popular in solving agricultural problems such as plant diseases faced by technically-noob farmers. This work proposes a novel method based on stacking ensemble machine learning to detect plant diseases in Uradbean precisely. Two classifiers: support vector machine (SVM), random forest (RF) are trained on a dataset consists of Uradbean infected and healthy leaf images. These classifiers are stacked with logistic regression (LR) classifier. In the diverse ensemble, LR classifier is used as a meta-learner which enhanced the precision of the disease classification. The fuzzy C-Means clustering with particle swarm optimization is used for image segmentation. Haralick, Hu Moments and color histogram methods are used in feature extraction. During the tests, the proposed model is also compared with pretrained networks: DenseNet-201, ResNet-50, and VGG19. It achieved an impressive classification accuracy of 96.82 % which is higher than the individual classifiers and pre-trained networks. To validate model performance, it is evaluated on a benchmark public dataset consists of A pple leaf images and achieved 98.30% accuracy. It is observed that ensemble method reflects an advantage over individual models in increasing the classification rates and reducing the computational overhead in comparison to pre-trained networks which struggle due to the issues such as irrelevant features, generation of pertinent characteristics, and noise. [ABSTRACT FROM AUTHOR]

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Cucumber wilt, caused by Fusarium oxysporum f. sp. cucumerinum (FOC), is a soilborne disease that poses a significant threat to cucumber production, resulting in substantial yield losses. This study aimed to evaluate the biocontrol and growth-promoting effects of Bacillus velezensis, a highly active bacterial strain. In vitro assays revealed that B. velezensis F9 exhibited broad-spectrum antifungal activity against eight plant pathogenic fungi, with inhibition ratio ranging from 62.66% to 88.18%. Additionally, the strain displayed the ability to produce IAA (5.97 ± 1.75 µg/mL), fix nitrogen, produce siderophores, and form biofilms. In vitro growth promotion assays demonstrated that different concentrations of B. velezensis F9 significantly promoted cucumber seedling growth. Furthermore, two pot experiments revealed that the strain exhibited biocontrol efficacy against cucumber wilt, with disease control rates ranging from 42.86% to 67.78%. Notably, the strain significantly increased the plant height, fresh weight, and dry weight, with increases ranging from 20.67% to 60.04%, 40.27% to 75.51%, and 22.07% to 52.54%, respectively. Two field trials confirmed the efficacy of B. velezensis F9 in controlling cucumber wilt, with disease control rates of 44.95% and 33.99%, respectively. The strain effectively alleviated the dwarfing and wilting symptoms caused by the pathogen. Compared with the FOC treatment, the F9 + FOC treatment significantly increased the plant height, fresh weight, and dry weight, with increases of 43.85% and 56.28%, 49.49% and 23.70%, and 36.25% and 73.63%, respectively. Enzyme activity assays indicated that inoculation significantly increased SOD activity in cucumber leaves and neutral phosphatase, sucrase, and urease activity in rhizosphere soil. Correlation analysis revealed a negative correlation between the disease index and plant height, fresh weight, dry weight, and peroxidase activity, with correlation coefficients of −0.53, −0.60, −0.38, and −0.45, respectively. These findings suggest that plant height, fresh weight, and dry weight are significantly negatively correlated with the cucumber disease index, highlighting their importance as indicators for evaluating the biocontrol efficacy of B. velezensis F9. In conclusion, B. velezensis F9 is a highly effective plant growth-promoting rhizobacterium with excellent biocontrol potential, showcasing promising applications in agricultural production. [ABSTRACT FROM AUTHOR]

Anacardium occidentale is the tree source of cashew nuts, a commodity with increased demand globally and threatened by several diseases. Here we report on the occurrence of a disease that has severely affected production of cashew nuts in Panama and that can be of regional importance. Our first observation of this disease was in 2013, in the locality of Gamboa (Colon, Panama). Blight symptoms of the disease starts with foliar lesions characterized by initial necrotic points surrounded by yellow discoloration or chlorotic zones. These lesions progressively expand, transforming into larger, brown patches with rounded to irregular shapes. Lesions can coalesce and cover entire leaves causing them to drop and produce defoliation of entire branches or trees that become unproductive. Trees with similar symptoms were later observed in several provinces in Panama indicating that the disease was widespread in the country. We obtained consistent isolation of the same fungus from lesions on A. occidentale from multiple localities and pathogenicity tests were conducted on detached leaves as well as on healthy leaves on an adult tree. Morphological and phylogenetic analyses of the isolated fungus showed that the causal agent was Calonectria variabilis, a fungus previously reported to occur on Schefflera morototoni, Theobroma grandiflora, and Eucalyptus. This is the first time that C. variabilis is reported as a pathogen of A. occidentale and the first record of this species for Panama. [ABSTRACT FROM AUTHOR]

As an essential component of the global economic crop, cotton is highly susceptible to the impact of diseases on its yield and quality. In recent years, artificial intelligence technology has been widely used in cotton crop disease recognition, but in complex backgrounds, existing technologies have certain limitations in accuracy and efficiency. To overcome these challenges, this study proposes an innovative cotton disease recognition method called CANnet, and we independently collected and constructed an image dataset containing multiple cotton diseases. Firstly, we introduced the innovatively designed Reception Field Space Channel (RFSC) module to replace traditional convolution kernels. This module combines dynamic receptive field features with traditional convolutional features to effectively utilize spatial channel attention, helping CANnet capture local and global features of images more comprehensively, thereby enhancing the expressive power of features. At the same time, the module also solves the problem of parameter sharing. To further optimize feature extraction and reduce the impact of spatial channel attention redundancy in the RFSC module, we connected a self-designed Precise Coordinate Attention (PCA) module after the RFSC module to achieve redundancy reduction. In the design of the classifier, CANnet abandoned the commonly used MLP in traditional models and instead adopted improved Kolmogorov Arnold Networks-s (KANs) for classification operations. KANs technology helps CANnet to more finely utilize extracted features for classification tasks through learnable activation functions. This is the first application of the KAN concept in crop disease recognition and has achieved excellent results. To comprehensively evaluate the performance of CANnet, we conducted extensive experiments on our cotton disease dataset and a publicly available cotton disease dataset. Numerous experimental results have shown that CANnet outperforms other advanced methods in the accuracy of cotton disease identification. Specifically, on the self-built dataset, the accuracy reached 96.3%; On the public dataset, the accuracy reached 98.6%. These results fully demonstrate the excellent performance of CANnet in cotton disease identification tasks. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) is a crucial step in building knowledge graphs for crop diseases and pests. To enhance NER accuracy, we propose a new NER model—GatedMan—based on the gated fusion unit and Manhattan attention. GatedMan utilizes RoBERTa as a pre-trained model and enhances it using bidirectional long short-term memory (BiLSTM) to extract features from the context. It uses a gated unit to perform weighted fusion between the outputs of RoBERTa and BiLSTM, thereby enriching the information flow. The fused output is then fed into a novel Manhattan attention mechanism to capture the long-range dependencies. The global optimum tagging sequence is obtained using the conditional random fields layer. To enhance the model's robustness, we incorporate adversarial training using the fast gradient method. This introduces adversarial examples, allowing the model to learn more disturbance-resistant feature representations, thereby improving its performance against unknown inputs. GatedMan achieved F1 scores of 93.73%, 94.13%, 93.98%, and 96.52% on the AgCNER, Peoples_daily, MSRA, and Resume datasets, respectively, thereby outperforming the other models. Experimental results demonstrate that GatedMan accurately identifies entities related to crop diseases and pests and exhibits high generalizability in other domains. [ABSTRACT FROM AUTHOR]

Fungal effectors play critical roles in manipulating plant immune responses and promoting colonization. Sphaerulina musiva is a heterothallic ascomycete fungus that causes Septoria leaf spot and stem canker disease in poplar (Populus spp.) plantations. This disease can result in premature defoliation, branch and stem breakage, increased mortality, and plantation failure. However, little is known about the interaction between S. musiva and poplar. Previous work predicted 142 candidate secreted effector proteins in S. musiva (SmCSEPs), 19 of which were selected for further functional characterization in this study. SmCSEP3 induced plant cell death in Nicotiana benthamiana, while 8 out of 19 tested SmCSEPs suppressed cell death. The signal peptides of these eight SmCSEPs exhibited secretory activity in a yeast signal sequence trap assay. Confocal microscopy revealed that four of these eight SmCSEPs target both the cytoplasm and the nucleus, whereas four predominantly localize to discrete punctate structures. Pathogen challenge assays in N. benthamiana demonstrated that the transient expression of six SmCSEPs promoted Fusarium proliferatum infection. The expression of these six SmCSEP genes were induced during infection. SmCSEP2, SmCSEP13, and SmCSEP25 suppressed chitin-triggered reactive oxygen species burst and callose deposition in N. benthamiana. The candidate secreted effector proteins of S. musiva target multiple compartments in the plant cell and modulate different pattern-triggered immunity pathways. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2023.

The objective of this systematic review (SR) was to select studies on the use of gene editing by CRISPR technology related to plant resistance to biotic stresses. We sought to evaluate articles deposited in six electronic databases, using pre-defined inclusion and exclusion criteria. This SR demonstrates that countries such as China and the United States of America stand out in studies with CRISPR/Cas. Among the most studied crops are rice, tomatoes and the model plant Arabidopsis thaliana. The most cited biotic agents include the genera, Xanthomonas, Manaporthe, Pseudomonas and Phytophthora. This SR also identifies several CRISPR/Cas-edited genes and demonstrates that plant responses to stressors are mediated by many complex signaling pathways. The Cas9 enzyme is used in most articles and Cas12 and 13 are used as additional editing tools. Furthermore, the quality of the articles included in this SR was validated by a risk of bias analysis. The information collected in this SR helps to understand the state of the art of CRISPR/Cas aimed at improving resistance to diseases and pests to understand the mechanisms involved in most host–pathogen relationships. This SR shows that the CRISPR/Cas system provides a straightforward method for rapid gene targeting, providing useful information for plant breeding programs.

Among the vegetables, tomato is one of the economically important vegetables even at the global level. The nutritional and health benefits of tomato are so numerous to the extent that it has gained so much value in Africa due to its low-cost cultivation and contribution to livelihoods. However, Fusarium oxysporum lycopersici (FOL) and drought have been identified as potential threats to tomato production in Nigeria. To manage these challenges, one of the approaches is to identify Nigerian tomato cultivars that can exhibit resistance to FOL and drought. This study therefore investigates four Nigeria tomato cultivars; NGB00714, NGB00715, NGB00725 and NGB00740 to ascertain their resistance attributes to FOL and drought. Prior to their evaluation, Petri - dish seed germination bioassay was used in-vitro to assess the germination potentials of the tomato seeds. NGB00725 had the best germination, followed by NGB00714 and NGB00740 while the least was NGB00715. Using the Petri - dish in-vitro blotter technique, each tomato seed cultivar was separately exposed to FOL spores and FOL metabolites under drought stress induced by polyethyleneglycol (PEG-6000). Cultivar NGB00725, followed by NGB00740 exhibited resistance to FOL under PEG-induced drought stress based on their seed germination potentials. Bioprospecting resistant Nigeria tomato cultivars in this study is an indication of sustainable production of tomatoes in Nigeria against FOL under drought stress.