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This paper showcases the development of plant virology in Croatia at the University of Zagreb, Faculty of Science, from its beginning in the 1950s until today, more than 70 years later. The main achievements of the previous and current group members are highlighted according to various research topics and fields. Expectedly, some of those accomplishments remained within the field of plant virology, but others make part of a much-extended research spectrum exploring subviral pathogens, prokaryotic plant pathogens, fungi and their viruses, as well as their interactions within ecosystems. Thus, the legacy of plant virology in Croatia continues to contribute to the state of the art of microbiology far beyond virology. Research problems pertinent for directing the future research endeavors are also proposed in this review.

Featured Application: This system will help the farmer to detect plant any disease at very early stage to take timely pre-emptive action. In this paper, we proposed a hybrid deep learning approach for detecting and classifying tomato plant leaf diseases early. This hybrid system is a combination of a convolutional neural network (CNN), convolutional attention module (CBAM), and support vector machines (SVM). Initially, the proposed model can detect nine different tomato diseases but is not limited to this. The proposed system is tested using a database containing images of tomato leaves. The obtained results were very encouraging, giving us accuracy up to 97.2%, which can be improved with the improvement of learning processes. The proposed system is very efficient and lightweight, so the farmer can install it on any smart device having a digital camera and processing capabilities. With a bit of training, a farmer can detect any disease immediately, which will help him take timely pre-emptive action. [ABSTRACT FROM AUTHOR]

Plant diseases such as drought stress and pest diseases significantly impact crops' growth and yield levels. By detecting the surface characteristics of plant leaves, we can judge the growth state of plants and whether diseases occur. Traditional manual detection methods are limited by the professional knowledge and practical experience of operators. In recent years, a detection method based on deep learning has been applied to improve detection accuracy and reduce detection time. In this paper, we propose a disease detection method using a convolutional neural network (CNN) with multi-scale feature fusion for maize leaf disease detection. Based on the one-stage plant disease network YoLov5s, the coordinate attention (CA) attention module is added, along with a key feature weight to enhance the effective information of the feature map, and the spatial pyramid pooling (SSP) module is modified by data augmentation to reduce the loss of feature information. Three experiments are conducted under complex conditions such as overlapping occlusion, sparse distribution of detection targets, and similar textures and backgrounds of disease areas. The experimental results show that the average accuracy of the MFF-CNN is higher than that of currently used methods such as YoLov5s, Faster RCNN, CenterNet, and DETR, and the detection time is also reduced. The proposed method provides a feasible solution not only for the diagnosis of maize leaf diseases, but also for the detection of other plant diseases. [ABSTRACT FROM AUTHOR]

Around the world, agriculture is one of the important sectors of human life in terms of food, business, and employment opportunities. In the farming field, wheat is the most farmed crop but every year, its ultimate production is badly influenced by various diseases. On the other hand, early and precise recognition of wheat plant diseases can decrease damage, resulting in a greater yield. Researchers have used conventional and Machine Learning (ML)-based techniques for crop disease recognition and classification. However, these techniques are inaccurate and time-consuming due to the unavailability of quality data, inefficient preprocessing techniques, and the existing selection criteria of an efficient model. Therefore, a smart and intelligent system is needed which can accurately identify crop diseases. In this paper, we proposed an efficient ML-based framework for various kinds of wheat disease recognition and classification to automatically identify the brown- and yellow-rusted diseases in wheat crops. Our method consists of multiple steps. Firstly, the dataset is collected from different fields in Pakistan with consideration of the illumination and orientation parameters of the capturing device. Secondly, to accurately preprocess the data, specific segmentation and resizing methods are used to make differences between healthy and affected areas. In the end, ML models are trained on the preprocessed data. Furthermore, for comparative analysis of models, various performance metrics including overall accuracy, precision, recall, and F1-score are calculated. As a result, it has been observed that the proposed framework has achieved 99.8% highest accuracy over the existing ML techniques. [ABSTRACT FROM AUTHOR]

Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semantic segmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neural network. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance. [ABSTRACT FROM AUTHOR]

Arbuscularmycorrhizal fungi (AMF) are a class of beneficial microorganisms that are widely distributed in soil ecosystems and can form symbionts with 80% of terrestrial higher plants, and improve the nutritional status of plants. The use of AMF as a biocontrol method to antagonize soil-borne pathogens has received increasing interest from phytopathologists and ecologists. In this paper, the mechanisms of resistance to diseases induced by AMF and the application of AMF to plant fungal, bacterial, and nematode diseases have been summarized. This study aimed to enhance the potential use of AMF as a biological control method to prevent plant diseases in the future. Root morphological alteration characteristics were explained, including the influence of AMF on root structure, function, and the regulation of AMF via secondary metabolites. AMF can improve the rhizosphere environment by influencing the physical and chemical proprieties of soil, enhancing the growth of other beneficial microorganisms, and by competing with pathogenic microorganisms. Two microorganism types may compete for the same invasive sites in root systems and regulate nutrition distribution. AMF can induce the host plant to form defense systems, including improving phytohormone concentrations, inducing signal substrate production, gene expression regulation, and enhancing protein production. [ABSTRACT FROM AUTHOR]

Plants' diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99 % for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96 % . For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy ( 96 % ). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets. [ABSTRACT FROM AUTHOR]

Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases. [ABSTRACT FROM AUTHOR]

The tomato is one of the most consumed agri-food products in Lebanon. Several fungal pathogens, including Alternaria species, can infect tomato plants during the whole growing cycle. Alternaria infections cause severe production and economic losses in field and during storage. In addition, Alternaria species represent a serious toxicological risk since they are able to produce a wide range of mycotoxins, associated with different toxic activities on human and animal health. Several Alternaria species were detected on tomatoes, among which the most important are A. solani , A. alternata , and A. arborescens . A set of 49 Alternaria strains isolated from leaves and stems of diseased tomato plants were characterised by using a polyphasic approach. All strains were included in the recently defined phylogenetic Alternaria section and grouped in three well-separated sub-clades, namely A. alternata (24 out of 49), A. arborescens (12 out of 49), and A. mali morpho-species (12 out of 49). One strain showed high genetic similarity with an A. limoniasperae reference strain. Chemical analyses showed that most of the Alternaria strains, cultured on rice, were able to produce alternariol (AOH), alternariol methyl ether (AME), altenuene (ALT) and tenuazonic acid (TA), with values up to 5634, 16,006, 5156, and 4507 mg kg -1 , respectively. In addition, 66% of the strains were able to co-produce simultaneously the four mycotoxins investigated. The pathogenicity test carried out on 10 Alternaria strains, representative of phylogenetic sub-clades, revealed that they were all pathogenic on tomato fruits. No significant difference among strains was observed, although A. alternata and A. arborescens morpho-species strains. This paper reports new insights on mycotoxin profiles, genetic variability, and pathogenicity of A. mali morpho-species strains. This paper reports new insights on mycotoxin profiles, genetic variability, and pathogenicity of Alternaria species on tomatoes.

The adoption of Oxford Nanopore Technologies (ONT) sequencing as a tool in plant virology has been relatively slow despite its promise in more recent years to yield large quantities of long nucleotide sequences in real time without the need for prior amplification. The portability of the MinION and Flongle platforms combined with lowering costs and continued improvements in read accuracy make ONT an attractive method for both low- and high-scale virus diagnostics. Here, we provide a detailed step-by-step protocol using the ONT Flongle platform that we have developed for the routine application on a range of symptomatic post-entry quarantine and domestic surveillance plant samples. The aim of this methods paper is to highlight ONT's feasibility as a valuable component to the diagnostician's toolkit and to hopefully stimulate other laboratories towards the eventual goal of integrating high-throughput sequencing technologies as validated plant virus diagnostic methods in their own right.

Using bacteriophages (bacterial viruses) to control pathogenic bacteria is a promising approach in horticulture. However, the application of this strategy in real conditions requires compliance with particular technological and environmental restraints. The presented paper concerns the process of phage selection to create a cocktail that is efficient against the circulating causal agents of potato soft rot. The resulting phage cocktail causes a complete lysis of a mixture of circulating pectobacterial strains in vitro. In the context of being used to treat ware potatoes during off-season storage, the protocol of phage application via the humidity maintenance system was designed. The phage cocktail was shown to reduce the population of Pectobacterium spp. 10-12-fold, achieving a population that was below a symptomatic threshold.

With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applications is essentially the fine-grained image classification task, which aims to learn effective discriminative features that can identify the subtle differences among similar visual samples. It is still challenging to solve for existing standard models troubled by oversized parameters and low accuracy performance. Therefore, in this paper, we propose a feature-enhanced attention neural network (Fe-Net) to handle the fine-grained image recognition of crop pests and diseases in innovative agronomy practices. This model is established based on an improved CSP-stage backbone network, which offers massive channel-shuffled features in various dimensions and sizes. Then, a spatial feature-enhanced attention module is added to exploit the spatial interrelationship between different semantic regions. Finally, the proposed Fe-Net employs a higher-order pooling module to mine more highly representative features by computing the square root of the covariance matrix of elements. The whole architecture is efficiently trained in an end-to-end way without additional manipulation. With comparative experiments on the CropDP-181 Dataset, the proposed Fe-Net achieves Top-1 Accuracy up to 85.29% with an average recognition time of only 71 ms, outperforming other existing methods. More experimental evidence demonstrates that our approach obtains a balance between the model's performance and parameters, which is suitable for its practical deployment in precision agriculture art applications. [ABSTRACT FROM AUTHOR]

Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales. This review provides an overview of traditional diagnostic methods followed by a comprehensive description of optical sensing technology, including camera systems, platforms, and spectral data analysis to detect plant viral diseases. The paper is organized along six multidisciplinary sections: (1) Impact of plant viral disease on plant physiology and consequent phenotypic changes, (2) direct diagnostic methods, (3) traditional indirect detection methods, (4) optical sensing technologies, (5) data processing techniques and modelling for disease detection, and (6) comparison of the costs. Finally, the current challenges and novel ideas of optical sensing for detecting plant viruses are discussed. [ABSTRACT FROM AUTHOR]

Each year, unwanted plant pests and diseases, such as Hendel or potato soft rot, cause damage to crops and ecosystems all over the world. To continue to feed the growing population and protect the global ecosystems, the surveillance and management of the spread of these pests and diseases are crucial. Traditional methods of detection are often expensive, bulky and require expertise and training. Therefore, inexpensive, portable, and user-friendly methods are required. These include the use of different gas-sensing technologies to exploit volatile organic compounds released by plants under stress. These methods often meet these requirements, although they come with their own set of advantages and disadvantages, including the sheer number of variables that affect the profile of volatile organic compounds released, such as sensitivity to environmental factors and availability of soil nutrients or water, and sensor drift. Furthermore, most of these methods lack research on their use under field conditions. More research is needed to overcome these disadvantages and further understand the feasibility of the use of these methods under field conditions. This paper focuses on applications of different gas-sensing technologies from over the past decade to detect plant pests and diseases more efficiently. [ABSTRACT FROM AUTHOR]

The soil-borne species Aneurinibacillus migulanus (A. migulanus) strains Nagano and NCTC 7096 were shown to be potent biocontrol agents active against several plant diseases in agricultural and forest ecosystems. Both strains produce the cyclic peptide gramicidin S (GS) that was described as the main weapon inhibiting some gram-negative and gram-positive bacteria and fungus-like organisms along with the production of biosurfactant and hemolysis activities. However, the contribution of the cyclic peptide gramicidin S (GS) to the biocontrol ability of A. migulanus has never been studied experimentally. In this paper, using a mutant of the A. migulanus Nagano strain (E1 mutant) impaired in GS biosynthesis we evaluated the contribution of GS in the biocontrol potential of A. migulanus against Phytophthora spp. The two strains of A. migulanus, Nagano and NCTC 7096, were tested in a pilot study for the inhibition of the growth of 13 Phytophthora species in dual culture assays. A. migulanus Nagano was significantly more inhibitory than NCTC 7096 to all species. Additionally, using apple infection assays, P. rosacearum MKDF-148 and P. cryptogea E2 were shown to be the most aggressive on apple fruits displaying clear infection halos. Therefore, the three A. migulanus strains, Nagano, NCTC 7096, and E1, were used in apple infection experiments to check their effect on infection ability of these two Phytophthora species. Treatment with A. migulanus Nagano significantly reduced the severity of symptoms in apple fruits compared with NCTC 7096. A. migulanus E1 mutant showed total loss of biocontrol ability suggesting that GS is a major actor in the biocontrol ability of A. migulanus Nagano strain. [ABSTRACT FROM AUTHOR]

The prevention and management of crop diseases play an important role in agricultural production, but there are many types of crop diseases and complex causes, and their prevention and identification add difficulties to the process. The traditional methods of identifying diseases mostly rely on human visual and manual inspection, which requires a certain amount of expert knowledge and experience. There are shortcomings such as strong subjectivity and low accuracy. This paper takes the common diseases of kiwifruit as the research object. Based on deep learning and computer vision models, and given the influence of a complex background in actual scenes on the detection of diseases, as well as the shape and size characteristics of diseases, an innovative method of target detection and semantic segmentation was proposed to identify diseases accurately. The main contributions of this research are as follows: We produced the world's first high-quality dataset on kiwifruit. We used the target detection algorithm YOLOX, we stripped the kiwi leaves from the natural background and removed the influencing factors existing in the complex background. Based on the mainstream semantic segmentation networks UNet and DeepLabv3+, the experimental results showed that the ResNet101 network achieved the most effective results in the identification of kiwi diseases, with an accuracy rate of 96.6%. We used the training method of learning rate decay to further improve the training effect without increasing the training cost. After experimental verification, our two-stage disease detection algorithm had the advantages of high accuracy, strong robustness, and wide detection range, which provided a more efficient solution for solving the problem of precise monitoring of crop growth environment parameters. [ABSTRACT FROM AUTHOR]

Ginseng is an important medicinal plant in Korea. The roots of the ginseng plant have medicinal properties; thus, it is very important to maintain the quality of ginseng roots. Root rot disease is a major disease that affects the quality of ginseng roots. It is important to predict this disease before it causes severe damage to the plants. Hence, there is a need for a non-destructive method to identify root rot disease in ginseng plants. In this paper, a method to identify the root rot disease by analyzing the RGB plant images using image processing and deep learning is proposed. Initially, plant segmentation is performed, and then the noise regions are removed in the plant images. These images are given as input to the proposed linear deep learning model to identify root rot disease in ginseng plants. Transfer learning models are also applied to these images. The performance of the proposed method is promising in identifying root rot disease. [ABSTRACT FROM AUTHOR]

Phytopathogenic cercosporoid fungi have been investigated comprehensively due to their important role in causing plant diseases. A significant amount of research has been focused on the biology, morphology, systematics, and taxonomy of this group, with less of a focus on molecular or biochemical issues. Early and extensive research on these fungi focused on taxonomy and their classification based on in vivo features. Lately, investigations have mainly addressed a combination of characteristics such as morphological traits, host specificity, and molecular analyses initiated at the end of the 20th century. Some species that are important from an economic point of view have been more intensively investigated by means of genetic and biochemical methods to better understand the pathogenesis processes. Cercosporin, a photoactivated toxin playing an important role in Cercospora diseases, has been extensively studied. Understanding cercosporin toxicity in relation to reactive oxygen species (ROS) production facilitated the discovery and regulation of the cercosporin biosynthesis pathway, including the gene cluster encoding pathway enzymes. Furthermore, these fungi may be a source of other biotechnologically important compounds, e.g., industrially relevant enzymes. This paper reviews methods and important results of investigations of this group of fungi addressed at different levels over the years.

Russian wheat aphid, Diuraphis noxia (Kurdjumov), is a severe pest of wheat, Triticum aestivum L., throughout the world. Resistant cultivars are viewed as the most economical and environmentally viable control available. Studies to identify molecular markers to facilitate resistance breeding started in the 1990s, and still continue. This paper reviews and discusses the literature pertaining to the D. noxia R-genes on chromosome 7D, and markers reported to be associated with them. Individual plants with known phenotypes from a panel of South African wheat accessions are used as examples. Despite significant inputs from various research groups over many years, diagnostic markers for resistance to D. noxia remain elusive. Factors that may have impeded critical investigation, thus blurring the accumulation of a coherent body of information applicable to Dn resistance, are discussed. This review calls for a more fastidious approach to the interpretation of results, especially considering the growing evidence pointing to the complex regulation of aphid resistance response pathways in plants. Appropriate reflection on prior studies, together with emerging knowledge regarding the complexity and specificity of the D. noxia -wheat resistance interaction, should enable scientists to address the challenges of protecting wheat against this pest in future.

The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of mobile computing devices and the advancements in artificial intelligence have created opportunities for developing technologies to assist farmers in plant disease detection and treatment. To this end, deep learning has been widely used for disease detection in plants with highly favorable outcomes. In this paper, we propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices. As opposed to the publicly available datasets, images used in this study were collected in the field by considering important parameters of image-capturing devices such as angle, scale, orientation, and environmental conditions. Furthermore, extensive data augmentation was used to expand the dataset and make it more challenging to enable robust training. Investigations of recent architectures revealed that transfer learning of scale-sensitive models like Inception yield results much better with such challenging datasets with extensive data augmentation. Through parameter quantization, we optimized the Inception-v3 model for deployment on resource-constrained devices. The optimized model successfully classified healthy and diseased fruits and leaves with more than 92% accuracy on mobile devices.

Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.

This paper proposes to study the potentialities of on-board colour imaging for the in-field detection of a textbook case disease: the grapevine downy mildew. It introduces an algorithmic strategy for the detection of various forms of foliar symptoms on proximal high-resolution images. The proposed strategy is based on structure-colour representations and probabilistic models of grapevine tissues. It operates in three steps: (i) Formulating descriptors to extract the characteristic and discriminating properties of each class. They combine the Local Structure Tensors (LST) with colorimetric statistics calculated in pixel's neighbourhood. (ii) Modelling the statistical distributions of these descriptors in each class. To account for the specific nature of LSTs, the descriptors are mapped in the Log-Euclidean space. In this space, the classes of interest can be modelled with mixtures of multivariate Gaussian distributions. (iii) Assigning each pixel to one of the classes according to its suitability to their models. The decision method is based on a "seed growth segmentation" process. This step exploits statistical criteria derived from the probabilistic model. The resulting processing chain reliably detects downy mildew symptoms and estimates the area of the affected tissues. A leave-one-out cross-validation is conducted on a dataset constituted of a hundred independent images of grapevines affected only by downy mildew and/or abiotic stresses. The proposed method achieves an extensive and accurate recovery of foliar symptoms, with on average, a 83% pixel-wise precision and a 76% pixel-wise recall.

Food contamination with mycotoxins is a worldwide concern, because these toxins produced by several fungal species have detrimental effects on animal and/or human health. In maize, fumonisins are among the toxins with the highest threatening potential because they are mainly produced by Fusarium verticillioides , which is distributed worldwide. Plant breeding has emerged as an effective and environmentally safe method to reduce fumonisin levels in maize kernels, but although phenotypic selection has proved effective for improving resistance to fumonisin contamination, further resources should be mobilized to meet farmers' needs. Selection based on molecular markers linked to quantitative trait loci (QTL) for resistance to fumonisin contamination or/and genotype values obtained using prediction models with markers distributed across the whole genome could speed up breeding progress. Therefore, in the current paper, previously identified genomic regions, genes, and/or pathways implicated in resistance to fumonisin accumulation will be reviewed. Studies done until now have provide many markers to be used by breeders, but to get further insight on plant mechanisms to defend against fungal infection and to limit fumonisin contamination, the genes behind those QTLs should be identified.

Fungi and oomycetes encompass many pathogens affecting crops worldwide. Their effective control requires screening pathogens across the local and international trade networks along with the monitoring of pathogen inocula in the field. Fundamentals to all of these concerns are their efficient detection, identification, and quantification. The use of molecular markers showed the best promise in the field of plant pathogen diagnostics. However, despite the unquestionable benefits of DNA-based methods, two significant limitations are associated with their use. The first limitation concerns the insufficient level of sensitivity due to the very low and uneven distribution of pathogens in plant material. The second limitation pertains to the inability of widely used diagnostic assays to detect cryptic species. Targeting mtDNA appears to provide a solution to these challenges. Its high copy number in microbial cells makes mtDNA an attractive target for developing highly sensitive assays. In addition, previous studies on different pathogen taxa indicated that mitogenome sequence variation could improve cryptic species delimitation accuracy. This review sheds light on the potential application of mtDNA for pathogen diagnostics. This paper covers a brief description of qPCR and DNA barcoding as two major strategies enabling the diagnostics of plant pathogenic fungi and oomycetes. Both strategies are discussed along with the potential use of mtDNA, including their strengths and weaknesses.

Bacterial spot is a serious disease of tomato caused by at least four species of Xanthomonas . These include X. euvesicatoria (race T1), X. vesicatoria (race T2), X. perforans (races T3 and T4), and X. gardneri, with the distinct geographical distribution of each group. Currently, X. gardneri and X. perforans are two major bacterial pathogens of tomato in North America, with X. perforans (race T4) dominating in east-coast while X. gardneri dominating in the Midwest. The disease causes up to 66% yield loss. Management of this disease is challenging due to the lack of useful chemical control measures and commercial resistant cultivars. Although major genes for resistance ( R ) and quantitative resistance have been identified, breeding tomato for resistance to bacterial spot has been impeded by multiple factors including the emergence of new races of the pathogen that overcome the resistance, multigenic control of the resistance, linkage drag, non-additive components of the resistance and a low correlation between seedling assays and field resistance. Transgenic tomato with Bs2 and EFR genes was effective against multiple races of Xanthomonas . However, it has not been commercialized because of public concerns and complex regulatory processes. The genomics-assisted breeding, effectors-based genomics breeding, and genome editing technology could be novel approaches to achieve durable resistance to bacterial spot in tomato. The main goal of this paper is to understand the current status of bacterial spot of tomato including its distribution and pathogen diversity, challenges in disease management, disease resistance sources, resistance genetics and breeding, and future prospectives with novel breeding approaches.

Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobilebased system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species. [ABSTRACT FROM AUTHOR]

In the realm of smart agriculture technology's rapid advancement, the integration of various sensors and Internet of Things (IoT) devices has become prevalent in the agricultural sector. Within this context, the precise identification of pests and diseases using unmanned robotic systems assumes a crucial role in ensuring food security, advancing agricultural production, and maintaining food reserves. Nevertheless, existing recognition models encounter inherent limitations such as suboptimal accuracy and excessive computational efforts when dealing with similar pests and diseases in real agricultural scenarios. Consequently, this research introduces the lightweight cross-layer aggregation neural network (LCA-Net). To address the intricate challenge of fine-grained pest identification in agricultural environments, our approach initially enhances the high-performance large-scale network through lightweight adaptation, concurrently incorporating a channel space attention mechanism. This enhancement culminates in the development of a cross-layer feature aggregation (CFA) module, meticulously engineered for seamless mobile deployment while upholding performance integrity. Furthermore, we devised the Cut-Max module, which optimizes the accuracy of crop pest and disease recognition via maximum response region pruning. Thorough experimentation on comprehensive pests and disease datasets substantiated the exceptional fine-grained performance of LCA-Net, achieving an impressive accuracy rate of 83.8%. Additional ablation experiments validated the proposed approach, showcasing a harmonious balance between performance and model parameters, rendering it suitable for practical applications in smart agricultural supervision. [ABSTRACT FROM AUTHOR]

Accurate, rapid and non-destructive disease identification in the early stage of infection is essential to ensure the safe and efficient production of greenhouse cucumbers. Nevertheless, the effectiveness of most existing methods relies on the disease already exhibiting obvious symptoms in the middle to late stages of infection. Therefore, this paper presents an early identification method for cucumber diseases based on the techniques of hyperspectral imaging and machine learning, which consists of two procedures. First, reconstruction fidelity terms and graph constraints are constructed based on the decision criterion of the collaborative representation classifier and the desired spatial distribution of spectral curves (391 to 1044 nm) respectively. The former constrains the same-class and different-class reconstruction residuals while the latter constrains the weighted distances between spectral curves. They are further fused to steer the design of an offline algorithm. The algorithm aims to train a linear discriminative projection to transform the original spectral curves into a low dimensional space, where the projected spectral curves of different diseases own better separation trends. Then, the collaborative representation classifier is utilized to achieve online early diagnosis. Five experiments were performed on the hyperspectral data collected in the early infection stage of cucumber anthracnose and Corynespora cassiicola diseases. Experimental results demonstrated that the proposed method was feasible and effective, providing a maximal identification accuracy of 98.2% and an average online identification time of 0.65 ms. The proposed method has a promising future in practical production due to its high diagnostic accuracy and short diagnosis time.

The plant disease recognition model based on deep learning has shown good performance potential. However, high complexity and nonlinearity lead to the low transparency and poor interpretability of such models. These limitations greatly limit the deployment and application of such models in field scenarios. To solve the above problems, we propose a dense caption generative model, Veg DenseCap. This model takes vegetable leaf images as input and uses object detection technology to locate abnormal parts of the leaf and identify the disease results. More importantly, it can describe the disease features it sees in natural language, and users can judge whether the relevant features are semantically consistent with human cognition based on these descriptions. First of all, a dataset containing Chinese feature description statements for images of 10 leaf diseases involving two vegetables (cucumber and tomato) was established. Secondly, Faster R-CNN was used as a disease detector to extract visual features of diseases, and LSTM was used as a language generator to generate description statements for disease features. Finally, the Convolutional Block Attention Module (CBAM) and the Focal Loss function were employed to overcome the imbalance between positive and negative samples and the weak performance of Faster R-CNN in obtaining key features. According to the test results, the Intersection-over-Union (IoU) and Meteor joint evaluation index of Veg-DenseCap achieved a mean Average Precision (mAP) of 88.0% on the dense captioning dataset of vegetable leaf disease images, which is 9.1% higher than that of the classical FCLN model. The automatically generated description statements are characterized by advantages of accurate feature description, correct grammar, and high diversity. [ABSTRACT FROM AUTHOR]

Cnidium officinale Makino, a perennial crop in the Umbeliperae family, is one of Korea's representative forest medicinal plants. However, the growing area of C. officinale has been reduced by plant disease and soil sickness caused by fusarium wilt. This study isolated rhizosphere bacteria from C. officinale, and their antagonistic activity was evaluated against Fusarium solani. Particularly, four isolated strains, namely, PT1, ST7, ST8, and SP4, showed a significant antagonistic activity against F. solani. An in planta test showed that the mortality rates of shoots were significantly low in the PT1-inoculated group. The fresh and dry weights of the inoculated plants were also higher than that of the other groups. The 16S rRNA gene sequencing identified the strain PT1 as Leclercia adecarboxylata, and downstream studies confirmed the production of antagonism-related enzymes such as siderophore and N-acetyl-β-glucosaminidase. The phosphorous solubilizing ability and secretion of related enzymes were also analyzed. The results showed that PT1 strain could be utilized as promising plant growth-promoting rhizobacteria (PGPR) and biocontrol agents (BCAs). [ABSTRACT FROM AUTHOR]

In this paper we have characterized the lineage of two traits associated with the coat proteins (CPs) of the tombusvirids: Silencing suppression and HR elicitation in Nicotiana species. We considered that the tombusvirid CPs might collectively be considered an effector, with the CP of each CP-encoding species comprising a structural variant within the family. Thus, a phylogenetic analysis of the CP could provide insight into the evolution of a pathogen effector. The phylogeny of the CP of tombusvirids indicated that CP representatives of the family could be divided into four clades. In two separate clades the CP triggered a hypersensitive response (HR) in Nicotiana species of section Alatae but did not have silencing suppressor activity. In a third clade the CP had a silencing suppressor activity but did not have the capacity to trigger HR in Nicotiana species. In the fourth clade, the CP did not carry either function. Our analysis illustrates how structural changes that likely occurred in the CP effector of progenitors of the current genera led to either silencing suppressor activity, HR elicitation in select Nicotiana species, or neither trait.

Tea diseases have a significant impact on the yield and quality of tea during the growth of tea trees. The shape and scale of tea diseases are variable, and the tea disease targets are usually small, with the intelligent detection processes of tea diseases also easily disturbed by the complex background of the growing region. In addition, some tea diseases are concentrated in the entire area of the leaves, needing to be inferred from global information. Common target detection models are difficult to solve these problems. Therefore, we proposed an improved tea disease detection model called TSBA-YOLO. We use the dataset of tea diseases collected at the Maoshan Tea Factory in China. The self-attention mechanism was used to enhance the ability of the model to obtain global information on tea diseases. The BiFPN feature fusion network and adaptively spatial feature fusion (ASFF) technology were used to improve the multiscale feature fusion of tea diseases and enhance the ability of the model to resist complex background interference. We integrated the Shuffle Attention mechanism to solve the problem of difficult identifications of small-target tea diseases. In addition, we used data-enhancement methods and transfer learning to expand the dataset and relocate the parameters learned from other plant disease datasets to enhance tea diseases detection. Finally, SIoU was used to further improve the accuracy of the regression. The experimental results show that the proposed model is good at solving a series of problems encountered in the intelligent recognition of tea diseases. The detection accuracy is ahead of the mainstream target detection models, and the detection speed reaches the real-time level. [ABSTRACT FROM AUTHOR]

Agriculture is a cornerstone of economic prosperity, but plant diseases can severely impact crop yield and quality. Identifying these diseases accurately is often difficult due to limited expert availability and ambiguous information. Early detection and automated diagnosis systems are crucial to mitigate these challenges. To address this, we propose a lightweight convolutional neural network (CNN) designed for resource-constrained devices termed as LeafNet. LeafNet draws inspiration from the block-wise VGG19 architecture but incorporates several optimizations, including a reduced number of parameters, smaller input size, and faster inference time while maintaining competitive accuracy. The proposed LeafNet leverages small, uniform convolutional filters to capture fine-grained details of plant disease features, with an increasing number of channels to enhance feature extraction. Additionally, it integrates channel attention mechanisms to prioritize disease-related features effectively. We evaluated the proposed method on four datasets: the benchmark plant village (PV), the data repository of leaf images (DRLIs), the newly curated plant composite (PC) dataset, and the BARI Sunflower (BARI-Sun) dataset, which includes diverse and challenging real-world images. The results show that the proposed performs comparably to state-of-the-art methods in terms of accuracy, false positive rate (FPR), model size, and runtime, highlighting its potential for real-world applications. [ABSTRACT FROM AUTHOR]

Plant pathogenic bacteria are responsible for a substantial number of plant diseases worldwide, resulting in significant economic losses. Bacteria are exposed to numerous stress factors during their epiphytic life and within the host. Their ability to survive in the host and cause symptomatic infections depends on their capacity to overcome stressors. Bacteria have evolved a range of defensive and adaptive mechanisms to thrive under varying environmental conditions. One such mechanism involves the induction of chaperone proteins that belong to the heat shock protein (Hsp) family. Together with proteases, these proteins are integral components of the protein quality control system (PQCS), which is essential for maintaining cellular proteostasis. However, knowledge of their action is considerably less extensive than that of human and animal pathogens. This study discusses the modulation of Hsp levels by phytopathogenic bacteria in response to stress conditions, including elevated temperature, oxidative stress, changes in pH or osmolarity of the environment, and variable host conditions during infection. All these factors influence bacterial virulence. Finally, the secretion of GroEL and DnaK proteins outside the bacterial cell is considered a potentially important virulence trait. [ABSTRACT FROM AUTHOR]

Crop diseases significantly threaten agricultural productivity, leading to unstable food supply and economic losses. The current approaches to automated crop disease recognition face challenges such as limited datasets, restricted coverage of disease types, and inefficient feature extraction, which hinder their generalization across diverse crops and disease patterns. To address these challenges, we propose an efficient data augmentation method to enhance the performance of deep learning models for crop disease recognition. By constructing a new large-scale dataset comprising 24 different classes, including both fruit and leaf samples, we intend to handle a variety of disease patterns and improve model generalization capabilities. Geometric transformations and color space augmentation techniques are applied to validate the efficiency of deep learning models, specifically convolution and transformer models, in recognizing multiple crop diseases. The experimental results show that these augmentation techniques improve classification accuracy, achieving F1 scores exceeding 98%. Feature map analysis further confirms that the models effectively capture key disease characteristics. This study underscores the importance of data augmentation in developing automated, energy-efficient, and environmentally sustainable crop disease detection solutions, contributing to more sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns. This approach allows for a holistic analysis of spatially distributed symptoms, crucial for accurately diagnosing diseases in trees. Extensive experiments conducted on apple, grape, and tomato leaf disease datasets demonstrate the model's superior performance, achieving over 99% accuracy and significantly improving F1 scores compared to traditional methods such as ResNet, VGG, and MobileNet. These findings underscore the effectiveness of the proposed model for precise and reliable plant disease classification. [ABSTRACT FROM AUTHOR]

Simple Summary: Plant disease classification is a crucial research field due to its practical applications in agriculture. While many methods have been developed, significant progress has been made using convolutional neural networks. However, training these models usually requires large datasets, which are difficult to collect in the domain of plant diseases. This challenge is common across various fields, leading to the development of advanced methods, like few-shot learning. Few-shot learning enables image classification even when fewer than 10 examples are available for each category. Many of these methods rely on identifying similarities between images. Several loss functions used in few-shot learning, along with popular neural network architectures, were evaluated in this study to classify 68 classes of plant diseases. The dataset consisted of 4000 images collected in real-world conditions, making it ideal for testing these methods. The results highlight the most effective approaches for model organization and training, along with the preferred similarity learning techniques. Additionally, the reduced dataset has been published online, enabling other researchers to compare their methods and contribute to advancements in this field. Early detection of plant diseases is crucial for agro-holdings, farmers, and smallholders. Various neural network architectures and training methods have been employed to identify optimal solutions for plant disease classification. However, research applying one-shot or few-shot learning approaches, based on similarity determination, to the plantdisease classification domain remains limited. This study evaluates different loss functions used in similarity learning, including Contrastive, Triplet, Quadruplet, SphereFace, CosFace, and ArcFace, alongside various backbone networks, such as MobileNet, EfficientNet, ConvNeXt, and ResNeXt. Custom datasets of real-life images, comprising over 4000 samples across 68 classes of plant diseases, pests, and their effects, were utilized. The experiments evaluate standard transfer learning approaches alongside similarity learning methods based on two classes of loss function. Results demonstrate the superiority of cosine-based methods over Siamese networks in embedding extraction for disease classification. Effective approaches for model organization and training are determined. Additionally, the impact of data normalization is tested, and the generalization ability of the models is assessed using a special dataset consisting of 400 images of difficult-to-identify plant disease cases. [ABSTRACT FROM AUTHOR]

One of the main obstacles in rice cultivation is tungro disease, caused by Rice Tungro Spherical Virus (RTSV) and Rice Tungro Bacilliform Virus (RTBV), which are transmitted by green leafhopper vectors (Nephotettix virescens). This disease can be controlled by using pesticides and refugia plants. Excessive use of pesticides can have negative impacts and high costs, so it is necessary to control the use of pesticides. In this study, a mathematical model of the spread of tungro virus disease in rice plants was developed by considering the characteristics of the virus, the presence of green leafhoppers and natural enemies, refugia planting, and pesticide use. From this model, dynamic and sensitivity analyses were carried out, and the optimal control theory was searched using the Pontryagin minimum principle. The analysis results showed three equilibriums: two non-endemic equilibriums (when plant and vector populations exist and when plant, vector, and natural enemy populations exist) and one endemic equilibrium. The non-endemic equilibrium will be asymptotically stable locally if R 0 < 1 . At the same time, the parameters that greatly influence the spread of this disease are parameters μ ,   μ 2 , and ϕ for local sensitivity analysis and α ,   a ,   β ,   b ,   ϕ , and μ 2 for global sensitivity analysis. The results of the numerical simulation show that control using combined control is more effective in reducing the intensity of the spread of tungro disease in rice plants than control in the form of planting refugia plants as a source of food for natural enemies. The use of pesticides is sufficient for only four days, so the costs incurred are quite effective in controlling the spread of this disease. [ABSTRACT FROM AUTHOR]

Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVIRB) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVIRB was designed. GRVIRB demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVIRB has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales. [ABSTRACT FROM AUTHOR]

In the realm of global food security, plants serve as the primary source of sustenance. However, plant diseases pose a significant threat to this security. The process for diagnosing these diseases forms the bedrock of disease control efforts. The precision and expediency of these diagnoses wield substantial influence over disease management and the consequent reduction of economic losses. This research endeavors to diagnose the prevalent crops in Jordan, as identified by the Jordanian Department of Statistics for the year 2019. These crops encompass four key agricultural varieties: cucumbers, tomatoes, lettuce, and cabbage. To facilitate this, a novel dataset known as "Jordan22" was meticulously curated. Jordan22 was compiled by collecting images of diseased and healthy plants captured on Jordanian farms. These images underwent meticulous classification by a panel of three agricultural specialists well-versed in plant disease identification and prevention. The Jordan22 dataset comprises a substantial size, amounting to 3210 images. The results yielded by the CNN were remarkable, with a test accuracy rate reaching an impressive 0.9712. Optimal performance was observed when images were resized to 256 × 256 dimensions, and max pooling was used instead of average pooling. Furthermore, the initial convolutional layer was set at a size of 32, with subsequent convolutional layers standardized at 128 in size. In conclusion, this research represents a pivotal step towards enhancing plant disease diagnosis and, by extension, global food security. Through the creation of the Jordan22 dataset and the meticulous training of a CNN model, we have achieved substantial accuracy in disease detection, paving the way for more effective disease management strategies in agriculture. [ABSTRACT FROM AUTHOR]

Pythium spp. are soil-borne pathogens that cause damping-off and root rot diseases in many plant species such as cucumber. In the current study, the effect of dried roots–stems and leaves of Ageratina adenophora (Sprengel) R. King and H. Robinson (=Eupatorium adenophorum Sprengel) alone and in combination with pyrogenic biomass biochar to control Pythium aphanidermatum (Edson) Fitz was assessed. In four treatments of leaves, roots–stems, leaves + biochar, and roots–stems + biochar, it was observed that the treatment with leaves at an Emax (maximal effective concentration on control fungi) of 79 g/kg of soil had the most antifungal effect on P. aphanidermatum. Also, the Cmax (the highest level of control) increased with time and reached 82.4% and 71% on days 30 and 60, respectively. The highest cucumber fresh fruit weight and the highest height of the stems in the greenhouse were observed in leaf treatment of A. adenophora. Biochar did not have any remarkable controlling effect on P. aphanidermatum, and its population increased. The main compounds extracted from the dried leaves and roots–stems of A. adenophora, including α-pinene, nonanone, hexahydronaphthalene, 3-undecanone, muurolene, and heneicosane, had antifungal properties. We concluded that the leaves of A. adenophora have the potential to be used as a bio-fumigant for P. aphanidermatum management. [ABSTRACT FROM AUTHOR]

Septoria leaf spot (SLS), caused by the hemibiotrophic fungus Septoria lycopersici, poses a significant threat to tomato production systems. While fungicides can effectively reduce disease epidemics, planting resistant cultivars remains the most efficient and economical control method. Conventional screening and breeding have identified new sources of resistance to S. lycopersici among wild relatives. However, gaps exist in the literature concerning tomato—S. lycopersici interactions, such as plant defense mechanisms, fungal pathogenicity mechanisms, and interaction dynamics that can inform the development of durable resistance through additional research. To further enhance host resistance to S. lycopersici, molecular methods such as marker-assisted breeding, gene editing, genomic selection, and transgenic approaches can be employed. Moreover, the durability of resistance and efficacy of disease management can be optimized within an integrated pest management (IPM) framework that advances diverse tactics to suppress diseases and improve plant productivity. This review highlights the current understanding of pathogen biology, host genetic solutions, and novel strategies to combat the SLS problem and suggests directions for future research. [ABSTRACT FROM AUTHOR]

This review explores the use of machine learning (ML) techniques for detecting pests and diseases in crops, which is a significant challenge in agriculture, leading to substantial yield losses worldwide. This study focuses on the integration of ML models, particularly Convolutional Neural Networks (CNNs), which have shown promise in accurately identifying and classifying plant diseases from images. By analyzing studies published from 2019 to 2024, this work summarizes the common methodologies involving stages of data acquisition, preprocessing, segmentation, feature extraction, and prediction to develop robust ML models. The findings indicate that the incorporation of advanced image processing and ML algorithms significantly enhances disease detection capabilities, leading to the early and precise diagnosis of crop ailments. This can not only improve crop yield and quality but also reduce the dependency on chemical pesticides, contributing to more sustainable agricultural practices. Future research should focus on enhancing the robustness of these models to varying environmental conditions and expanding the datasets to include a wider variety of crops and diseases. CNN-based models, particularly specialized architectures like ResNet, are the most widely used in the studies reviewed, making up 42.36% of all models, with ResNet alone contributing 7.65%. This highlights ResNet's appeal for tasks that demand deep architectures and sophisticated feature extraction. Additionally, SVM models account for 9.41% of the models examined. The prominence of both ResNet and MobileNet reflects a trend toward architectures with residual connections for deeper networks, alongside efficiency-focused designs like MobileNet, which are well-suited for mobile and edge applications. [ABSTRACT FROM AUTHOR]

A pathogen strain responsible for sweet potato stem and foliage scab disease was isolated from sweet potato stems. Through a phylogenetic analysis based on the rDNA internal transcribed spacer (ITS) region, combined with morphological methods, the isolated strain was identified as Elsinoë batatas. To comprehensively analyze the pathogenicity of the isolated strain from a genetic perspective, the whole-genome sequencing of E. batatas HD-1 was performed using both the PacBio and Illumina platforms. The genome of E. batatas HD-1 is about 26.31 Mb long in 167 scaffolds, with a GC content of 50.81%, and 7898 protein-coding genes, 131 non-coding RNAs, and 1954 interspersed repetitive sequences were predicted. Functional annotation revealed that 408 genes encode virulence factors involved in plant disease (DFVF—Plant). Notably, twenty-eight of these virulence genes encode secretory carbohydrate-active enzymes (CAZymes), including two endo-1,4-β-xylanase genes and seven cutinase genes, which suggested that endo-1,4-β-xylanase and cutinase play a vital role in the pathogenicity of E. batatas HD-1 within sweet potato. In total, twelve effectors were identified, including five LysM effectors and two CDIP effectors, suggesting that LysM and CDIP effectors play significant roles in the interaction between E. batatas HD-1 and sweet potato. Additionally, our analysis of biosynthetic gene clusters (BGCs) showed that two gene clusters are involved in melanin and choline metabolism. This study enriches the genomic resources of E. batatas and provides a theoretical foundation for future investigations into the pathogenic mechanisms of its infection in sweet potatoes, as well as potential targets for disease control. [ABSTRACT FROM AUTHOR]

Closed environment agriculture (CEA) is rapidly gaining traction as a sustainable option to meet global food demands while mitigating the impacts of climate change. Fungal pathogens represent a significant threat to crop productivity in CEA, where the controlled conditions can inadvertently foster their growth. Historically, the detection of pathogens has largely relied on the manual observation of signs and symptoms of disease in the crops. These approaches are challenging at large scale, time consuming, and often too late to limit crop loss. The emergence of fungicide resistance further complicates management strategies, necessitating the development of more effective diagnostic tools. Recent advancements in technology, particularly in molecular and isothermal diagnostics, offer promising tools for the early detection and management of fungal pathogens. Innovative detection methods have the potential to provide real-time results and enhance pathogen management in CEA systems. This review explores isothermal amplification and other new technologies in detection of fungal pathogens that occur in CEA. [ABSTRACT FROM AUTHOR]

This study investigated the biosynthesis, statistical optimization, characterization, and biocontrol activity of silver nanoparticles (AgNPs) produced by newly isolated Trichoderma sp. The Trichoderma asperellum strain TA-3N was identified based on the ITS gene sequence, together with its phenotypic characteristics (GenBank accession number: OM321439). The color change from light yellow to brown after the incubation period indicates AgNPs biosynthesis. The UV spectrum revealed a single peak with the maximum absorption at 453 nm, indicating that T. asperellum produces AgNPs effectively. A Rotatable Central Composite Design (RCCD) was used to optimize the biosynthesis of AgNPs using the aqueous mycelial-free filtrate of T. asperellum. The optimal conditions for maximum AgNPs biosynthesis (156.02 µg/mL) were predicted theoretically using the desirability function tool and verified experimentally. The highest biosynthetic produced AgNPs by T. asperellum reached 160.3 µg/mL using AgNO3 concentration of 2 mM/mL, initial pH level of 6, incubation time of 60 h, and biomass weight of 6 g/100 mL water. SEM and TEM imaging revealed uniform spherical shape particles that varied in size between 8.17 and 17.74 nm. The synthesized AgNPs have a Zeta potential value of −9.51 mV. FTIR analysis provided insights into the surface composition of AgNPs, identifying various functional groups such as N–H, -OH, C-H, C=O, and the amide I bond in proteins. Cytotoxicity and genotoxicity assays demonstrated that AgNPs in combination with T. asperellum can mitigate the toxic effects of Fusarium oxysporum on barley. This intervention markedly enhanced cell division rates and decreased chromosomal irregularities. The results indicate that AgNPs synthesized by T. asperellum show the potential as an eco-friendly and efficient method for controlling plant diseases. Further studies are necessary to investigate their possible use in the agricultural sector. [ABSTRACT FROM AUTHOR]

Movements of plant pathogenic microorganisms in uncontaminated areas occur today at an alarming rate, driven mainly by global trade and climate change. These invaders can trigger new disease outbreaks able to impact the biodiversity and economies of vast territories and affect a variety of ecosystem services. National and supranational regulatory deficiencies, such as inadequate quarantine measures and ineffective early pathogen detection at ports of entry, exacerbate the issue. Thus, there is an urgent need for accurate and rapid diagnostic tools to intercept invasive and nonindigenous plant pathogens. The LAMP (Loop-mediated isothermal AMPlification) technique is a robust, flexible tool representing a significant advance in point-of-care (POC) diagnostics. Its user-friendliness and sensitivity offer a breakthrough in phytosanitary checks at points of entry (harbors and airports), for disease and pest surveillance at vulnerable sites (e.g., nurseries and wood-processing and storage facilities), and for territorial monitoring of new disease outbreaks. This review highlights the strengths and weaknesses of LAMP, emphasizing its potential to revolutionize modern plant disease diagnostics. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa is an aerobic, Gram-negative bacterium that is responsible for many plant diseases. The bacterium is the causal agent of Pierce's disease in grapes and is also responsible for citrus variegated chlorosis, peach phony disease, olive quick decline syndrome and leaf scorches of various species. The production of biofilm is intrinsically linked with persistence and transmission in X. fastidiosa. Biofilm formation is regulated by members of the Diffusible Signal Factor (DSF) quorum sensing signalling family which are comprised of a series of long chain cis-unsaturated fatty acids. This article describes the evaluation of a library of N-acyl sulfonamide bioisosteric analogues of BDSF, XfDSF1 and XfDSF2 for their ability to control biofilm production in X. fastidiosa. The compounds were screened against both the wild-type strain Temecula and an rpfF* mutant which can perceive but not produce XfDSF. Planktonic cell abundance was measured via OD600 while standard crystal violet assays were used to determine biofilm biomass. Several compounds were found to be effective biofilm inhibitors depending on the nature of the sulfonamide substituent. The findings reported here may provide future opportunities for biocontrol of this important plant pathogen. [ABSTRACT FROM AUTHOR]

Tomato leaf diseases pose a significant threat to plant growth and productivity, necessitating the accurate identification and timely management of these issues. Existing models for tomato leaf disease recognition can primarily be categorized into Convolutional Neural Networks (CNNs) and Visual Transformers (VTs). While CNNs excel in local feature extraction, they struggle with global feature recognition; conversely, VTs are advantageous for global feature extraction but are less effective at capturing local features. This discrepancy hampers the performance improvement of both model types in the task of tomato leaf disease identification. Currently, effective fusion models that combine CNNs and VTs are still relatively scarce. We developed an efficient CNNs and VTs fusion network named ECVNet for tomato leaf disease recognition. Specifically, we first designed a Channel Attention Residual module (CAR module) to focus on channel features and enhance the model's sensitivity to the importance of feature channels. Next, we created a Convolutional Attention Fusion module (CAF module) to effectively extract and integrate both local and global features, thereby improving the model's spatial feature extraction capabilities. We conducted extensive experiments using the Plant Village dataset and the AI Challenger 2018 dataset, with ECVNet achieving state-of-the-art recognition performance in both cases. Under the condition of 100 epochs, ECVNet achieved an accuracy of 98.88% on the Plant Village dataset and 86.04% on the AI Challenger 2018 dataset. The introduction of ECVNet provides an effective solution for the identification of plant leaf diseases. [ABSTRACT FROM AUTHOR]

Yellow rose (Rosa xanthina) is a common ornamental shrub species widely cultivated in China. However, canker disease symptoms were discovered during our investigations in Beijing and Xinjiang, China. The fungal isolates were obtained from diseased barks and identified using combined methods of morphology and phylogeny based on a partial region of ITS, LSU, rpb2, tef1, and tub2 sequences. As a result, a new species of Seimatosporium named S. chinense was proposed and described herein. The new species is distinguished from its phylogenetic sister species, S. gracile and S. nonappendiculatum, by conidial characters. The present study improves the species concept in Seimatosporium and provides fundamental data for the yellow rose canker disease control in the future. [ABSTRACT FROM AUTHOR]