Plant Diseases Research Articles

Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS).

Rice is a critical staple crop that feeds more than half of the world's population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by Xanthomonas oryzae. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of X. oryzae under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of X. oryzae and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that X. oryzae's potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter's mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.

Microbiome-mediated plant disease resistance: recent advances and future directions

Microbiome-mediated plant disease resistance: recent advances and future directions

New Method for Tomato Disease Detection Based on Image Segmentation and Cycle-GAN Enhancement

A major concern in data-driven deep learning (DL) is how to maximize the capability of a model for limited datasets. The lack of high-performance datasets limits intelligent agriculture development. Recent studies have shown that image enhancement techniques can alleviate the limitations of datasets on model performance. Existing image enhancement algorithms mainly perform in the same category and generate highly correlated samples. Directly using authentic images to expand the dataset, the environmental noise in the image will seriously affect the model’s accuracy. Hence, this paper designs an automatic leaf segmentation algorithm (AISG) based on the EISeg segmentation method, separating the leaf information with disease spot characteristics from the background noise in the picture. This algorithm enhances the network model’s ability to extract disease features. In addition, the Cycle-GAN network is used for minor sample data enhancement to realize cross-category image transformation. Then, MobileNet was trained by transfer learning on an enhanced dataset. The experimental results reveal that the proposed method achieves a classification accuracy of 98.61% for the ten types of tomato diseases, surpassing the performance of other existing methods. Our method is beneficial in solving the problems of low accuracy and insufficient training data in tomato disease detection. This method can also provide a reference for the detection of other types of plant diseases.

Fostering ayurvedic plant wellness: Innovative leaf disease detection using computer vision and machine learning

Ayurveda is a conventional medicinal approach that has its roots in India and has been used for hundreds of years. It is still popular today since it is entirely natural and free of side effects. Although ayurvedic medicines are made from natural botanical substances, their safety depends on the way they are administered, taking into account the needs of the individual and the specific disease states they are treating. Diseases that affect the plants' leaves are regarded to be one of the main reasons for a decline in the output of ayurvedic plants in India's agricultural, economic, cosmetic, and pharmaceutical sectors. A plant exhibits signs of plant diseases in diverse sections of the plant, however, leaves are the most frequently observed component for spotting an infection. The objectives of this work are to modernize and innovate an autonomous ayurvedic plant leaf disease detection system by examining the scientific basis of computer vision. Additionally, a full analysis of computer vision techniques such as image pre-processing, segmentation, and feature extraction is covered to detect defects in plant leaves. This work investigates cutting-edge machine learning methods for identifying plant diseases that employ a variety of computer vision techniques. Based on the review, the article addresses the challenges and offers recommendations for changes that could be made in the future.

Fostering ayurvedic plant wellness: Innovative leaf disease detection using computer vision and machine learning

Ayurveda is a conventional medicinal approach that has its roots in India and has been used for hundreds of years. It is still popular today since it is entirely natural and free of side effects. Although ayurvedic medicines are made from natural botanical substances, their safety depends on the way they are administered, taking into account the needs of the individual and the specific disease states they are treating. Diseases that affect the plants' leaves are regarded to be one of the main reasons for a decline in the output of ayurvedic plants in India's agricultural, economic, cosmetic, and pharmaceutical sectors. A plant exhibits signs of plant diseases in diverse sections of the plant, however, leaves are the most frequently observed component for spotting an infection. The objectives of this work are to modernize and innovate an autonomous ayurvedic plant leaf disease detection system by examining the scientific basis of computer vision. Additionally, a full analysis of computer vision techniques such as image pre-processing, segmentation, and feature extraction is covered to detect defects in plant leaves. This work investigates cutting-edge machine learning methods for identifying plant diseases that employ a variety of computer vision techniques. Based on the review, the article addresses the challenges and offers recommendations for changes that could be made in the future.

Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet’s lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.

Effectiveness and Genetic Control of Trichoderma spp. as a Biological Control of Wheat Powdery Mildew Disease.

Wheat powdery mildew (WPM) is one of the most devasting diseases that affects wheat yield worldwide. Few efforts have been made to control such a serious disease. An effective way to control WPM is urgently needed. Biological control is an effective way to control plant diseases worldwide. In this study, the efficiency of three different Trichoderma spp. in controlling WPM at the seedling growth stage was tested using 35 highly diverse wheat genotypes. Highly significant differences were found in WPM resistance among the four treatments, confirming the efficiency of Trichoderma in controlling WPM. Of the three species, T. asperellum T34 (T34) was the most effective species in controlling WPM, as it reduced the symptoms by 50.56%. A set of 196 wheat genotypes was used to identify the genetic control of the WPM resistance induced by T34. A total of 39, 27, and 18 gene models were identified to contain the significant markers under Pm, T34, and the improvement in powdery mildew resistance due to T34 (T34_improvement) conditions. Furthermore, no gene model was common between T34 and Pm, suggesting the presence of completely different genetic systems controlling the resistance under T34 and Pm. The functional annotation and biological process pathways of the detected gene models confirm their association with the normal and induced resistance. This study, for the first time, confirms the efficiency of T34 in controlling WPM and provides a deep understanding of the genetic control of induced and normal resistance to WPM.

Improving losses & accuracy through design of deep convolutional generative adversarial network (DCGAN) for plant disease detection tasks

Improving losses & accuracy through design of deep convolutional generative adversarial network (DCGAN) for plant disease detection tasks

Natural SNP Variation in GbOSM1 Promotor Enhances Verticillium Wilt Resistance in Cotton.

Osmotin is classified as the pathogenesis-related protein 5 group. However, its molecular mechanism involved in plant disease resistance remains largely unknown. Here, a Verticillium wilt (VW) resistance-related osmotin gene is identified in Gossypium barbadense (Gb), GbOSM1. GbOSM1 is preferentially expressed in the roots of disease-resistant G. barbadense acc. Hai7124 and highly induced by Verticillium dahliae (Vd). Silencing GbOSM1 reduces the VW resistance of Hai7124, while overexpression of GbOSM1 in disease-susceptible G. hirsutum improves tolerance. GbOSM1 predominantly localizes in tonoplasts, while it relocates to the apoplast upon exposure to osmotic stress or Vd infection. GbOSM1 confers VW resistance by hydrolyzing cell wall polysaccharides of Vd and activating plant immune pathways. Natural variation contributes to a differential CCAAT/CCGAT elements in the OSM1 promoter in cotton accessions. All G. hirsutum (Gh) exhibit the CCAAT haplotype, while there are two haplotypes of CCAAT/CCGAT in G. barbadense, with higher expression and stronger VW resistance in CCGAT haplotype. A NFYA5 transcription factor binds to the CCAAT element of GhOSM1 promoter and inhibits its transcription. Silencing GhNFYA5 results in higher GhOSM1 expression and enhances VW resistance. These results broaden the insights into the functional mechanisms of osmotin and provide an effective strategy to breed VW-resistant cotton.

Dispersal of microbes from grassland fire smoke to soils.

Wildland fire is increasingly recognized as a driver of bioaerosol emissions, but the effects that smoke-emitted microbes have on the diversity and community assembly patterns of the habitats where they are deposited remain unknown. In this study, we examined whether microbes aerosolized by biomass burning smoke detectably impact the composition and function of soil sinks using lab-based mesocosm experiments. Soils either containing the native microbial community or presterilized by γ-irradiation were inundated with various doses of smoke from native tallgrass prairie grasses. Smoke-inundated, γ-irradiated soils exhibited significantly higher respiration rates than both smoke-inundated, native soils and γ-irradiated soils exposed to ambient air only. Microbial communities in γ-irradiated soils were significantly different between smoke-treated and control soils, which supports the hypothesis that wildland fire smoke can act as a dispersal agent. Community compositions differed based on smoke dose, incubation time, and soil type. Concentrations of phosphate and microbial biomass carbon and nitrogen together with pH were significant predictors of community composition. Source tracking analysis attributed smoke as contributing nearly 30% of the taxa found in smoke-inundated, γ-irradiated soils, suggesting smoke may play a role in the recovery of microbial communities in similar damaged soils. Our findings demonstrate that short-distance microbial dispersal by biomass burning smoke can influence the assembly processes of microbial communities in soils and has implications for a broad range of fields including agriculture, restoration, plant disease, and biodiversity.

Effects of Silicon on Development and Controlling of Powdery Scab in Irish Potato (Solanum tuberosum)

Effects of different silicon levels on plant height, stem diameter, disease incidence and severity and its activity on polyphenol oxidase and peroxidase enzymes on powdery scab caused by Spongospora subterranean on Irish potatoes (BP1) was assessed. This was a greenhouse study at the University of Zimbabwe Crop Science department. A mixture of 50% sterilised vermiculite and 50% sterilised sand was used as growing media. 0, 250, 500 and 750ppm silicon concentrations were used. Irrigation water, other fertilizers and insecticides applied were made uniform across all treatments. Variations among treatments were monitored from three weeks after planting then on weekly basis for the coming eighty weeks. Silicon concentrations showed significance difference (p<0.001) on plant height and stem diameter of Irish potatoes. It proved to play a significant role in the growth of the potato plants. Silicon levels showed no significant difference on powdery scab incidence with all plants getting diseased. Silicon reduced the infestation severity at high concentrations. It also increased the activity of polyphenol oxidase and peroxidase enzymes in potatoes at different response rates. Results from the study indicates great potential of silicon based fertilizers in controlling powdery scab as well as growth rate of Irish potatoes.

Assessing the Opportunities and Risks of DUS and VCU Variety Testing for Sustainable Production through SWOT Analysis Results

Within the European Union (EU), new plant varieties to be included in the Common catalog of a member state have to be registered on the national list after plant variety testing processes to establish whether the candidate variety is distinguishable, uniform, and stable (DUS) and meets the cultivation or use value requirement (VCU). Technical development, climate change, and changing consumer needs, including the detection of GMOs, necessitate the innovation of plant variety testing methods. In our study, we assessed new characters, testing methods, and inclusion of additional data for the potential to benefit the DUS and VCU protocols. To achieve our goal, we asked experts to fill in questionnaires for the DUS and VCU methods currently used for a selection of common crops, including potato, maize, lentil, oilseed rape, and perennial grass. Within the EU-funded “InnoVar” project, partners sent out questionnaires to 19 European Countries and to 3 countries outside Europe. Surveys were aimed at analyzing the strengths, weaknesses, opportunities, and threats (SWOT) of the current methods. With their help, it is possible to look for a new direction, opportunity, and strategy to incorporate, together with the innovative new techniques, into the development of the new methods. Our study demonstrated that the SWOT analysis could be used to achieve the set goals. Results obtained after evaluation of surveys confirmed that introduction of new characters such as cold tolerance, nitrogen and water efficiency, etc. has become necessary, as has the inclusion of new test methods (molecular markers, precision techniques, organic farming). The development of high-yielding, disease and/or pest-resistant plant varieties with good adaptability and the accurate evaluation of genotypes play a crucial role in ensuring that farmers can access high-performing plant varieties and contribute to sustainable food production.

Assessing the Pathogenicity of Berkeleyomyces rouxiae and Fusarium oxysporum f. sp. vasinfectum on Cotton (Gossypium hirsutum) Using a Rapid and Robust Seedling Screening Method.

Cotton (Gossypium spp.) is the most important fibre crop worldwide. Black root rot and Fusarium wilt are two major diseases of cotton caused by soil-borne Berkeleyomyces rouxiae and Fusarium oxysporum f. sp. vasinfectum (Fov), respectively. Phenotyping plant symptoms caused by soil-borne pathogens has always been a challenge. To increase the uniformity of infection, we adapted a seedling screening method that directly uses liquid cultures to inoculate the plant roots and the soil. Four isolates, each of B. rouxiae and Fov, were collected from cotton fields in Australia and were characterised for virulence on cotton under controlled plant growth conditions. While the identities of all four B. rouxiae isolates were confirmed by multilocus sequencing, only two of them were found to be pathogenic on cotton, suggesting variability in the ability of isolates of this species to cause disease. The four Fov isolates were phylogenetically clustered together with the other Australian Fov isolates and displayed both external and internal symptoms characteristic of Fusarium wilt on cotton plants. Furthermore, the isolates appeared to induce varied levels of plant disease severity indicating differences in their virulence on cotton. To contrast the virulence of the Fov isolates, four putatively non-pathogenic Fusarium oxysporum (Fo) isolates collected from cotton seedlings exhibiting atypical wilt symptoms were assessed for their ability to colonise cotton host. Despite the absence of Secreted in Xylem genes (SIX6, SIX11, SIX13 and SIX14) characteristic of Fov, all four Fo isolates retained the ability to colonise cotton and induce wilt symptoms. This suggests that slightly virulent strains of Fo may contribute to the overall occurrence of Fusarium wilt in cotton fields. Findings from this study will allow better distinction to be made between plant pathogens and endophytes and allow fungal effectors underpinning pathogenicity to be explored.

Image segmentation for pest detection of crop leaves by improvement of regional convolutional neural network

Pest detection is important for crop cultivation. Crop leaf is the main place of pest invasion. Current technologies to detect crop pests have constraints, such as low efficiency, storage demands and limited precision. Image segmentation is a fast and efficient computer-aided detection technology. High resolution image capture solidly supports the crucial processes in discerning pests from images. Study of analytical methods help parse information in the images. In this paper, a regional convolutional neural network (R-CNN) architecture is designed in combination with the radial bisymmetric divergence (RBD) method for enhancing the efficiency of image segmentation. As a special application of RBD, the hierarchical mask (HM) is produced to endorse detection and classification of the leaf-dwelling pests, offering enhanced efficiency and reduced storage requirements. Moreover, to deal with some mislabeled data, a threshold variable is introduced to adjust a fault-tolerant mechanism into HM, to generate a novel threshold-based hierarchical mask (TbHM). Consequently, the hierarchical mask R-CNN (HM-R-CNN) model and the threshold-based hierarchical mask R-CNN (TbHM-R-CNN) model are established to detect various types of healthy and pest-invasive crop leaves to select the regional image features that are rich in pest information. Then simple linear iterative clustering (SLIC) method is incorporation to finish the image segmentation for the classification of pest invasion. The models are tuned and optimized, then validated. The most optimized modeling results are from the TbHM-R-CNN model, with the classification accuracy of 96.2%, the recall of 97.5% and the F1 score of 0.982. Additionally, the HM-R-CNN model observed appreciable results second only to the best model. These results indicate that the proposed methodologies are well-suited for training and testing a dataset of plant diseases, offering heightened accuracy in pest classification. This study revealed that the proposed methods significantly outperform the existing techniques, marking a substantial improvement over current methods.

Assessment of Trichoderma Species Isolated From Volcanic Soil of a Durian Field in Sisaket Province, Thailand for Plant Growth Promotion and Biocontrol Potential

Trichoderma species are ubiquitous saprophytic fungi commonly found in soil, with potential as fungal biocontrol agents for plant disease control and growth promotion. This research aimed to assess the attributes of Trichoderma sp. as plant growth promoters, focusing on nitrogen fixation, siderophore production, plant nutrient solubilization and indole-3-acetic acid production. Out of 19 isolates tested, 6 (T05, T19, T25, T27, T28 and T33) showed superior plant growth promotion abilities. These isolates were further evaluated for their capacity to enhance the germination of Khao Dawk Mali 105 rice seeds. Seeds were soaked in a suspension of conidia at a concentration of 1×1012 conidia/mL, and germination rates were measured. The germination rate ranged between 77.67 - 86.67 %, statistically significantly higher than the control rate of 33.33 %. Among the isolates, T05 exhibited the highest promotion of plant growth, evidenced by root length (7.27 cm), shoot length (8.50 cm), fresh weight (0.54 g) and dry weight (0.27 g), all significantly different from the control. Additionally, the potential of isolates to inhibit the fungal pathogen Colletotrichum sp. MN-3 was assessed using a dual culture method, with T35 showing the highest inhibition percentage (60.90 %). Morphological classification and nucleotide sequencing of the ITS1-5.8S-ITS2 region of rDNA gene identified the isolates as T. harzianum, T. reesei, T. asperellum and T. longibrachiatum. These findings underscore the effectiveness of Trichoderma sp. in promoting plant growth and suppressing plant pathogenic fungi. HIGHLIGHTSSix Trichoderma isolates (T05, T19, T25, T27, T28 and T33) exhibited superior plant growth promotion abilities. These isolates significantly enhanced germination rates of Khao Dawk Mali 105 rice seeds compared to the method. Among the isolates, T05 showed the greatest promotion of plant growth, as evidenced by root length, shoot length, fresh weight and dry weight. Trichoderma isolates demonstrated potential for inhibiting the fungal pathogen Colletotrichum sp. MN-3. Morphological classification divided the Trichoderma sp. into 4 groups, and species identification through nucleotide sequencing revealed T. harzianum, T. reesei, T. asperellum and T. longibrachiatum. GRAPHICAL ABSTRACT

Pest and Disease Video Classification with Convolutional Neural Network and Transfer Learning

The important field crops of agriculture are affected due to attack of various pests and diseases which leads to reduction in crop production. Early classification and identification of pests and diseases in plant helps farmers to take mitigation steps. To address this issue with computer vision based techniques, convolutional neural network (CNN) based deep learning models were studied for classification of pests and diseases videos. Six different CNN models were developed. Two approaches namely from scratch learning and transfer learning were used. Data augmentation techniques such as reflection, scaling, rotation, and translation were also applied to prevent the network from overfitting. The classification accuracy of 99.19%, 99.08% and 98.80% was attained in VGG19, DENSENET201 and CNN 5 Layer model. The results demonstrated that CNN models with good architecture can classify pests and diseases with good performance.

Endophyte Bacillus vallismortis BL01 to Control Fungal and Bacterial Phytopathogens of Tomato (Solanum lycopersicum L.) Plants

Some strains of Bacillus vallismortis have been reported to be efficient biocontrol agents against tomato pathogens. The aim of our study was to assess the biocontrol ability of the endophytic strain BL01 Bacillus vallismortis through in vitro and field trials, as well as to verify its plant colonization ability and analyze the bacterial genome in order to find genes responsible for the biocontrol activity. We demonstrated in a gnotobiotic system and by confocal laser microscopy that the endophytic strain BL01 was able to colonize the endosphere and rhizosphere of tomato, winter wheat and oilseed rape. In vitro experiments demonstrated the inhibition activity of BL01 against a wide range of phytopathogenic fungi and bacteria. BL01 showed biological efficacy in two-year field experiments with tomato plants against black bacterial spotting by 40–70.8% and against late blight by 47.1% and increased tomato harvest by 24.9% or 10.9 tons per hectare compared to the control. Genome analysis revealed the presence of genes that are responsible for the synthesis of biologically active secondary metabolites, which could be responsible for the biocontrol action. Strain BL01 B. vallismortis can be considered an effective biocontrol agent to control both fungal and bacterial diseases in tomato plants.

3'UTR of tobacco vein mottling virus regulates downstream GFP expression and changes in host gene expression.

Tobacco vein mottling virus (TVMV) is a member of the family Potyviridae. The 3' untranslated region (3'UTR) of viral genomic RNA has been reported to significantly impact viral infection. Nevertheless, the role of the TVMV 3'UTR during viral infection remains unknown. Here, a 3'UTR-GFP expression vector was transiently expressed in Nicotiana benthamiana, in which the 3'UTR of TVMV was introduced upstream of the green fluorescent protein (GFP) gene. Transcriptome sequencing was performed to analyze the genes associated with plant resistance. The effect of the TVMV 3'UTR on GFP expression was studied using an Agrobacterium-mediated transient expression assay, revealing that the TVMV 3'UTR significantly inhibited GFP expression. Transcriptome analysis of differentially expressed genes in 3'UTR-GFP in N. benthamiana was performed to elucidate the why the TVMV 3'UTR inhibited GFP expression. Eighty genes related to plant disease resistance were differentially expressed, including 29 upregulated and 51 downregulated genes. Significantly upregulated genes included those encoding the calcium-binding protein CML24, leucine-rich repeat receptor-like tyrosine-protein kinase, and respiratory burst oxidase homolog protein E. The significantly downregulated genes included calcium-binding protein 7, ethylene-responsive transcription factor 10, endoglucanase 5, and receptor-like protein kinase. These findings indicate that the 3'UTR of TVMV may inhibit the expression of GFP gene by inducing the expression of plant resistance genes. This study provides a theoretical basis for further research on the function and mechanism of the TVMV 3'UTR.

An efficient plant disease prediction model using darner drain fly optimization-based deep convolutional neural network

An efficient plant disease prediction model using darner drain fly optimization-based deep convolutional neural network

Potato Leaf Disease Detection Based on a Lightweight Deep Learning Model

Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions. The management of potato diseases is critical to the agricultural industry, as these diseases can lead to substantial losses in crop production. The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. The proposed method not only enhances the precision of potato leaf disease detection but also reduces the computational and storage demands, with a mere 0.40 GFLOPs and a model size of 16.8 MB. This makes it well-suited for use on edge devices with limited resources, enabling real-time disease detection in agricultural environments. The experimental results demonstrated that the accuracy of the proposed method in identifying seven potato leaf diseases was 90.68%, providing a comprehensive solution for potato crop management.