Crop Diseases Research Articles

In Situ Diagnosis and Digital Cataloguing of Plant Pathogenic Fungi Through Mobile‐Based Foldscope Microscopy

ABSTRACTAgriculture confronts multifaceted challenges across the spectrum of crop production, with pest and disease management being a prominent concern. Timely diagnosis of crop diseases is imperative for mitigating production costs and curbing the adverse environmental impacts of chemical pesticides. In the present investigation, mobile phone‐based foldscope microscopy (MBFM) was used to diagnose various field samples infected with fungal diseases of field and horticultural crops, and the same was validated with the normal microscope pictures and field symptoms. The MBFM was also used to diagnose seed‐borne microflora associated with wheat and spores of commercial formulation of bioagents and validated. The MBFM utilises both symptoms and morphological structures of pathogen for in situ field diagnosis and hence advantages over the symptom‐based mobile Apps. This study underscores the utility of foldscope microscope as a potent technique for plant pathologists and extension workers to enable real‐time and in situ identification of diseases caused by fungal pathogens in various agricultural crops.

Genome-wide analysis of calmodulin binding Protein60 candidates in the important crop plants.

Efficient management of environmental stresses is essential for sustainable crop production. Calcium (Ca²⁺) signaling plays a crucial role in regulating responses to both biotic and abiotic stresses, particularly during host-pathogen interactions. In Arabidopsis thaliana, calmodulin-binding protein 60 (CBP60) family members, such as AtCBP60g, AtCBP60a, and AtSARD1, have been well characterized for their involvement in immune regulation. However, a comprehensive understanding of CBP60 genes in major crops remains limited. In this study, we utilized the Phytozome v12.1 database to identify and analyze CBP60 genes in agriculturally important crops. Expression patterns of a Oryza sativa (rice) CBP60 gene, OsCBP60bcd-1, were assessed in resistant and susceptible rice genotypes in response to infection by the bacterial pathogen Xanthomonas oryzae. Localization of CBP60 proteins was analyzed to predict their functional roles, and computational promoter analysis was performed to identify stress-responsive cis-regulatory elements. Phylogenetic analysis revealed that most CBP60 genes in crops belong to the immune-related clade. Expression analysis showed that OsCBP60bcd-1 was significantly upregulated in the resistant rice genotype upon pathogen infection. Subcellular localization studies suggested that the majority of CBP60 proteins are nuclear-localized, indicating a potential role as transcription factors. Promoter analysis identified diverse stress-responsive cis-regulatory elements in the promoters of CBP60 genes, highlighting their regulatory potential under stress conditions. The upregulation of OsCBP60bcd-1 in response to Xanthomonas oryzae and the presence of stress-responsive elements in its promoter underscore the importance of CBP60 genes in pathogen defense. These findings provide a basis for further investigation into the functional roles of CBP60 genes in crop disease resistance, with implications for enhancing stress resilience in agricultural species.

A Convolutional Neural Network Model for Crop Disease Detection System

Crop diseases pose a significant challenge to global food security, adversely impacting agricultural output and resulting in considerable economic repercussions. The prompt and precise identification of these diseases is essential for effective intervention and sustainable agricultural practices. This study introduces a model based on Convolutional Neural Networks (CNNs) for the automated detection of crop diseases. The model employs advanced deep learning methodologies to recognize and categorize plant diseases through the analysis of leaf images. Our CNN framework is trained on an extensive dataset comprising both diseased and healthy plant images, employing multiple convolutional layers to extract intricate features, including texture, color variations, and patterns linked to specific diseases. The model demonstrates a high level of accuracy in identifying a variety of diseases across different crop species by learning from both overt symptoms and subtle cues. We evaluate the performance of the system using established metrics such as accuracy and precision, thereby validating its efficacy in practical applications. The proposed system is designed for implementation in low-resource agricultural settings, offering farmers a cost-effective, dependable, and real-time solution for monitoring crop health.

Indigenous Technical Knowledge Practices for Managing Pests and Diseases in Agricultural Crops

Aims: This study aims to document indigenous technical knowledge (ITK) practices used by farmers in the Tirunelveli district for managing pests and diseases in agricultural crops. It focuses on understanding traditional methods and their relevance in sustainable pest and disease control. Study Design: This is a descriptive field study employing a mixed-method approach, combining a literature review and qualitative data collection from farmers. Place and Duration of Study: The study took place in Tirunelveli district of Tamil Nadu, India. The research was conducted over three months from January to March. Methodology: The research was conducted in three phases 1) Literature Review: Existing studies on indigenous pest and disease management techniques in the region were reviewed. 2) Data Collection: Interviews and focus group discussions were held with farmers to gather traditional knowledge on pest and disease control practices. 3) Data Analysis: The collected information was analysed for common practices and evaluated for effectiveness. Results: The study uncovered numerous indigenous techniques, including the use of neem leaves, cow urine, and plant-based remedies for pest control. Out of 150 farmers interviewed, 80% used traditional methods, with 60% reporting success in reducing crop damage. These practices were found to be cost-effective and eco-friendly, reducing dependence on chemical pesticides. Conclusion: The research highlights the potential of ITK in sustainable agriculture particularly for pest and disease management. It underscores the importance of preserving and promoting these traditional methods as viable alternatives to modern chemical practices.

Georgia’s potentials for sustainable intensification, increasing food security and rural incomes.

Abstract Increasing global demand for agricultural commodities spurs conversions of natural ecosystems. Sustainable intensification in areas of high yield gaps has been proposed a pathway to achieve food security, support rural livelihoods, while also reducing the impact of commodity production by narrowing yield gaps on existing agricultural lands, while improving resource efficiency. Following the dissolution of the Union of Soviet Socialist Republics (USSR), Georgia experienced one of the highest losses of agricultural productivity among all former USSR countries and is now highly dependent on food imports. Closing yield gaps in Georgia through sustainable intensification has the potential to increase food self-sufficiency, support rural livelihoods, and strengthen food security and sovereignty. We estimated its potential for sustainable intensification in current agricultural areas to achieve self-sufficiency for wheat, maize, and barley. We found that crop yields can be doubled to tripled under high-input production systems, using high-yielding varieties, optimized inputs, fertilizers, and pest control. Yet, self-sufficiency in wheat can only be reached if at least 80% of the potentially attainable yields are achieved and if land is strategically allocated between crops. To achieve such increases, farmers need access to and training for using different crop varieties, fertilizers, and pest and disease control practices and products. Intensification increases the risks to ecosystem services’ health and livelihoods, particularly raising equity concerns. Yet, intensifying very low input systems is often found to be more sustainable, with high yield increases compared to limited impacts on the environment. The high employment rate in the agricultural sector in Georgia particularly provides opportunities to reduce poverty and increase livelihoods through increasing incomes and food security.

Towards robust crop disease detection for complex real field background images

Most of the work done in image processing-based crop disease detection focuses on images with plain background. This paper presents a technique for crop disease detection for complex real field background images. A segmentation technique is presented to extract leaf patches from the entire image. Transform domain cepstral analysis is proposed for obtaining cepstral coefficients, to attain two level classifications. The first level classifies the crop species while the second level classifies the species into healthy leaf or leaf with specific type of disease. The work is tested on three crops Banana, Soybean and Grape and is checked on plain background laboratory images and on complex real field images. Suggested technique give species level accuracy of 94.33 %, 94.11 % and 98.44 % and disease level average accuracy of 97.75 %, 96.66 % and 97.95 % for Banana, Soybean and Grape, respectively. Comparison with standard features like texture and shape indicate that the presented technique gives the best results for both plain and complex background images suggesting its utilization in crop disease detection to reduce the agricultural and economic losses.

AgriScan: Next.js powered cross-platform solution for automated plant disease diagnosis and crop health management

Plant diseases present a formidable challenge to the agricultural sector worldwide, leading to significant losses, with the US experiencing annual losses amounting to one-third of crop production. Diagnosis of crop diseases through optical observation of leaf symptoms is particularly daunting for farmers with limited resources. Therefore, there is an urgent need for enhanced detection, monitoring, and prediction methods to mitigate agricultural losses effectively. Harnessing the power of computer vision and deep learning, this paper introduces a cross-platform system designed to automate plant leaf disease diagnosis. The system employs convolutional neural networks to classify 46 disease categories, trained on a dataset comprising 96,206 images of healthy and infected plant leaves. The user interface, accessible across multiple platforms including Android, iOS, Windows, and Linux, allows farmers to capture photos of infected leaves and receive real-time disease classification along with confidence percentages. By empowering farmers to maintain crop health and prevent the application of incorrect fertilizers, the system aims to optimize crop productivity. Performance evaluation includes metrics such as classification accuracy and processing time, with the model achieving an impressive overall accuracy of 93.45% across 46 common disease classes spanning 16 crop species.

Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection

Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection

Fludioxonil does not antagonize propiconazole activity against Geotrichum candidum.

Fludioxonil and propiconazole are frequently used fungicides for managing fungal diseases in specialty crops. They are often used in mixture to manage brown rot caused by Monilinia fructicola and sour rot caused by the yeast-like fungus Geotrichum candidum,respectively, on stone fruits. Research indicated, however, that fludioxonil can increase the expression of the ATP-binding cassette transporters CDR1 and CDR2 in the yeast Candida albicans, leading to increased resistance to DMI fungicides. In this study, mycelial growth assays and detached fruit assays were conducted with G. candidum isolates of varying sensitivity to propiconazole to investigate potential antagonism between the two active ingredients. Isolates sensitive, reduced-sensitive, and resistant to propiconazole were able to grow with or without 20 µg/ml fludioxonil on PDA containing up to 0, 3, and 30 μg/ml propiconazole and on water agar containing up to 7.5, 7.5, and 30 μg/ml propiconazole, respectively, and no antagonistic interactions were detected. Propiconazole controlled disease with or without 20 µg/ml fludioxonil equally on detached cherry fruit caused by isolates with different DMI sensitivity phenotypes. Our results indicated no detectable antagonism between fludioxonil and propiconazole that would interfere with the mixture's ability to manage sour rot of cherry.

Accurate crop disease identification and classification in smart agriculture using a three-tier model and optimized fully conventional network

Accurate crop disease identification and classification in smart agriculture using a three-tier model and optimized fully conventional network

Unlocking the potential of simulated hyperspectral imaging in agro environmental analysis: a comprehensive study of algorithmic approaches

This study focuses on identifying and evaluating the severity of powdery mildew disease in tomato plants. The uniqueness of this work lies in combining the imaging and advanced deep learning methods to develop a technique that transforms Red Green Blue (RGB) images into Simulated Hyperspectral Images (SHSI) to perform spectral and spatial analysis for precise detection and assessment of powdery mildew severity, thereby enhancing disease management. Furthermore, this research evaluates three advanced pre-trained VGG16 models, ResNet50 and EfficientNet-B7 algorithms for image preprocessing and feature extraction. Extracted features are passed to a neural network generator model to convert RGB image features into SHSIs, providing insights into the spectrum. This method enables the image analysis to perform assessments from SHSIs for health classification using Normalized Difference Vegetation Index (NDVI) values, which are meticulously compared with accurate hyperspectral data using metrics like mean absolute error (MAE) and root mean squared error (RMSE). This strategy enhances precision farming, environmental monitoring, and remote sensing accuracy. Results show that ResNet50’s architecture offers a robust framework for this study’s spectral and spatial analysis, making it a suitable choice over VGG16 and EfficientNet-B7 for transforming RGB images into SHSI. These simulated hyperspectral images offer a scalable and affordable approach for precise assessment of crop disease severity.

Dual RNA-seq reveals distinct families of co-regulated and structurally conserved effectors in Botrytis cinerea infection of Arabidopsis thaliana

BackgroundBotrytis cinerea is a broad-host-range pathogen causing gray mold disease and significant yield losses of numerous crops. However, the mechanisms underlying its rapid invasion and efficient killing of plant cells remain unclear.ResultsIn this study, we elucidated the dynamics of B. cinerea infection in Arabidopsis thaliana by live cell imaging and dual RNA sequencing. We found extensive transcriptional reprogramming events in both the pathogen and the host, which involved metabolic pathways, signaling cascades, and transcriptional regulation. For the pathogen, we identified 591 candidate effector proteins (CEPs) and comprehensively analyzed their co-expression, sequence similarity, and structural conservation. The results revealed temporal co-regulation patterns of these CEPs, indicating coordinated deployment of effectors during B. cinerea infection. Through functional screening of 48 selected CEPs in Nicotiana benthamiana, we identified 11 cell death-inducing proteins (CDIPs) in B. cinerea.ConclusionsThe findings provide important insights into the transcriptional dynamics and effector biology driving B. cinerea pathogenesis. The rapid infection of this pathogen involves the temporal co-regulation of CEPs and the prominent role of CDIPs in host cell death. This work highlights significant changes in gene expression associated with gray mold disease, underscoring the importance of a diverse repertoire of effectors crucial for successful infection.

Potato and Maize Plant Disease Detection Using Leaf Images

Plant diseases represent a serious threat to national productivity and global food security. Effective therapy for multiple diseases requires a precise and useful differentiation of them. In this work, a computerized system for the identification and categorization of diseases in potato and maize crops is developed using convolutional neural networks. The demonstration was created with the ResNet50V2 model and tested on a combined collection of images of leaves. The system achieved an astounding accuracy of 85.19. Enhancing model execution through exchange learning, fine-tuning, and information augmentation were all part of the process. With the use of another dataset, the trained model was verified and produced positive results, almost exactly differentiating between the disease-causing leaf type (potato or maize). This technology helps ranchers adopt sustainable and knowledgeable disease management methods by promoting timely mediations, which in turn advances disease discovery.

Design of agricultural question answering information extraction method based on improved BILSTM algorithm

With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python’s Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT (Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF (Conditional Random Field) and BILSTM algorithm, the result shows that the BE-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, which improves the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers’ needs and interests, the system can better recommend relevant and practical information.

Automated lesion detection in cotton leaf visuals using deep learning

Cotton is one of the major cash crop in the agriculture led economies across the world. Cotton leaf diseases affects its yield globally. Determining cotton lesions on leaves is difficult when the area is big and the size of lesions is varied. Automated cotton lesion detection is quite useful; however, it is challenging due to fewer disease class, limited size datasets, class imbalance problems, and need of comprehensive evaluation metrics. We propose a novel deep learning based method that augments the data using generative adversarial networks (GANs) to reduce the class imbalance issue and an ensemble-based method that combines the feature vector obtained from the three deep learning architectures including VGG16, Inception V3, and ResNet50. The proposed method offers a more precise, efficient and scalable method for automated detection of diseases of cotton crops. We have implemented the proposed method on publicly available dataset with seven disease and one health classes and have achieved highest accuracy of 95% and F-1 score of 98%. The proposed method performs better than existing state of the art methods.

A Summative Review of Advances in Sensor Technology for Precision Agriculture

Sensor technology has become a cornerstone of precision agriculture, offering a wide array of applications that enhance farming efficiency, productivity, and sustainability. By providing real-time data on soil health, crop growth, and environmental conditions, sensors enable farmers to optimize inputs such as water, fertilizers, and pesticides, reducing waste and improving yields. The development of advanced sensors, such as multi-spectral and hyper-spectral imaging, combined with artificial intelligence (AI) and machine learning (ML) algorithms, has revolutionized crop monitoring and disease detection, allowing for timely interventions that prevent significant losses. Additionally, the integration of wireless sensor networks (WSNs) and the Internet of Things (IoT) facilitates the seamless collection and transmission of data, enabling remote and automated farm management. This technology has proven instrumental in addressing the challenges of food security by increasing agricultural productivity and reducing input costs. Sensor technologies also promote sustainable land and water management by improving irrigation efficiency and reducing chemical runoff, thus minimizing environmental impact. Economic benefits are substantial, with farmers experiencing cost savings, increased yields, and higher profitability. Moreover, sensor technology contributes to climate-smart agriculture by improving resource use efficiency and reducing greenhouse gas emissions. However, challenges remain, including the high cost of sensor installation and maintenance, limited accessibility in remote regions, data privacy concerns, and the lack of interoperability between different sensor platforms. The future of sensor technology lies in the development of low-cost, biodegradable sensors, the increased use of autonomous and robotic platforms, and enhanced AI integration for more accurate data interpretation. Expanding the use of sensors in smallholder and developing world agriculture will be crucial for global food security and environmental sustainability, offering a promising path toward a more resilient and efficient agricultural system.

Microbial Degradation of Pesticides in Agricultural Environments: A Comprehensive review of Mechanisms, Factors and Biodiversity

Pesticides are used frequently in modern times to prevent and manage crop pests and diseases, but their residual effects have seriously harmed both the environment and the health of humans. Within the fields of all over the world applied restoration of the environment science and technology, the study of microbial breakdown of pesticides in soil ecosystems is a significant research area. Agricultural methods depend on the use of chemicals, including pesticides and herbicides, to control pests and weeds. However, these chemicals pose risks to the environment, human, and animal well-being. Microbes have shown promise in degrading these agricultural environmental hazards, mitigating their negative impact. The organisms that exist in the natural world, the research on bacteria that break down pesticides and herbicides, and the approach for the application of these bacteria has been summarized in the paper. This literature review aims to identify the microbes responsible for degrading these chemicals and assess their effectiveness in doing so. The goal of this review is to determine the bacteria responsible for the deterioration of agricultural environmental hazard chemicals, evaluate their efficacy in degrading these compounds, explore the factors influencing microbial degradation efficiency, and identify research gaps in the field. Inclusion criteria encompass studies published in English between 2010 and 2023 that focus on the degradation of agricultural environmental hazard chemicals by microbes, specifically microbial consortia, under controlled conditions. A systematic literature review will be conducted using databases like ScienceDirect, Web of Science, and PubMed. Data extracted from selected studies will include information on pesticide types, microorganisms involved in degradation, mechanisms of microbial degradation, factors affecting microbial degradation, current trends in microbial degradation of pesticides, biodiversity of pesticide-degrading microbes, plasmid-borne pesticide resistance in bacterial communities, and strategies for pesticide degradation by microbial consortia. This research tries to present a comprehensive knowledge of microbial degradation mechanisms, highlight the potential of microbial consortia in pesticide degradation, and contribute to sustainable and eco-friendly approaches for addressing pesticide residues in agricultural environments. This study focused on the variables that affect the microbial breakdown of pesticides and the technique by which microorganisms decompose under natural conditions. In addition, the current trends of research on the microbial degradation of pesticides as well as a few visible challenges that continue to need attention were described.

Immunomodulating melatonin-decorated silica nanoparticles suppress bacterial wilt (Ralstonia solanacearum) in tomato (Solanum lycopersicum L.) through fine-tuning of oxidative signaling and rhizosphere bacterial community

BackgroundTomato (Solanum lycopersicum L.) production is severely threatened by bacterial wilt, caused by the phytopathogenic bacterium Ralstonia solanacearum. Recently, nano-enabled strategies have shown tremendous potential in crop disease management.ObjectivesThis study investigates the efficacy of biogenic nanoformulations (BNFs), comprising biogenic silica nanoparticles (SiNPs) and melatonin (MT), in controlling bacterial wilt in tomato.MethodsSiNPs were synthesized using Zizania latifolia leaves extract. Further, MT containing BNFs were synthesized through the one-pot approach. Nanomaterials were characterized using standard characterization techniques. Greenhouse disease assays were conducted to assess the impact of SiNPs and BNFs on tomato plant immunity and resistance to bacterial wilt.ResultsThe SiNPs and BNFs exhibited a spherical morphology, with particle sizes ranging from 13.02 nm to 22.33 nm for the SiNPs and 17.63 nm to 21.79 nm for the BNFs, indicating a relatively uniform size distribution and consistent shape across both materials. Greenhouse experiments revealed that soil application of BNFs outperformed SiNPs, significantly enhancing plant immunity and reducing bacterial wilt incidence by 78.29% in tomato plants by maintaining oxidative stress homeostasis via increasing the activities of antioxidant enzymes such as superoxide dismutase (31.81%), peroxidase (32.9%), catalase (32.65%), and ascorbate peroxidase (47.37%) compared to untreated infected plants. Additionally, BNFs induced disease resistance by enhancing the production of salicylic acid and activating defense-related genes (e.g., SlPAL1, SlICS1, SlNPR1, SlEDS, SlPD4, and SlSARD1) involved in phytohormones signaling in infected tomato plants. High-throughput 16 S rRNA sequencing revealed that BNFs promoted growth of beneficial rhizosphere bacteria (Gemmatimonadaceae, Ramlibacter, Microscillaceae, Anaerolineaceae, Chloroplast and Phormidium) in both healthy and diseased plants, while suppressing R. solanacearum abundance in infected plants.ConclusionOverall, these findings suggest that BNFs offer a more promising and sustainable approach for managing bacterial wilt disease in tomato plants.Graphical

Wheat Rust Disease Detection Using Convolutional Neural Network

Wheat is a vital crop globally, playing a crucial role in ensuring food security. Any damage to wheat crops, particularly from diseases like wheat rust, can exacerbate the global food crisis. Wheat rust, along with other crop diseases, poses a serious threat to agricultural sustainability and food supply. Technological approaches to detect crop health can significantly enhance disease prevention compared to traditional manual methods. This study focuses on detecting wheat rust diseases, specifically wheat leaf rust, stem rust, and yellow rust, all of which vary in their impact on crops. By employing image analysis of wheat plants, the research aims to accurately distinguish between healthy crops and those affected by rust diseases. The experiments consider multiple variables, such as learning rate, dropout, and train-test split ratio, reflecting a comprehensive research approach. The model achieved an impressive 99.64% accuracy in detecting wheat rust, highlighting its potential for early disease detection and prevention.

Mitigating root rot in Panax notoginseng: The synergistic effects of biochar and Chaetomium globosum YIM PH30719

Root rot is the main soilborne disease that causes the replant failure of Panax notoginseng, a famous medicinal plant in Asia. Biochar and biocontrol agents have been shown to reduce replant failure separately; however, a knowledge gap still exists regarding the effectiveness and mechanisms of their combined application on the root rot incidence of P. notoginseng. In this study, we conducted a pot experiment using a randomized block design, biochar derived from tobacco stems was applied at 0, 1, 2, and 3 % (T0, T1, T2, T3) to the soil that cultivating P. notoginseng for 10 years continuously; moreover, Chaetomium globosum YIM PH30719, a newly screened biocontrol strain, was applied at 0, 1.0×107, and 1.0×1010 cfu·mL−1 (C0, C1, C2) to the soil. Six months post-transplantation of P. notoginseng seedlings, root rot incidence, soil physicochemical properties, fungal communities, and abundance of the primary pathogen Fusarium oxysporum were investigated. The results showed that, comparing with the single application of YIM PH30719, the combined application with biochar was more efficient in reducing the root rot incidence (P<0.05). P. notoginseng experienced a root rot incidence of 65.4 % under the T0+C0 treatment, however, under T3+C2 treatment, the root rot incidence was only 5.6 % (P<0.05). The reduction in root rot incidence might be linked to changes in soil pH, organic matter, and essential nutrient contents, as well as the increased soil fungal diversity and the relative abundance (RA) of beneficial Chaetomium and Trichoderma, and the decreased RA of pathogenic Fusarium and Alternaria. Consequently, the joint use of biochar and biocontrol agents (YIM PH30719) synergistically decreased the occurrence of root rot by modifying soil physicochemical characteristics and fungal communities. Since root rot is commonly found in agriculture, the results of this study may help in controlling root rot disease in other crops.