Diseases Of Crop Plants Research Articles

Inhibitory Effect of Sorbus aucuparia Extracts on the Fusarium proliferatum and F. culmorum Growth and Mycotoxin Biosynthesis.

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

Expedition of Trichoderma formulations: Production to storage in India-A review

The usage of biopesticides is far less compared to chemical pesticides available in the market. Trichoderma is an effective biocontrol agent with a long history of success stories compared to all other available biocontrol agents. Application of Trichoderma formulation enhances the plant growth and is more effective in managing diseases of crop plants. In India, several popular products derived from different Trichoderma species have found commercial success among organic growers. In India, certain quantity of fruitful produces are from diverse class of Trichoderma hence, it must be commercialized. Many small and medium entrepreneurs have recently started producing biocontrol agents for commercial use, which has led to the introduction of numerous biocontrol products in the global market. This paper aims to review and discuss the carrier materials, mass production techniques, target diseases, recommended dose, application methods, storage and shelf life of Trichoderma formulations. The main obstacle inthe commercialization of bioproducts is reduced shelf life. Therefore, this review will explain the various types of Trichoderma formulations in several substrates and commercial viability of the substrates. Key words: Efficacy, Formulation, Substrates, Shelf life, Trichoderma

Expression analysis and characterization of resistance (R) genes in contrasting Fusarium wilt resistant chickpea genotypes

Fusarium wilt is one of the most notorious diseases of crop plants. This disease leads to huge crop losses in chickpea with substantial monetary value worldwide. The objective of the current study was to characterise Fusarium oxysporum f. sp. cicris (Foc), the causal agent of chickpea wilt, on morphological basis followed by the assessment of resistance of thirty chickpea germplasm accessions to Foc in a pot experiment. The second objective of this research was to study gene expression and characterisation of specific resistance (R) genes in different Fusarium wilt-resistant chickpea genotypes. The morphological identification and characterization of Foc was done by culturing the fungus potato dextrose agar (PDA) medium. Fusarium wild resistance was assessed by challenging 30 chickpea genotypes to artificial inoculum of Foc by using a pre-defined disease rating scale. Different resistance levels were showed by different chickpea genotypes ranging from highly resistant to highly susceptible. Two accessions demonstrated resistance, 15 were moderately resistant and the rest of the genotypes were moderately susceptible to susceptible. The in-silico expression analysis of R genes showed that two R genes, LOC101499430 and LOC101499568 were upregulated in chickpea roots, indicating their probable role in Fusarium wilt resistance. The genes were further assessed for their expression in response to Fusarium inoculation in contrasting Fusarium wilt resistant and susceptible chickpea genotypes. The resistant genotypes showed significantly higher expression levels of the selected R genes in comparison with susceptible genotypes. This study provides valuable insights into evaluating chickpea germplasm for Fusarium wilt resistance and identification of candidate resistance genes which could be employed for enhancement of Fusarium wilt resistance in chickpea.

Biopesticide potential of Anisomeles indica Kuntze leaf essential oil and its nanoemulsion against bacterial blight - Xanthomonas strains

The objective of this study was to examine the chemical composition of essential oil A. indica leaves and its nanoemulsion, and evaluation of potential for use as a biopesticide. Essential oil of A. indica extracted from fresh leaves was found to be 99.97% composed of 59 components, according to GC-MS analysis. A non-ionic surfactant Tween 80 and the essential oil of A. indica were combined to create a stable nanoemulsion using sonication method. A long durable stable nanoemulsion was obtained after thirty minutes of sonication, and it was examined using TEM (Transmission electron microscopy), DLS (Dynamic light scattering), and FTIR (Fourier transform infrared spectroscopy) analyses. To consider its practical applications, other factors such as stability, viscosity, pH, turbidity, and creaming index were also investigated. Antibacterial properties of essential oil and nanoemulsion were evaluated against bacterial blight plant pathogens Xanthomonas oryzae and Xanthomonas citri. The MIC (Minimum inhibitory concentration) was determined using the broth microdilution method, and the anti-Xanthomonas activity was assessed utilizing by agar disc diffusion method. Using the crystal violet (CV) technique, inhibition of biofilm formation and development were evaluated. Disturbance of bacterial membrane permeability was confirmed by estimating the proteins and nucleic acids (DNA, and RNA) leakage from the cell membrane. Greenhouse studies using rice/paddy plants were conducted to demonstrate the antibacterial potential of essential oil and nanoemulsion, using RNR-15048 rice cultivar by seed treatment and foliar spray groups. Results of the investigations suggest that, when the essential oil was transformed into a nanoemulsion, its antibacterial effect against bacteria was significantly increased, that might be attributed to the essential oil enhanced ability to penetrate the bacterial cells. Essential oil of A. indica and its nanoemulsion reduced rice bacterial blight disease by 84%and pathogen-virulence by 92%, and promoted the growth and development of rice/paddy seedlings. These results confirm that A. indica essential oil and its nanoemulsion possess anti-bacterial blight properties, without affecting growth and development of host plants. Essential oil of A. indica and its nanoemulsion could be used as biopesticide to control Xanthomonas induced bacterial blight diseases in crop plants either by using for seed treatment or as a foliar spray.

A deep learning-based approach for early detection of disease in sugarcane plants: an explainable artificial intelligence model

<span>In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. <br /> A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.</span>

Single and multi-crop species disease detection using ITSO based gated recurrent multi-attention neural network

<p>Diseases of crop plants pose a serious danger to agricultural output and progress. Predicting the onset of a disease outbreak in advance can help public health officials better manage the pandemic. Precision agriculture (PA) applications rely heavily on current information and communication technologies (ICTs) for their contribution to long-term sustainability. Preventative measures against plant diseases require accurate early disease prediction in order to be effective. The current computer vision-based illness detection technology can only detect the disease after it has already manifested. This research intends to provide a deep learning (DL) method for early disease attack prediction using Internet of Things (IoT) directly sensed environmental factors from crop fields. There is a robust relationship between environmental factors and the life cycles of plant diseases. Disease incidence in plants can be forecast based on environmental variables in the crop field. In order to solve these issues, the research presented here suggests using a gated recurrent multi-attention neural network (GRMA-Net). The study uses multilevel modules to zero down on informative areas in order to extract additional discriminative features, as informative characteristics tend to appear at various levels in a network. In order to capture long-range dependence and contextual interaction, these characteristics are first organised as spatial sequences and then input into a deep-gated recurrent unit (GRU). Finally, an enhanced version of the Tunicate swarm optimisation model (ITSO) is used to pick the best values for the model’s hyper-parameters. Four public datasets representing a wide range of crop types are used to assess the model’s efficacy. Some of these databases cover numerous crop species, like PlantVillage (38 categories), while others focus on a single crop, such as Apple (4), Maize (4), or Rice (5). The experimental findings show that the system achieves 99.16% accuracy in identifying agricultural diseases, which is higher than the accuracy of other current deep-learning approaches.</p>

Iron oxide nanoparticles as enhancers of growth-promoting bacteria: a step towards developing nano-biofertilizers

Co-inoculation of IONPs and PGPB in the soybean seed treatment improves the seedling development of soybean and shows a viable alternative for use as a nano-biofertilizer or for improving the potential control of plant diseases in crops.

Multi-disease Classification of Mango Tree Using Meta-heuristic-based Weighted Feature Selection and LSTM Model

Global food security can be influenced by the diseases in crop plants as several diseases straightforwardly influence the quality of the grains, vegetables, fruits, etc., which also results in affecting of agricultural productivity. Like other plants, the mango tree is also affected by several diseases, and also the identification of multi-disease classification with a single leaf is more complex, and also it is impossible to detect diseases with bare eyes. Based on the other plants, the mango tree is also affected by various diseases, which is more difficult to detect the disorders with bare eyes. It is error-prone, inconsistent, and unreliable. Here, the mango trees are affected during the production, and also affect the plant health regarding multi-diseases. When the plants are affected by the diseases, it may cause fewer amounts of productivity, as a result, impacting the economy. However, it is more critical to detect plant diseases with the large varieties of trees and plants. Various research tasks on deep learning approaches focus on identifying the diseases in plants including leaves and fruits. Thus, the main objective of this paper is to implement an effective and appropriate technique for diagnosing mango tree diseases and their symptoms through fruit and leaf images, and thus, there is a need for an appropriate system for cost-effective and early solutions to this problem. Hence, the main intention of this work is to implement an efficient and suitable technique for diagnosing mango tree diseases and also identify the symptoms through fruit and leaf images. Intending to overcome the existing challenges, there is a need for an appropriate system for achieving cost-effectiveness and also creating an early solution to resolve this problem. This paper intends to present novel deep learning models for mango tree multi-disease classification. Initially, the data collection is done for gathering the diseased parts of the mango tree in terms of leaf and fruit images. Then, the contrast enhancement of the images is performed by the “Contrast-Limited Adaptive Histogram Equalization (CLAHE)”. For the deep feature extraction of leaf images, and fruit images, Convolutional Neural Network (CNN) is employed, and the features from both inputs are concatenated for further processing. Further, the weighted feature selection is adopted for selecting the most significant features by the Adaptive Squirrel-Grey Wolf Search Optimization (AS-GWSO). Enhanced “Long Short Term Memory (LSTM)” is applied in the classification part with parameter optimization using the same AS-GWSO for enhancing classification accuracy. At last, the results of the designed system on various mango tree diseases verify that the designed approach has yielded the highest accuracy by evaluating conventional approaches. Therefore, it would also alleviate and treat the affected mango leaf diseases accurately.

Detection and Classification of Plant Diseases in Crops (Solanum lycopersicum) due to Pests Using Deep Learning Techniques: A Review

All people have access to food thanks to agriculture, even in areas with rapid population expansion. In order to ensure that the entire population has access to food, early diagnosis of plant diseases is recommended. However, when crops are still maturing, it might be challenging to predict diseases. The goal of this paper is to inform farmers about the use of machine learning to recognize various pest species in various crop plants. Deep learning establishes a continuing, cutting-edge method for analysing images with significant potential and promising outcomes. DL has expanded into the field of agriculture after demonstrating its effectiveness in a number of applications. Here, we reviewed various research articles that used deep learning methods to address diverse research issues in tomato (Solanum lycopersicum) plants. We look at the study areas in tomato plants where deep learning, data preprocessing, transfer learning, and augmentation approaches are performed. studied dataset details, including the number of photos, classes, and train test to validation ratios that were employed. We also look at comparisons made across different deep learning architectures and analyse the results. The results demonstrated that deep learning techniques beat all other image processing methods, however how well DL works depended on the dataset employed.

Epidemiology and Control of Fungal Diseases in Crop Plants

Crop fungal diseases are a major threat to crop health and food security worldwide. The epidemiology is the basis for effective and sustainable control of crop fungal diseases. Safe, effective, sustainable, and eco-friendly disease control measures have important economic, ecological, and social significances. This Special Issue, “Epidemiology and Control of Fungal Diseases of Crop Plants”, collected one communication and nine original research articles focusing on the identification and detection of the causal agents of alfalfa Fusarium root rot, strawberry black spot, and barley leaf stripe; the semantic segmentation of wheat stripe rust images; the image-based identification of wheat stripe rust and wheat leaf rust; the image-based identification of the severity of wheat Fusarium head blight; the development process of vanilla Fusarium wilt; the regional migration of wheat leaf rust pathogen; the early prediction of potato early blight; the screening of alternative fungicides for the control of alfalfa Fusarium root rot; and the biocontrol potential of endophytic fungi to control of cumin root rot, presenting the progress of research on the epidemiology and control of crop fungal diseases. The studies contained in this Special Issue facilitated the development of epidemiology of the related crop fungal diseases and provided some basis for control of the diseases, which is conducive to the sustainable management of these diseases.

Plant and soil-associated microbiome dynamics determine the fate of bacterial wilt pathogen Ralstonia solanacearum.

Plant and the soil-associated microbiome is important for imparting bacterial wilt disease tolerance in plants. Plants are versatile organisms that are endowed with the capacity to withstand various biotic and abiotic stresses despite having no locomotory abilities. Being the agent for bacterial wilt (BW) disease, Ralstonia solanacearum (RS) colonizes the xylem vessels and limits the water supply to various plant parts, thereby causing wilting. The havoc caused by RS leads to heavy losses in crop productivity around the world, for which a sustainable mitigation strategy is urgently needed. As several factors can influence plant-microbe interactions, comprehensive understanding of plant and soil-associated microbiome under the influence of RS and various environmental/edaphic conditions is important to control this pathogen. This review mainly focuses on microbiome dynamics associated with BW disease and also provide update on microbial/non-microbial approaches employed to control BW disease in crop plants.

Cross-Kingdom Interactions Between Plant and Fungal Viruses.

The large genetic and structural divergences between plants and fungi may hinder the transmission of viruses between these two kingdoms to some extent. However, recent accumulating evidence from virus phylogenetic analyses and the discovery of naturally occurring virus cross-infection suggest the occurrence of past and current transmissions of viruses between plants and plant-associated fungi. Moreover, artificial virus inoculation experiments showed that diverse plant viruses can multiply in fungi and vice versa. Thus, virus cross-infection between plants and fungi may play an important role in the spread, emergence, and evolution of both plant and fungal viruses and facilitate the interaction between them. In this review, we summarize current knowledge related to cross-kingdom virus infection in plants and fungi and further discuss the relevance of this new virological topic in the context of understanding virus spread and transmission in nature as well as developing control strategies for crop plant diseases.

DETECTION AND CLASSIFICATION OF PLANT LEAF DISEASES USING DIGTAL IMAGE PROCESSING METHODS: A REVIEW

Prediction and classification of plant leaf illnesses by farmers using conventional approaches can be unexciting and erroneous. Problems may occur while trying to predict the sort of illnesses manually. Inability to detect diseases of plants promptly could lead to the destruction of crop plants and this can cause serious decline in the yield. Losses can be prevented and yield be maximized when farmers adopt computerized image processing approaches in their farms. Numerous techniques have been proposed and used in the prediction of diseases of crop plants based on the images of the infected leaves. Researchers have in the past achieved a lot in the aspect of plant illnesses identification by exploring several techniques and models. However, improvement needs to be provided on account of reviews, new advancements and discussions. Deploying technology can greatly enhance crop production across the globe. Different approaches and models can be trained with huge data to identify new improved methods for uncovering diseases of plants to tackle problem of low yield. Previous works have determined the robustness of various image processing techniques such as; K-means clustering, Naive Bayes, Feed forward neutral network (FFNN), Support Vector Machine (SVM), K-nearest neighbor (KNN) classifier, Fuzzy logic, Genetic Algorithm (GA), Artificial Neural Network (ANN), Convolutional Neural Network (CNN) etc. This paper provides a critical review and results of different types of approaches and methods used previously to detect and classify various types of plant leaf illnesses using image processing approaches.

Epidemiological role of novel and already known ‘Ca. P. solani’ cixiid vectors in rubbery taproot disease of sugar beet in Serbia

Rubbery taproot disease (RTD) of sugar beet was recently associated with the plant pathogenic bacterium ‘Candidatus Phytoplasma solani’ (CaPsol) and reported throughout the Pannonian Plain with variations in severity. Tracing CaPsol epidemiological pathways was performed in the experimental sugar beet field in Rimski Šančevi (Serbia) in 2020–2021, where an RTD outbreak was recently recorded. A molecular epidemiology approach was applied to the study of three RTD occurrence scenarios: epidemic, non-epidemic and ‘absence of RTD’. As a result, Hyalesthes obsoletus ex Convolvulus arvensis was detected as a CaPsol vector to sugar beet, while two other cixiids were identified for the first time as vectors of the CaPsol-induced plant disease in crops: Reptalus quinquecostatus and R. cuspidatus. R. quinquecostatus was proposed culpable for the 2020 RTD epidemic outbreak in Rimski Šančevi when dSTOLg CaPsol strain predominated in the RTD-affected sugar beet, whereas R. cuspidatus had a negligible role in RTD occurrence and displayed ambiguous involvement in CaPsol epidemiology on a wider scale. The temporal discrepancy of the offset of CaPsol dissemination and disease occurrence is the main obstacle in predicting CaPsol-induced diseases. Predicting disease occurrence and severity can only be achieved by gaining a better understanding of CaPsol epidemiological pathways and insect vectors involved in disease outbreaks.

Disease Identification in Crop Plants based on Convolutional Neural Networks

The identification, classification and treatment of crop plant diseases are essential for agricultural production. Some of the most common diseases include root rot, powdery mildew, mosaic, leaf spot and fruit rot. Machine learning (ML) technology and convolutional neural networks (CNN) have proven to be very useful in this field. This work aims to identify and classify diseases in crop plants, from the data set obtained from Plant Village, with images of diseased plant leaves and their corresponding Tags, using CNN with transfer learning. For processing, the dataset composing of more than 87 thousand images, divided into 38 classes and 26 disease types, was used. Three CNN models (DenseNet-201, ResNet-50 and Inception-v3) were used to identify and classify the images. The results showed that the DenseNet-201 and Inception-v3 models achieved an accuracy of 98% in plant disease identification and classification, slightly higher than the ResNet-50 model, which achieved an accuracy of 97%, thus demonstrating an effective and promising approach, being able to learn relevant features from the images and classify them accurately. Overall, ML in conjunction with CNNs proved to be an effective tool for identifying and classifying diseases in crop plants. The CNN models used in this work are a very good choice for this type of tasks, since they proved to have a very high performance in classification tasks. In terms of accuracy, all three models are very accurate in image classification, with an accuracy of over 96% with large data sets.

A hybrid approach: impact of IoT in rice crop plant disease detection using image processing

A hybrid approach: impact of IoT in rice crop plant disease detection using image processing

A hybrid approach: impact of IoT in rice crop plant disease detection using image processing

A hybrid approach: impact of IoT in rice crop plant disease detection using image processing

Two decades of association mapping: Insights on disease resistance in major crops.

Climate change across the globe has an impact on the occurrence, prevalence, and severity of plant diseases. About 30% of yield losses in major crops are due to plant diseases; emerging diseases are likely to worsen the sustainable production in the coming years. Plant diseases have led to increased hunger and mass migration of human populations in the past, thus a serious threat to global food security. Equipping the modern varieties/hybrids with enhanced genetic resistance is the most economic, sustainable and environmentally friendly solution. Plant geneticists have done tremendous work in identifying stable resistance in primary genepools and many times other than primary genepools to breed resistant varieties in different major crops. Over the last two decades, the availability of crop and pathogen genomes due to advances in next generation sequencing technologies improved our understanding of trait genetics using different approaches. Genome-wide association studies have been effectively used to identify candidate genes and map loci associated with different diseases in crop plants. In this review, we highlight successful examples for the discovery of resistance genes to many important diseases. In addition, major developments in association studies, statistical models and bioinformatic tools that improve the power, resolution and the efficiency of identifying marker-trait associations. Overall this review provides comprehensive insights into the two decades of advances in GWAS studies and discusses the challenges and opportunities this research area provides for breeding resistant varieties.

Role of nanotechnology in improving quality of edible crop plants

Improving human health is the considered major concern in area of medical and agricultural sciences. It is achieved by providing good health supplements with balanced diet to people in the forms of cost effective and safe nano-biofortified or nano-bioengineered vegetable crop plants to fight against malnutrition and other various diseases. And these nano-based methodologies are involved the use of nanoparticles of essential nutrients like copper, iron, selenium, cobalt, and zinc being as fertilizers or stimulants in soils or waters to improve the qualities of vegetable crop plants and make them more enriched with major nutrients and diseased free. Hence, this strategic clinical and agro-practice based management should be considered to combat fungal and microbial diseases of crop plants through cost effective and green nanotechnology-based approach used in the form of nano-formulation and fortified bioagents and biostimulants.

Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use convolutional neural networks (CNN), a type of DL, to address various plant disease detection concerns was undertaken in the current publication. In this work, we have reviewed 100 of the most relevant CNN articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the CNN used in plant leaf disease detection. Moreover, Deep convolutional neural networks (DCNN) trained on image data were the most effective method for detecting early disease detection. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant disease detection.