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Lotus leaf diseases and pests have seriously threatened the yield and quality of lotus seeds, such as rhizome rot, leaf spot, virus disease, and Spodoptera litura. The detection of diseased leaves has been the most important measure to prevent and control the lotus leaf diseases and pests. However, manual detection cannot fully meet the large-scale production in recent years, due to the subjective and inefficient experience. It is still lacking in professional knowledge, easily leading to missed or false detection. Therefore, automatic detection can be expected to improve the planting quality in the actual environment of lotus fields. In this study, a lightweight detection model was proposed for the lotus leaf disease and pest using improved YOLOv8. The detection accuracy was improved to reduce the calculation scale for the better deployability of the model. At the same time, a new dataset of lotus leaf disease and pests was established to consider the different environmental conditions. Firstly, the convolution module (Conv) in the YOLOv8 neck network was replaced with GSConv. The C2f module was replaced with the VoV-GSCSP to form a slim-neck architecture, in order to reduce the computational complexity of the model for the high recognition accuracy. The C2f_EMA module was integrated with the EMA efficient multi-scale attention mechanism. The C2f module was then replaced in the backbone network, in order to extract the features of lotus leaf pests and diseases in the complex environments. The experimental results show that the improved YOLOv8 detection model effectively detected the lotus leaf pests and diseases. The mean average precision (mAP) was achieved at 89.3%, which was 1.6% higher than the baseline model; The number of parameters of the model was reduced by 0.2M, compared with the baseline model. The model size was only 5.6 MB. A comparison was performed on several mainstream one-stage target detection models: Faster RCNN, SSD, YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and YOLOv9. The results show that the improved YOLOv8 model shared significant advantages in the detection accuracy, number of parameters, and model size, compared with the rest mainstream models. Finally, the improved YOLOv8 model was deployed on the Jetson Xavier NX and Raspberry Pi 4B edge computing devices, where the frame rates were 27 and 0.7 frames/s, respectively. Compared with the YOLOv5 model, the frame rates increased by 8.9 and 0.3 frames/s, respectively. In terms of actual deployment performance, the improved YOLOv8 model performed better than the classic YOLOv5 model, indicating a better prospect for the mobile terminal deployment. The accurate identification can provide support to the automatic prevention and control of lotus leaf diseases and pests. [ABSTRACT FROM AUTHOR]

[Objectives] After being infected by pathogens or pests, citrus leaves can lead to abnormal growth and development, reduced yield, and even death of citrus trees. Early detection of citrus leaf diseases and pests can effectively allow for preventive measures to reduce losses [Methods] In the actual detection process, the YOLOv5s model had problems such as inaccurate positioning and complex background. Inspired by the VAN(visual attention network)model, the LKA(large kernel attention)module was introduced to improve the YOLOv5s model. The improved YOLOv5s model could achieve centralized attention and fine extraction of image information; replacing conventional up sampling methods with CARAFE lightweight operators could improve feature reconstruction quality, solve scale mismatch problems, and enhance detection performance; using the FReLU activation function could capture more key features of citrus pests and diseases, improving detection accuracy. In addition, a dataset of citrus leaves containing anthracnose, ulcer disease, and infestation by leaf miner pests was constructed for experimentation. [Results] The improved model YOLOv5-LC showed the detection results of citrus pests and diseases as follows: the average detection accuracy mAP50 reached 94.5% and mAP50:95 was 84.3%, which were 2.0% and 4.4% higher than the original model, and the model size was only 7.3 MB, with 93.8% accuracy and 84.5% recall, and the number of floating-point operations was only 18.5 G. [Conclusions] The improved model YOLOv5-LC could more accurately detect citrus pests and diseases. [ABSTRACT FROM AUTHOR]

In the world, grapes are considered as the most significant fruit, and it comprises various nutrients, like Vitamin C and it is utilized to produce wines and raisins. The major six general grape leaf diseases and pests are brown spots, leaf blight, downy mildew, anthracnose, and black rot. However, the existing manual detection methods are time-consuming and require more efforts. In this paper, an effectual grape leaf disease finding and percentage of pesticide detection approach is devised usingan optimized deep learning scheme. Here, the input image is pre-processed and then, black spot segmentation is done using proposed Taylor Remora Optimization Procedure (TROA). The TROA is the combination of Taylor concept and Remora Optimization Algorithm (ROA). After that, the multi-classification of grape leaf disease is performed to classify the disease as Black rot, Black measles, Isariopsis leaf spot and healthy. Accordingly, the training process of the Deep Neuro-Fuzzy Optimizer (DNFN) is done using Sine Cosine Butterfly Optimization (SCBO). Then, pesticide classification is done using Deep Maxout Network (DMN) and the training of the DMN is done using the Monarch Anti Corona Optimization (MACO) algorithm. Finally, the pesticide percentage level detection is performed using Deep Belief Network (DBN), which is trained by the TROA. The devised scheme obtained highest accuracy of 0.9327, sensitivity of 0.9383, and 0.9429. Thus, this method can assist as an effectual decision provision system for assisting the farmers to find the percentage of pesticide affected in grape leaf diseases. [ABSTRACT FROM AUTHOR]

Compared to traditional manual methods for assessing the cotton verticillium wilt (CVW) hazard level, utilizing deep learning models for foliage segmentation can significantly improve the evaluation accuracy. However, instance segmentation methods for images with complex backgrounds often suffer from low accuracy and delayed segmentation. To address this issue, an improved model, YOLO-VW, with high accuracy, high efficiency, and a light weight, was proposed for CVW hazard level assessment based on the YOLOv10n model. (1) It replaced conventional convolutions with the lightweight GhostConv, reducing the computational time. (2) The STC module based on the Swin Transformer enhanced the expression of foliage and disease spot boundary features, further reducing the model size. (3) It integrated a squeeze-and-excitation (SE) attention mechanism to suppress irrelevant background information. (4) It employed the stochastic gradient descent (SGD) optimizer to enhance the performance and shorten the detection time. The improved CVW severity assessment model was then deployed on a server, and a real-time detection application (APP) for CVW severity assessment was developed based on this model. The results indicated the following. (1) The YOLO-VW model achieved a mean average precision (mAP) of 89.2% and a frame per second (FPS) rate of 157.98 f/s in assessing CVW, representing improvements of 2.4% and 21.37 f/s over the original model, respectively. (2) The YOLO-VW model's parameters and floating point operations per second (FLOPs) were 1.59 M and 7.8 G, respectively, compressed by 44% and 33.9% compared to the original YOLOv10n model. (3) After deploying the YOLO-VW model on a smartphone, the processing time for each image was 2.42 s, and the evaluation accuracy under various environmental conditions reached 85.5%, representing a 15% improvement compared to the original YOLOv10n model. Based on these findings, YOLO-VW meets the requirements for real-time detection, offering greater robustness, efficiency, and portability in practical applications. This model provides technical support for controlling CVW and developing cotton varieties resistant to verticillium wilt. [ABSTRACT FROM AUTHOR]

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In modern horticulture, the grape industry across the globe has been coping with the issue of grape crop diseases. The detection of grape leaf diseases using automated methods can greatly assist farmers in mitigating yield losses and ensuring sustainability. However, existing systems face hurdles while handling grape leaf images at the farm level, and these models fail to generalize well on un-seen images. This study proposes the development of a well-curated real-time dataset of grape leaf images assimilated through field visits in the study area in India. This designed dataset is further used to train convolutional neural network models to accurately identify and classify grape leaves as either diseased or healthy. The potential of transfer learning using CNN models like VGG, ResNet, Inception, and Xception is assessed on the curated dataset. Our findings indicate that ResNet50V2 outperformed the other models in accurately identifying and classifying grape leaf diseases. Using transfer learning, existing weights (pretrained) and learned features were utilized for further training and fine-tuning the CNN models on our curated dataset. The results of the proposed approach are compared with existing automated grape leaf disease identification techniques. It is observed that the proposed approach, which is on a real-time grape leaf image dataset, provides the highest accuracy among others. Further, this study provides a wellcurated dataset of on-field grape leaf images in the Indian context, which can serve as a benchmark for future research. This study shows that deep learning techniques can aid farmers in identifying grape leaf diseases early. [ABSTRACT FROM AUTHOR]

In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool's applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices. [ABSTRACT FROM AUTHOR]

Maize, as one of the most important crops in the world, faces severe challenges from various diseases and pests. The timely and accurate identification of maize leaf diseases and pests is of great significance for ensuring agricultural production. Currently, the identification of maize leaf diseases and pests faces two key challenges: (1) In the actual process of identifying leaf diseases and pests, complex backgrounds can interfere with the identification effect. (2) The subtle features of diseases and pests are difficult to accurately extract. To address these challenges, this study proposes a maize leaf disease and pest identification model called LFMNet. Firstly, the localized multi-scale inverted residual convolutional block (LMSB) is proposed to perform preliminary down-sampling on the image, preserving important feature information for the subsequent extraction of fine disease and pest features in the model structure. Then, the feature localization bottleneck (FLB) is proposed to improve the model's ability to focus on and locate disease and pest characteristics and to reduce interference from complex backgrounds. Subsequently, the multi-hop local-feature fusion architecture (MLFFA) is proposed, which effectively addresses the problem of extracting subtle features by enhancing the extraction and fusion of global and local disease and pest features in images. After training and testing on a dataset containing 19,451 images of maize leaf diseases and pests, the LFMNet model demonstrated excellent performance, with an average identification accuracy of 95.68%, a precision of 95.91%, a recall of 95.78%, and an F1 score of 95.83%. Compared to existing models, it exhibits significant advantages, offering robust technical support for the precise identification of maize diseases and pests. [ABSTRACT FROM AUTHOR]

Lesion mimic often exhibits leaf disease-like symptoms even in the absence of pathogen infection, and is characterized by a hypersensitive-response (HR) that closely linked to plant disease resistance. Despite this, only a few lesion mimic genes have been identified in wheat. In this investigation, a lesion mimic wheat mutant named je0297 was discovered, showing no alteration in yield components when compared to the wild type (WT). Segregation ratio analysis of the F2 individuals resulting from the cross between the WT and the mutant revealed that the lesion mimic was governed by a single recessive gene in je0297. Using Bulked segregant analysis (BSA) and exome capture sequencing, we mapped the lesion mimic gene designated as lm6 to chromosome 6BL. Further gene fine mapping using 3315 F2 individuals delimited the lm6 within a 1.18 Mb region. Within this region, we identified 16 high-confidence genes, with only two displaying mutations in je0297. Notably, one of the two genes, responsible for encoding flavonol synthase, exhibited altered expression levels. Subsequent phenotype analysis of TILLING mutants confirmed that the gene encoding flavonol synthase was indeed the causal gene for lm6. Transcriptome sequencing analysis revealed that the DEGs between the WT and mutant were significantly enriched in KEGG pathways related to flavonoid biosynthesis, including flavone and flavonol biosynthesis, isoflavonoid biosynthesis, and flavonoid biosynthesis pathways. Furthermore, more than 30 pathogen infection-related (PR) genes exhibited upregulation in the mutant. Corresponding to this expression pattern, the flavonoid content in je0297 showed a significant decrease in the 4th leaf, accompanied by a notable accumulation of reactive oxygen, which likely contributed to the development of lesion mimic in the mutant. This investigation enhances our comprehension of cell death signaling pathways and provides a valuable gene resource for the breeding of disease-resistant wheat [ABSTRACT FROM AUTHOR]

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers’ model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best’ model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies. [ABSTRACT FROM AUTHOR]

Automatic detection and classification of mango leaf diseases is a complex task and manual detection system has issues like absence of experts, high cost, and lot of variations in the symptoms of the leaf disease. Hence we propose an automated solution in which the input images are gathered from standard resources and feature enhanced using contrast enhancement followed by segmentation using optimized Fuzzy C Means (FCM). Parameter optimization is done by Deviation-based Updated Dingo Optimizer (D-UDOX). The weighted feature selection is done by D-UDOX. The weighted features are provided to the classifier of Optimized Recurrent Neural Network (WO-RNN). Also, deep features are collected from a segmented image using ResNet-150. The classification with the extracted deep features is performed by WO-RNN. The parameter modification is done using D-UDOX for RNN and ResNet-150. The result of the recommended model achieves 96% accuracy and 93% F1-score which is relatively better than contemporary works. [ABSTRACT FROM AUTHOR]

This research delves into the critical domain of Tomato Leaf Disease classification using advanced machine learning techniques. Specifically, a comparative evaluation was conducted between a Base CNN model devoid of ResNet-50 integration and a Proposed Method harnessing the capabilities of ResNet-50. The results elucidated a notable enhancement in performance metrics when leveraging ResNet-50, with the Proposed Method consistently achieving exceptional accuracy scores of 99.96%, 99.98%, and 99.96% across data splits of 90:10, 80:20, and 70:30, respectively. Furthermore, the ResNet-50 integration significantly augmented key metrics, including recall, precision, and F1-Score, thereby accentuating its pivotal role in enhancing sensitivity and positive predictive value for tomato leaf disease classification. As for prospective research trajectories, this study highlights potential avenues for refinement, encompassing the exploration of ensemble techniques amalgamating diverse architectural frameworks, advanced data augmentation methodologies, and broader disease classification scopes. Collectively, this research underscores the transformative potential of ResNet-50 in agricultural diagnostics, advocating for continued exploration and innovation to fortify global food security and sustainable farming practices. Future research could explore ensemble techniques, advanced data augmentation, broader disease classification scopes, and interdisciplinary collaborations to develop comprehensive diagnostic tools for sustainable farming practices and global food security. [ABSTRACT FROM AUTHOR]

Introduction: The study addresses challenges in detecting cotton leaf pests and diseases under natural conditions. Traditional methods face difficulties in this context, highlighting the need for improved identification techniques. Methods: The proposed method involves a new model named CFNet-VoV-GCSP-LSKNet-YOLOv8s. This model is an enhancement of YOLOv8s and includes several key modifications: (1) CFNet Module. Replaces all C2F modules in the backbone network to improve multi-scale object feature fusion. (2) VoV-GCSP Module. Replaces C2F modules in the YOLOv8s head, balancing model accuracy with reduced computational load. (3) LSKNet Attention Mechanism. Integrated into the small object layers of both the backbone and head to enhance detection of small objects. (4) XIoU Loss Function. Introduced to improve the model's convergence performance. Results: The proposed method achieves high performance metrics: Precision (P), 89.9%. Recall Rate (R), 90.7%. Mean Average Precision (mAP@0.5), 93.7%. The model has a memory footprint of 23.3MB and a detection time of 8.01ms. When compared with other models like YOLO v5s, YOLOX, YOLO v7, Faster R-CNN, YOLOv8n, YOLOv7-tiny, CenterNet, EfficientDet, and YOLOv8s, it shows an average accuracy improvement ranging from 1.2% to 21.8%. Discussion: The study demonstrates that the CFNet-VoV-GCSP-LSKNet-YOLOv8s model can effectively identify cotton pests and diseases in complex environments. This method provides a valuable technical resource for the identification and control of cotton pests and diseases, indicating significant improvements over existing methods. [ABSTRACT FROM AUTHOR]

Coconut farming is a significant agricultural activity in South India, but the coconut trees face challenges due to adverse weather conditions and environmental factors. These challenges include various leaf diseases and pest infestations. Identifying and locating these issues can be difficult because of the large foliage and shading provided by the coconut trees. Recent research has shown that Computer Vision algorithms are becoming increasingly important for solving problems related to object identification and detection. So, in this work, the YOLOv4 algorithm was employed to detect and pinpoint diseases and infections in coconut leaves from images. The YOLOv4 model incorporates advanced features such as cross-stage partial connections, spatial pyramid pooling, contextual feature selection, and path-based aggregation. These features enhance the model's ability to efficiently identify issues such as yellowing and drying of leaves, pest infections, and leaf flaccidity in coconut leaf images taken in various environmental conditions. Furthermore, the model's predictive accuracy was enhanced through multi-scale feature detection, PANet feature learning, and adaptive bounding boxes. These improvements resulted in an impressive 88% F1-Score and an 85% Mean Average Precision. The model demonstrates its effectiveness and robustness even when dealing with medium-resolution images, offering improved accuracy and speed in disease and pest detection on coconut leaves. [ABSTRACT FROM AUTHOR]

Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for the validation process, such as apple, grape, and Citrus. In the first step, a noisy dataset is prepared by employing the original images to learn the designed framework better. The EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy data. After that, features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm (PFA). This algorithm aims to select the best features based on the fitness score and ignore redundant information. The selected features are finally classified usingmachine learning classifiers such asMediumNeuralNetwork, WideNeuralNetwork, and Support Vector Machine. The experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%, 99.7%, 99.7%, and 93.4% for apple, grape, Citrus fruit, and citrus plant leaves, respectively. A detailed analysis is conducted and also compared with the recent techniques, and the proposed framework shows improved accuracy. [ABSTRACT FROM AUTHOR]

In the National Institute of Research and Development for Potato and Sugar Beet Brasov, sixteen potato clones were evaluated to find the relative resistant one to late blight (Phytophthora infestans) and thereby diminish the number of fungicide sprays required. In the two years of study the presence of clones relatively resistant, which allows the development of an adequate protection strategy and the possibility to reduce the number of treatments has been observed. Disease development on foliage was assessed as percentage of foliage area damaged. The research was conducted under natural pressure of infection. From all clones studied, 1895/1, 1901/11 and 1895/4 showed the lowest level of resistance on the foliage, followed by 1927/1, 1941/8 and 1939/2. Instead, the clones 1901/6, 1930/and, 1979/5 were located towards the upper limit of resistance on the foliage during the entire vegetation period. [ABSTRACT FROM AUTHOR]

Coffee Robusta (Coffea canephora) increased its total production by 73.5% during the first quarter of 2023. In this study, twenty (20) samples each of healthy and infected coffee leaves were measured for their transmittance properties in the UV-Vis and NIR regions. Coffee Leaf Miner (CLM)-infected leaves were identified based on translucent patches on the plant foliage. The results showed that a healthy coffee leaf has a mean transmittance of 41.53 μW for the NIR region, while for the infected leaves, the mean transmittance is 47.06 μW. Healthy coffee Robusta leaves showed significant differences in their transmittance properties compared to infected coffee Robusta leaves in the UV (r = −0.15, p = 0.021, F = 5.8, t = −0.286), visible (r = −0.15, p = 0.018, F = 6.11, t = −2.88), and NIR (r = −0.14, p = 0.027, F = 5.28, t = −2.99) regions. A CLM index was introduced based on the intensity ratio of green and red wavelengths. I535/575 showed positive correlation with the estimated chlorophyll-a concentration for healthy (r = 0.94, p = 0.227) and infected (r = 0.56, p = 0.622) leaves. This method leads to the development of portable sensors for the early detection of CLM pests in plants. [ABSTRACT FROM AUTHOR]

Superior inbred lines are central to maize breeding as sources of natural variation. Although many elite lines have been sequenced, less sequencing attention has been paid to newly developed lines. We constructed a genome assembly of the elite inbred line KA105, which has recently been developed by an artificial breeding population named Shaan A and has shown desirable characteristics for breeding. Its pedigree showed genetic divergence from B73 and other lines in its pedigree. Comparison with the B73 reference genome revealed extensive structural variation, 58 presence/absence variation (PAV) genes, and 1023 expanded gene families, some of which may be associated with disease resistance. A network-based integrative analysis of stress-induced transcriptomes identified 13 KA105-specific PAV genes, of which eight were induced by at least one kind of stress, participating in gene modules responding to stress such as drought and southern leaf blight disease. More than 200,000 gene pairs were differentially correlated between KA105 and B73 during kernel development. The KA105 reference genome and transcriptome atlas are a resource for further germplasm improvement and surveys of maize genomic variation and gene function. [ABSTRACT FROM AUTHOR]

The study of pests and diseases on crops and crop relatives in the wild is valuable from both a theoretical and an applied point of view. Few studies have addressed multiple pests and diseases in such ecosystems. Our objectives were to (1) compare the prevalence of multiple pests and diseases on coffee in forests and more managed landscapes and (2) assess how spatial, environmental, host density and management factors affect the incidence and severity of coffee pests and diseases across forests. To achieve these objectives we studied different pests, diseases and one hyperparasite on wild and semi-wild coffee from 84 plots of 20 × 20 m across a forested landscape in southwest Ethiopia and compared the results to previous studies of more intensively managed adjacent landscapes. The prevalence of all surveyed coffee leaf pests and diseases was high (>71% of investigated plots) and very similar to the levels in more intensively managed landscapes reported in the literature. The incidence rates of all pests, diseases and the hyperparasite showed a high variation among the plots, and correlation with each other in some cases. However, this variation was weakly related to the measured environmental and management variables, but coffee pests and diseases were often positively related to high coffee density. One possible explanation for the similar prevalence is that, although the landscapes are different, the local environmental conditions have much in common since coffee is also grown under indigenous shade trees in more intensively managed landscapes. However, the variability in pest and disease levels among sites was large and it is difficult to predict where they attain high levels. There is a need of detailed investigations on drivers of spatio-temporal population dynamics of these species, including their natural enemies, to be able to provide advice for development of sustainable coffee disease management. [ABSTRACT FROM AUTHOR]

The primary organ of the plant, the leaf, is classified automatically using image processing methods. During processing, the leaf images' features are taken out and used for categorization. Research on plants and agriculture benefits from the extraction of leaf features. The manual feature extraction process is less structured and involves measuring features like shape symmetry, color, etc. with accuracy. Advancements in image processing techniques offer a productive means of extracting features from images of leaves, and numerous innovative techniques have been put forth in the literature. These studies rely less on human intuition and instead focus on automatically extracting features from images using a variety of algorithms. [ABSTRACT FROM AUTHOR]

Potato is one of the most important crops worldwide, and its productivity can be affected by various diseases, including leaf diseases. Early detection and accurate diagnosis of leaf diseases can help prevent their spread and minimize crop losses. In recent years, Convolutional Neural Networks (CNNs) have shown great potential in image classification tasks, including disease detection in plants. In this study, we propose a CNN-based approach for the prediction of potato leaf diseases. The proposed method uses a pre-trained CNN model, which is fine-tuned on a dataset of potato leaf images. The dataset includes images of healthy leaves and leaves infected with different diseases such as early blight and late blight. The trained model is then used to classify new images of potato leaves into healthy or diseased categories. The proposed approach achieves 97.4% accuracy in the classification of potato leaf diseases such as early blight potato leaf disease and late blight potato leaf disease, and can be used as an effective tool for early detection and management of these diseases in potato crops. [ABSTRACT FROM AUTHOR]

Due to obstruction in photosynthesis, the leaves of the plants get affected by the disease. Powdery mildew is the main disease in cucumber plants which generally occurs in the middle and late stages. Cucumber plant leaves are affected by various diseases, such as powdery mildew, downy mildew and Alternaria leaf spot, which ultimately affect the photosynthesis process; that's why it is necessary to detect diseases at the right time to prevent the loss of plants. This paper aims to identify and classify diseases of cucumber leaves at the right time using a deep convolutional neural network (DCNN). In this work, the Deep-CNN model based on disease classification is used to enhance the performance of the ResNet50 model. The proposed model generates the most accurate results for cucumber disease detection using data enhancement based on a different data set. The data augmentation method plays an important role in enhancing the characteristics of cucumber leaves. Due to the requirements of the large number of parameters and the expensive computations required to modify standard CNNs, the pytorch library was used in this work which provides a wide range of deep learning algorithms. To assess the model accuracy large quantity of four types of healthy and diseased leaves and specific parameters such as batch size and epochs were compared with various machine learning algorithms such as support vector machine method, self-organizing map, convolutional neural network and proposed method in which the proposed DCNN model gave better results. [ABSTRACT FROM AUTHOR]

The detection and accurate positioning of agricultural pests and diseases can significantly improve the effectiveness of disease and pest control and reduce the cost of prevention and control, which has become an urgent need for crop production. Aiming at the low precision of maize leaf pest and disease detection, a new model of maize leaf pest and disease detection using attention mechanism and multi-scale features was proposed. Our model combines a convolutional block attention module (CBAM) with the ResNet50 backbone network to suppress complex background interference and enhance feature expression in specific regions of the maize leaf images. We also design a multi-scale feature fusion module that aggregates local and global information at different scales, improving the detection performance for objects of varying sizes. This module reduces the number of parameters and enhances efficiency by using a lightweight module and replacing the deconvolutional layer. Experimental results on a natural environment dataset demonstrate that our proposed model achieves an average detection accuracy of 85.13%, which is 9.59% higher than the original CenterNet model. The model has 24.296 M parameters and a detection speed of 23.69 f/s. Compared with other popular models such as SSD-VGG, YOLOv5, Faster-RCNN, and Efficientdet-D0, our proposed model demonstrates superior performance in the fast and accurate detection of maize leaf pests and diseases. This model has practical applications in the identification and treatment of maize pests and diseases in the field, and it can provide technical support for precision pesticide application. The trained model can be deployed to a web client for user convenience. [ABSTRACT FROM AUTHOR]

Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf diseases and insect pests, a novel identification model based on an improved ResNet-50 architecture was proposed, which incorporated the coordinate attention (CA) module and weight-adaptive multi-scale feature fusion (WAMFF) to enhance the ResNet-50's image feature extraction capabilities. Transfer learning and online data enhancement are employed to boost the model's generalization ability. The proposed model achieved a top-1 accuracy rate of 98.32% on the basis of AppleLeaf9 datasets, which is 4.58% higher than the value from the original model, and the improved model can effectively improve the localization of lesion features. Furthermore, compared with mainstream deep networks, such as AlexNet, VGG16, DenseNet, MNASNet, and GoogLeNet on the same dataset, the top-1 accuracy rate increased by 7.3%, 3.19%, 4.98%, 6.04% and 3.87%, respectively. The experimental results demonstrate that the improved model is effective in improving the identification accuracy of apple leaf diseases and insect pests and enhancing the model's effective feature extraction capabilities. [ABSTRACT FROM AUTHOR]

In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms. [ABSTRACT FROM AUTHOR]

Detection technology has been widely used for apple leaf diseases and pests in recent years, with the rapid development of deep learning. However, most of the existing identification equipment is deployed in the field, with limited network conditions and computing resources, whereas, a wide variety of diseases and pests, with limited recognition accuracy, which is not conducive to the development of digital agriculture. In this study, a fast identification (S-DenseNet-E) was proposed to realize the best effect on public data sets. The DenseNet's Dense module was selected to add an auxiliary model, in order to facilitate the identification of indistinguishable diseases. The S-DenseNet model was designed for the multi-diseases using the DenseNet121. The aggregation connection was adopted only in the output layer of the module in the S-DenseNet. The high reasoning speed of DenseNet121 was achieved to solve the dense connection. The average all-category F1-score of SDenseNet was 85.14%, the inference time was 33.03 ms, and the number of parameters was 1.1 M. Compared with DenseNet121, the average F1-score of all categories was improved by 0.13 by 4.28, the inference time was shortened by 32.87 ms, and the number of parameters was reduced by 6.96 M. An auxiliary model was used to assist S-DenseNet to identify the indistinguishable disease, which was built S-DenseNet-E of composite recognition framework. Furthermore, the one-vote strategy was adopted under two-model voting. The S-DenseNet-E maintained the high recognition of the S-DenseNet for the multi-diseases, while the effective recognition of the indistinguishable disease indicated a low inference time and small parameters. S-DenseNet-E achieved an average F1-score of 85.86% in all categories, an F1-score of 70.10% for indistinguishable disease, an inference time of 38.92 ms, and a parameter of 2.2 M. Compared with the S-DenseNet, the SDenseNet-E improved the F1-score by 4.28 on indistinguishable diseases, and the inference time by 5.89 ms. Compared with the DenseNet121, S-DenseNet-E shared an average F1-score improvement of 0.85 percentage points in all categories, an F1- score improvement of 5.18 percentage points in indistinguishable disease, a reduction in the number of parameters by 5.86 M, where the inference time was reduced by 26.98 ms. Therefore, the S-DenseNet-E presented better recognition performance for two complex situations, namely, apples suffering from multiple diseases and unidentifiable diseases, where was required fewer computational resources. The practical application of the model was also verified in the field. Specifically, 1 730 apple leaf disease images were collected on site in Banan District, Chongqing, China. Three types were divided into healthy, Scab, and rust leaves. The improved model was compared with other models for experiments. The S-DensseNet model showed a recognition accuracy of 91.91% for healthy apple leaves, 91.83% for Scab apple leaves, and 98.43% for Trust apple leaves. The S-DensseNet model also demonstrated the least inference time, only requiring 42.72 ms. The experimental results show that the S-DenseNet and S-DenseNet-E can be expected to run on embedded edge computing devices, while more accurately identifying the multiple diseases and unidentifiable diseases of apple leaves, and fully meeting the actual production needs of apple orchards. The finding has an important significance for the development of digital agriculture. [ABSTRACT FROM AUTHOR]

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model's generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases' and pests' prevention and control. [ABSTRACT FROM AUTHOR]

Plant diseases reduce yield and quality in agricultural production by 20–40%. Leaf diseases cause 42% of agricultural production losses. Image processing techniques based on artificial neural networks are used for the non-destructive detection of leaf diseases on the plant. Since leaf diseases have a complex structure, it is necessary to increase the accuracy and generalizability of the developed machine learning models. In this study, an artificial neural network model for bean leaf disease detection was developed by fusing descriptive vectors obtained from bean leaves with HOG (Histogram Oriented Gradient) feature extraction and transfer learning feature extraction methods. The model using feature fusion has higher accuracy than only HOG feature extraction and only transfer learning feature extraction models. Also, the feature fusion model converged to the solution faster. Feature fusion model had 98.33, 98.40 and 99.24% accuracy in training, validation, and test datasets, respectively. The study shows that the proposed method can effectively capture interclass distinguishing features faster and more accurately. [ABSTRACT FROM AUTHOR]

Influenced by the ecological environment and other factors, the quality and yield of lotus root have been seriously affected by the occurrence of diseases and insect pests in recent years. With the improvement of living standards and the development of the lotus industry chain, people are looking for green food, high-yield and high-quality products. Nowadays, many farmers and planters are unable to accurately identify the diseases and pests of lotus due to lack of professional knowledge of diseases and insect pests control. There is a shortage of efficient, low-cost and automatic identification technology for the prevention and control of lotus diseases and insect pests. The diagnosis and identification of diseases and insect pests are of great significance for the prevention and control of diseases and insect pests in lotus fields. Over the past few years, deep learning technology has been widely used in the field of plant diseases and insect pests recognition to automatically extract the features of plant diseases and insect pests. In order to achieve an efficient and accurate diagnosis of lotus leaf diseases and insect pests, lotus leaf diseases and insect pests dataset was constructed and preliminary experiments were constructed on AlexNet, VGG-16, ResNet50, ResNeXt50, and DenseNet121 models. The experimental results indicated that DenseNet121 has the best performance on the dataset, lotus leaf diseases and insect pests identification model based on improved DenseNet was improved. Firstly, different methods for dynamic data enhancement were compared in this paper. The results show that resizing and randomly resizing the image is more accurate than directly resizing to the same size. The loss of detail information in part of the image is caused by over-compressing the image size, which affects the model’s recognition effect. The accuracy of the model was increased from 81.47% to 85.01% by using the data enhancement method of resize, random resized crop, random horizontal flip and random adjust sharpness. AdaMax optimizer was used to replace Stochastic Gradient Optimization optimizer and the accuracy of DenseNet model has been improved by 3.04 percentage points. The Stem block uses multi-layer small convolution for fast dimensionality reduction and a branch structure to combine convolution and maximum pooling. It improves the ability of the model to extract shallow features at a lower operating cost. The Squeeze and Excitation attention mechanism block and sharpen cosine similarity convolution were introduced in the Denselayer of the Dense Block and the Transition Layer. This method improved the recognition ability of the model to lotus leaf diseases, and verified the effectiveness of sharpen cosine convolution to improve the performance of the model. Transfer learning was performed on the Plantvillage dataset. The accuracy of the improved model is 91.34%, which 9.87 percentage points higher than before improvement and optimization. In order to solve the problem of monitoring diseases and insect pests in lotus fields, the improved model was applied to the identification of lotus field diseases and insect pests in UAV images. The calibration area of lotus leaf was cut and predicted by reasoning, then different masks were generated according to the model prediction results and added to the UAV image to generate a distribution map of lotus field diseases and insect pests. The recognition of lotus field diseases and insect pests in the UAV image was investigated, automatic classification and recognition of leaf spot, viral disease, Spodoptera litura, lotus Sclerotium leaf rot and lotus rhizome rot were realized. It provides a new method for efficient and accurate identification and dynamic monitoring of lotus diseases. It also supplies information supports for variable pesticide application and flight path planning in plant disease prevention and control based on UAV. [ABSTRACT FROM AUTHOR]

In a developing country like India agriculture plays a noteworthy role. Agricultural intervention in the livelihood of rural India indulges by about 58%. Thus, preventing significant loss in quantity and yield of these plants is important and majorly dependent on recognition and classification of diseases those plants might possess. Advanced and developing technologies like Image processing are used to classify such issues using different types of algorithms and techniques. Initially, the leaf of a plant gets affected, when plant develops a particular type of disease. In this project, four consecutive stages are used to discover the type of disease. The four stages consist of pre-processing, segmentation, extraction of features and their classification. To remove the noise we are doing the pre-processing and to part the affected or damages area of the leaf, image segmentation is used. The k-nearest neighbors (KNN) algorithm, which is a guided, supervised and advance machine learning algorithm, is implemented to find solutions for both the problems related to classification and regression. During the terminal stage, user is recommended treatment that might help. Mostly live plants are adversely affected by the diseases. This paper conveys representation of leaf disease detection by using image processing that can identify drawbacks in the said plant by inputting images, based on color, bound and texture to give the brisk and reliable results to the farmer. [ABSTRACT FROM AUTHOR]

Agriculture plays a crucial part in the overall growth of the country. Smart agriculture provides today's farmers a help in decision making. Farmers or professionals typically monitor crops for disease detection and identification by using traditional methods. Many techniques are designed to improve the productivity and quality of the crops, but the disease prediction of the existing techniques leads to loss of productivity and quality. Overcoming these issues this paper aims to develop a system to detect Bacterial Blight and Alternaria using Alexnet algorithm at early seedling stage. The main objective is to detect two major diseases Bacterial Blight and Alternaria using Alexnetalgorithm. The dataset of pomegranate leaf images total 1245 was created which was unavailable for these diseases, 80% of dataset is used for the training part another 20% is used for the testing part. For evaluating the performance of Alexnetalgorithm, the performassnce metrics such as accuracy, precision, recallwas considered. Results showed a high accuracy rate of 97.60% and this developed pomegranate leaf diseases detection system is better than other algorithm in terms of accuracy, loss, recall. Developed system has loss of 0.1(scaled between 0-1) which is very less comparative to other similar models. Finding of this paper isDataset was created which was unavailable and proposed approach have high accuracy than others through which we can detect diseases at very earlyseedling stage. [ABSTRACT FROM AUTHOR]

Background: Groundnut (Arachis hypogaea L.) is the fourth most important oil seed crop in the world. The early and late spots are a foliage disease more common and more destructive of groundnut which cause severe yield losses up to 70% and reduces the quality of the pod and fodder. Recent efforts have targeted on developing resistant source against leaf spot disease which are helpful for discarding the hazardous toxic chemical compounds used for controlling the disease. The current study aimed to identify the sources of resistance to leaf spot from the newly identified genotypes as well as from wild species and to study the way in which these were responding to the to the disease development. Methods: Seventy three groundnut genotypes were screened against late leaf spot disease under artificial epiphytotic conditions in field at AICRP on Groundnut, MPKV, Rahuri during kharif 2015-16. The observations of disease intensity and rate of infection were recorded. Result: Among the Seventy three groundnut genotypes, 10 genotypes showed resistant and 33 genotypes showed the moderately resistant reaction to leaf spot disease. The 25 entries showed the susceptible and 05 genotypes showed the highly susceptible reaction to the leaf spot disease. All the susceptible cultivars belong to A. hypogaea sub. sp. fastigiata var. fastigiata to as Spanish bunch. These 10 resistant genotypes of groundnut including two wild species viz., RHRG 6083, KDG 128, GPBD 4, ICGV 94118, ICGV 4983, ICG 12672, ICGV 13160 (A. batizocoi), ICGV 13165 (A. cardenasi), ICG 11426 and ICGV8193 were selected for intensive artificial screening under glasshouse conditions and also confirmed resistance. The resistant lines will be useful to develop introgression lines using marker-assisted backcrossing approach to improve foliar fungal disease resistance in popular groundnut varieties. [ABSTRACT FROM AUTHOR]

In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method based on Convolutional Neural Network (CNN) is proposed for automatic pumpkin leaf image classification. The Proposed Denoising and deep Convolutional Neural Network (CNN) method enhances the Pumpkin Leaf Preprocessing and diagnosis. Real time data base was used for training and testing of the proposed work. Investigation on existing pre-trained network Alexnet and googlenet was investigated is done to evaluate the performance of the proposed method. The system and computer simulations were performed using Matlab tool. [ABSTRACT FROM AUTHOR]

This article, published in the journal Molecular Horticulture, discusses the development of a molecular marker for cherry crinkle leaf disease (CCL). CCL is a genetic disease that affects sweet cherry varieties and can significantly impact the yield of cherry orchards. The researchers used a population of sweet cherry hybrids to identify a molecular marker, called Prc123, that is correlated with the presence of CCL. They also tested the genotypes of Prc123 in various sweet cherry cultivars and found that the predicted occurrence of CCL based on the marker was consistent with the actual occurrence. This marker can be used to prevent the spread of CCL and assist in breeding non-CCL cultivars. [Extracted from the article]

In agricultural applications, the most essential task is to classify leaf diseases and their associated pests from various aspects. To achieve this, a Deep Convolutional Neural Network (DCNN) model was developed to classify the leaf diseases based on the soil and climatic features. But it needs a recommendation system to control the pesticide use for controlling the leaf diseases caused by specific pests. Hence, this paper hybridizes the Multi-dimensional Feature Learning-based DCNN (MFL-DCNN) with the Rough Set (RS) on an intuitionistic Fuzzy approximation space (RSF)-based decision support system to suggest the proper pesticides for a certain crop to be planted in a particular region. First, the leaf images are augmented by the Positional-aware Dual-Attention and Topology-Fusion with Evolutionary Generative Adversarial Network (PDATFEGAN) model. Then, the multi-dimensional data such as the created leaf images, pest, soil, weather, and pesticide data are fed to the DCNN with a softmax classifier for classifying leaf diseases and related pests. Then, the RSF-based decision model is applied, which determines the correlation between leaf disease and pests to recommend suitable pesticides. Finally, the experimental results reveal that the MFL-DCNN-RSF accomplishes a maximum efficiency than all other models for recommending pesticides to control leaf diseases and pests. [ABSTRACT FROM AUTHOR]

Diseases and insect pests of tea leaves cause huge economic losses to the tea industry every year, so the accurate identification of them is significant. Convolutional neural networks (CNNs) can automatically extract features from images of tea leaves suffering from insect and disease infestation. However, photographs of tea tree leaves taken in a natural environment have problems such as leaf shading, illumination, and small-sized objects. Affected by these problems, traditional CNNs cannot have a satisfactory recognition performance. To address this challenge, we propose YOLO-Tea, an improved model based on You Only Look Once version 5 (YOLOv5). Firstly, we integrated self-attention and convolution (ACmix), and convolutional block attention module (CBAM) to YOLOv5 to allow our proposed model to better focus on tea tree leaf diseases and insect pests. Secondly, to enhance the feature extraction capability of our model, we replaced the spatial pyramid pooling fast (SPPF) module in the original YOLOv5 with the receptive field block (RFB) module. Finally, we reduced the resource consumption of our model by incorporating a global context network (GCNet). This is essential especially when the model operates on resource-constrained edge devices. When compared to YOLOv5s, our proposed YOLO-Tea improved by 0.3%–15.0% over all test data. YOLO-Tea's A P 0.5 , A P T L B , and A P G M B outperformed Faster R-CNN and SSD by 5.5%, 1.8%, 7.0% and 7.7%, 7.8%, 5.2%. YOLO-Tea has shown its promising potential to be applied in real-world tree disease detection systems. [ABSTRACT FROM AUTHOR]

Objective. To isolate and identify the causal agent of leaf spot (LS) in Cocos nucifera L. var. Brazilian green dwarf (BGD) and to evaluate the in vitro effect of biorational products in inhibiting the growth of the phytopathogen. Materials and methods. The causal agent was isolated from leaves with LS symptoms and identified morphologically. Five biorational products (Jaque Mate®, Fullkover®, Hongo cero®, Sigatoxx®, and Timorex Gold®) were evaluated in vitro at doses of 2.0, 1.0, 0.5, and 0.25% v/v added to the PDA culture medium. In the center of Petri dishes, 5 µL of a conidiospore solution (9.2×104 conidiospores/mL) of the phytopathogen was inoculated. Colony diameter (CD) was measured, the percentage of mycelial growth inhibition (MGI) was calculated, and the LD50 and LD90 were obtained. Data were analyzed by analysis of variance and Tukey multiple comparisons of means (P=0.05). Results. Bipolaris sp. was diagnosed as the causal agent associated with LS in C. nucifera. Jaque Mate® at all doses, Sigatoxx ® at 2.0 and 1.0%, and Timorex Gold® at 2.0% prevented the growth of Bipolaris sp. with 100% MGI. The lowest LD50 was for Sigatoxx ® (3.67%). Conclusion. Jaque Mate®, Sigatoxx®, and Timorex Gold® were the most effective extracts in inhibiting the growth of Bipolaris sp. isolated from C. nucifera BGD. [ABSTRACT FROM AUTHOR]

Leaf diseases may harm plants in different ways, often causing reduced productivity and, at times, lethal consequences. Detecting such diseases in a timely manner can help plant owners take effective remedial measures. Deficiencies of vital elements such as nitrogen, microbial infections and other similar disorders can often have visible effects, such as the yellowing of leaves in Catharanthus roseus (bright eyes) and scorched leaves in Fragaria×ananassa (strawberry) plants. In this work, we explore approaches to use computer vision techniques to help plant owners identify such leaf disorders in their plants automatically and conveniently. This research designs three machine learning systems, namely a vanilla CNN model, a CNN-SVM hybrid model, and a MobileNetV2-based transfer learning model that detect yellowed and scorched leaves in Catharanthus roseus and strawberry plants, respectively, using images captured by mobile phones. In our experiments, the models yield a very promising accuracy on a dataset having around 4000 images. Of the three models, the transfer learning-based one demonstrates the highest accuracy (97.35% on test set) in our experiments. Furthermore, an Android application is developed that uses this model to allow end-users to conveniently monitor the condition of their plants in real time. [ABSTRACT FROM AUTHOR]

In India about 70% of population rely on yield from agriculture. The plants, leaves have been affected by the disease caused by insects that transfers the infection to other plants in agriculture. During the infection of these diseases the production has been decreased in the farm. Therefore it is necessary to identify these infections at the earliest. The disease detection in banana has turned out be more perplexing in the farm. The banana plant disease detection through image processing becomes more efficient also it is highly essential for farmers in evaluating the plant growth without any manual support economically. This paper proposed leaf disease detection by feature extraction using GLCM and Deep convoluted neural network (DCNN) based classification. The dataset has been collected based on pre-historic dataset of cultivation field and this data has been processed. The features have been extracted using GLCM and the image has been classified using DCNN. Then finally the disease affected area has been identified by extracting features and this feature has been classified for enhancing the accuracy of detecting the disease so we use GLCM-DCNN. Then the simulation results shows the accuracy, recall, precision and f-1 score as the parameters of proposed method. This technique will detect the disease early and helps the farmer to induce pesticides to avoid spreading of disease. [ABSTRACT FROM AUTHOR]

Barley (Hordeum vulgare L.) is one of the most important cereal crops that their grains are used in the malt and forage industry. Yield and quality losses occur in barley due to abiotic and biotic stress factors. Barley leaf diseases caused by fungal pathogens are common diseases leading to significant yield losses. Within the framework of environmentally-friendly integrated crop management, disease-resistant varieties should be developed together with a breeding program for sustainable agriculture. In disease resistance studies, the first and the most important step is to monitor the situation of the disease agent(s) in a region or country. For this purpose; In 2019-2020, barley leaf diseases survey in 54 barley fields was done in 5 provinces of Central Anatolia (Ankara, Eskişehir, Kırıkkale, Kırşehir, Yozgat). As a result of the surveys, the most common disease was determined as barley stripe disease (Drechslera gramínea). Barley net spot disease (D. teres f. maculata, D. teres f. teres), Barley leaf spot (Rhynchosporium commune) and Barley powdery mildew (E. graminis f. sp. hordei) diseases followed in this order. [ABSTRACT FROM AUTHOR]

Bacterial Speck Disease caused by Pseudomonas syringae pv. tomato is an important tomato bacterial disease that causes yield losses. In recent years, the disease has been a problem in tomato growing areas such as greenhouses and open fields and also commercial nurseries. Seed treatment is an important disease management strategy in the control of the disease, and seed treatments using by antagonistic microorganisms are an effective and environmentally friendly disease control method. In the research, biological control possibilities of the disease were investigated with antagonistic bacterial microorganisms isolated from tomato fields. In order to isolate the antagonists, a total of 36 soil and root samples were collected from the tomato plant growing areas in Erdemli and Toroslar districts of Mersin province. A total of 323 candidate antagonist bacterial strains were isolated from these samples in their isolations according to bacteriological techniques. The antimicrobial effects of these strains were investigated by in vitro petri dishes, and 12 antagonistic strains formed an inhibition zone between 2.3-11.0 mm and suppressed the growth of the pathogen. The strains reduced the disease incidence by 33-95% ratios and disease severity by 28-95% ratios when the twelve strains, which were successful in in vitro tests, were applied to tomato seeds as biological seed treatments. Applications did not have a negative effect on seed germination. In the second biological seed treatment experiment with the four most successful antagonistic bacterial strains, the disease was suppressed by 81-100% ratios. Thus, the effect of successful antagonists has been proven for the second time. The MALDI-TOF MS technique was used in the identification of the successful antagonistic strains, it was determined that the antagonists were identified as Bacillus subtilis, Bacillus pumilus and Paenobacillus polymyxa. It has been shown in the study that Bacillus species with antagonist properties can be used successfully as biological seed treatments in the control of Bacterial Speck Disease on tomatoes. It should be investigated in the further research that the effect of the antagonistic Bacillus strains on the inhibition of the disease on foliage parts of tomato, their mechanism of action and the effects on other antagonists, plant pathogens. There is also a need for detailed studies such as mass production, obtaining licenses and patents for its practical use. It was concluded that the antagonistic strains can be used in part of the integrated disease management programs after the additional experiments are performed. [ABSTRACT FROM AUTHOR]

Parthenocissus semicordata is an excellent vine plant for vertical greening of walls or buildings. A leaf spot disease has been investigated on P. semicordata in the Matoushan National Nature Reserve in Fuzhou city, Jiangxi province, China, since August 2021. The disease incidence was approximately 60–65%, which led to affecting its growth and ornamental value. The pathogen was identified as Colletotrichum siamense based on morphological characteristics and phylogenetic analysis. Pathogenicity tests were conducted by inoculating healthy leaves with spore suspension of C. siamense , and Koch's postulates were confirmed by re-isolation and identification of the pathogen from infected leaves. This is the first known occurrence of C. siamense causing leaf spot on P. semicordata in China. These findings play a crucial role in increasing the knowledge of P. semicordata diseases in China and provide pivotal information for epidemiological studies and control strategies. • New pathogen of Parthenocissus semicordata foliage disease. • New host of Colletotrichum siamense. • Multi-locus concatenated phylogenetic analysis confirmed the pathogen [ABSTRACT FROM AUTHOR]

In the agricultural industry, rice infections have resulted in significant productivity and economic losses. The infections must be recognized early on to regulate and mitigate the effects of the attacks. Early diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses, reduce pesticide use, and boost ta country's economy. Assessing the health of a rice plant through its leaves is usually done as a manual ocular exercise. In this manuscript, three rice plant diseases: Bacterial leaf blight, Brown spot, and Leaf smut, were identified using the Alexnet Model. Our research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply, lowering prices, and reducing production's environmental impact that affects the economy of any country. Farmers would be able to get more exact and faster results with this technology, allowing them to administer the most acceptable treatment available. By Using Alex Net, the proposed approach achieved a 99.0% accuracy rate for diagnosing rice leaves disease. [ABSTRACT FROM AUTHOR]