Plant Disease Diagnosis Research Articles

Processing Plant Diseases Using Transformer Model

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.

A plant disease classification using one-shot learning technique with field images

Early diagnosis of plant diseases is crucial for preventing plagues and mitigating their effects on crops. The most precise automatic methods for identifying plant diseases using images of plant fields are powered by deep learning. Big image datasets should always be gathered and annotated for these methods to work, which is often not technically or financially feasible. This paper offers one-shot learning (OSL) techniques for plant disease classification with limited datasets utilizing Siamese Neural Network (SNN). There are five different crop kinds in the dataset: grape, wheat, cotton, cucumber, and corn. Five sets of images showing both healthy and diseased crops are used to represent each of the new crops. The dataset's includes 25 classes with 875 leaf images. Data augmentation techniques are used to enhance the size and dimension of the plant leaf disease image dataset. To provide effective segmentation, this paper provides a unique method for region-based image segmentation that divides an image into its most prominent regions. It also addresses issues with earlier region-based segmentation methods. SVM-based classifiers have better generalization properties as their efficiency does not depend on the number of features. Such merit is beneficial in primary diagnostics decisions to check if the input image is included in the database or not to reduce the consumed time. OSL was applied and compared to standard fine-tuning transfer learning utilizing Siamese networks and triplet loss. Siamese provides superior classification accuracy and localization accuracy with minimal errors than other approaches. The proposed approach has a total processing time of 5 ms, which makes it appropriate for real-time applications. In terms of specificity, sensitivity, precision, accuracy, MCC, and F-measure, the proposed approach beats all current machine learning algorithms for small training sets.

A System for Identifying Deficiency Elements in Apple Leaf Disease Based on Deep Learning

Background: Plant diseases and pests were natural disasters that seriously threaten agricultural production. Leaf diseases posed a significant threat to the overall productivity and quality of apple orchards. Currently, the diagnosis of plant diseases in apple orchards mainly relied on manual labor, which was both time-consuming and costly. It took a considerable amount of time to train an expert capable of accurately identifying apple diseases and pests, and different crop diseases vary. However, with the use of AI technology, training could be completed in just a few hours to a day, transforming computers into experts in disease and pest identification. Although China became a major apple cultivation nation, the development data from 2021 revealed that traditional advantageous production areas were undergoing dynamic adjustments due to optimization of industrial structure, leading to a decrease in cultivated land. Conversely, the demand for apples in the Chinese market continued to rise, even as the country's population structure gradually trended towards an aging population. This situation underscored the urgent need to replace the laborious and high-cost task of identifying apple foliar diseases in the cultivation process with intelligent agricultural technological solutions. Taking into account the aforementioned factors of the natural environment and socio-cultural considerations and addressing the issue of reduced apple yields due to the high impact of foliar diseases, we initiated study on disease prevention and control during the cultivation process. Methods: To overcome the objective impact of environmental factors, the system extracted images and performed operations such as enhancement and segmentation. Subsequently, a convolutional neural network was employed to process the enhanced images, extracting features such as color, shape, and texture of apple foliar lesions, which were then summarized into histograms using mathematical methods. These histograms served as the primary reference for distinguishing various diseases. Python was chosen as the programming language for the system, and the recognition system was operated by integrating frontend technologies, backend database design, and other relevant techniques. Results: Based on a substantial number of experimental results, the apple leaf disease and nutrient deficiency recognition system could accurately and with extremely high precision identifying major diseases commonly occurring in apple plants, including apple scab, leaf spot disease, rust disease, gray mold disease, and brown spot disease. Moreover, the system was capable of proposing remedies tailored to the specific pain points of each disease. It not only detected apple leaf diseases but also performs nutrient deficiency detection. This holds significant importance in enhancing both the yield and quality of apples. Conclusion: For the commonly occurring foliar diseases in cultivation, the system exhibited the capability to autonomously recognize and provide decision feedback. In contrast to the less efficient and more costly traditional approach of consulting experts, the more accessible apple foliar disease recognition system was poised to replace it as a primary tool for prevention and control assistance. This system will serve as a guiding companion, safeguarding the robust growth of apple plants.

Sustainable Coffee Leaf Diagnosis: A Deep Knowledgeable Meta-Learning Approach

Multi-task visual recognition plays a pivotal role in addressing the composite challenges encountered during the monitoring of crop health, pest infestations, and disease outbreaks in precision agriculture. Machine learning approaches have been revolutionizing the diagnosis of plant disease in recent years; however, they require a large amount of training data and suffer from limited generalizability for unseen data. This work introduces a novel knowledgeable meta-learning framework for the few-shot multi-task diagnosis of biotic stress in coffee leaves. A mixed vision transformer (MVT) learner is presented to generate mixed contextual attention maps from discriminatory latent representations between support and query images to give more emphasis to the biotic stress lesions in coffee leaves. Then, a knowledge distillation strategy is introduced to avoid disastrous forgetting phenomena during inner-loop training. An adaptive meta-training rule is designed to automatically update the parameters of the meta-learner according to the current task. The competitive results from exhaustive experimentations on public datasets demonstrate the superior performance of our approach over the traditional methods. This is not only restricted to enhancing the accuracy and efficiency of coffee leaf disease diagnosis but also contributes to reducing the environmental footprint through optimizing resource utilization and minimizing the need for chemical treatments, hence aligning with broader sustainability goals in agriculture.

Denoising Diffusion Probabilistic Models and Transfer Learning for citrus disease diagnosis.

Plant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models. This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample. Two training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic dataset produced by DDPM, followed by fine-tuning on the original citrus leaf images for disease classification through transfer learning. The Method 2 utilizes the pre-trained Swin Transformer model on the ImageNet dataset and fine-tunes it on the augmented dataset composed of the original and DDPM synthetic images. The test results indicate that Method 1 achieved a validation accuracy of 96.3%, while Method 2 achieved a validation accuracy of 99.8%. Both methods effectively addressed the issue of model overfitting when dealing with a small dataset. Additionally, when compared with VGG16, EfficientNet, ShuffleNet, MobileNetV2, and DenseNet121 in citrus disease classification, the experimental results demonstrate the superiority of the proposed methods over existing approaches to a certain extent.

Evolutionary meta-heuristic optimized model: An application to plant disease diagnosis

Deep learning strategies have been achieved over the historical decades to resolve many computer vision applications. Recently, these deep learning algorithms have been extensively used as a tool in classification problems. Generally, the deep learning algorithms trained with gradient-based optimizers, which has some downsides such as the slow speed of convergence and stuck in local minima. As a solution, the planned work using meta-heuristic based Grey Wolf and Whales optimization algorithms for the automatic plant disease detection model. The planned work has explored the application of automatic plant disease identification through the leaf images with the help of the image processing approach. The planned research has evaluated the deep learning algorithm with Grey Wolf and Whales optimization techniques using the three types of datasets, such as Plant Village, New Plant Disease, and Rice Leaf Disease databases. The simulation consequences illustrate that the computational efficiency of the Grey Wolf and Whales based automatic disease identification process is boosted when coupled with the deep learning method.

Automated Plant Disease Diagnosis Using Deep Learning Model

With the world's population growing, it is vital that food crops and medicinal plants are required to be developed in large quantity. A model that can help us understand the process more comprehensively will be explored. Based on a thorough understanding, we are exploring a model that will enable us to extract the precise information and in-depth knowledge we require. There are many plants that contract lethal diseases every year, including food plants. This reduces their output rates. We must employ automated methods to locate the issue inside the facility if we do not want manufacturing prices to rise. The detection of plant diseases can be automated through robotics and image processing. As a result of recent technological advancements, we now have the ability to simplify our artwork. A deep learning and image processing approach can increase the efficiency of detection processes. A great deal of progress has been made in diagnosing plant diseases. An artificial intelligence model was trained to assist in diagnosing flora disorders. Based on the photo information (leaf), the model can be controlled. Identifying the problem and discovering the condition in this study, we propose the use of computer vision technology combined with fuzzy logic to detect and grade leaf diseases. GLCM is performed to extract texture features, and fuzzy logic is applied to grade the disease. K-means clustering is applied for determining defected areas; GLCM is used to determine defected areas; and fuzzy logic is used for diagnosing diseases. About 70% of classifications are accurate according to the model. By using Speed Up Robust Features (SURF), DENSE, and Bag of Visual Words (BOVW) in addition to the global features, the accuracy of the system can be enhanced. A treatment program will help people better understand the illness if one is offered.

“Advanced Deep Learning Techniques for Plant Disease Diagnosis and Treatment”

Abstract: The pivotal role of agriculture in shaping people’s lives and economies is evident, with a substantial contribution to GSP and employment. The prevalence of diseases poses a major challenge. Novel system is introduced for the early detection of leaf diseases using Deep Learning and Tensorflow technology. The model analyses pictures from plant village, focusing on bell pepper, tomato and potato plants. Climate change soil erosion and difficulty in detecting pathogens underscore the need for early disease recognition to minimize pesticide use. The system demonstrates a remarkable 95.80% correctness in disease identification, benefiting cultivators by recommending appropriate measures in a separate context. The recognition of medicinal plant gains importance for various stakeholders. A fully automated approach combining computer vision and machine learning achieves an accuracy of 90.1% in detecting 24 different medicinal plant species, outperforming other methods.

Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM

IntroductionAt present, machine learning and image processing technology are widely used in plant disease diagnosis. In order to address the challenges of subjectivity, cost, and timeliness associated with traditional methods of diagnosing potassium deficiency in apple tree leaves. MethodsThe study proposes a model that utilizes image processing technology and machine learning techniques to enhance the accuracy of detection during each growth period. Leaf images were collected at different growth stages and processed through denoising and segmentation. Color and shape features of the leaves were extracted and a multiple regression analysis model was used to screen for key features. Linear discriminant analysis was then employed to optimize the data and obtain the optimal shape and color feature factors of apple tree leaves during each growth period. Various machine-learning methods, including SVM, DT, and KNN, were used for the diagnosis of potassium deficiency. ResultsThe MLR-LDA-SVM model was found to be the optimal model based on comprehensive evaluation indicators. Field experiments were conducted to verify the accuracy of the diagnostic model, achieving high diagnostic accuracy during different growth periods. DiscussionThe model can accurately diagnose whether potassium deficiency exists in apple tree leaves during each growth period. This provides theoretical guidance for intelligent and precise water and fertilizer management in orchards.

Correction: A novel plant disease diagnosis framework by integrating semi-supervised and ensemble learning

Correction: A novel plant disease diagnosis framework by integrating semi-supervised and ensemble learning

Identifying Plant Pests and Diseases with Artificial Intelligence: A Short Comment

Advances in artificial intelligence (AI) have the potential to transform the way we identify and monitor plant pests and diseases. However, there are several challenges that need to be addressed, including the need for large, high-quality datasets, the requirement for expertise in AI and data science, and the development of more sophisticated algorithms and methods. As these challenges are addressed, there is likely to be an increasing focus on the application of AI in the agricultural sector, including plant disease diagnosis.

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.

Corrigendum: mPD-APP: a mobile-enabled plant diseases diagnosis application using convolutional neural network toward the attainment of a food secure world

Corrigendum: mPD-APP: a mobile-enabled plant diseases diagnosis application using convolutional neural network toward the attainment of a food secure world

Ensemble of CNN models for classification of groundnut plant leaf disease detection

Plant diseases pose a significant threat to the world's nutrition and can have severe consequences for smallholder farmers who rely on a thriving groundnut crop for their livelihoods. Therefore, it is crucial to create an algorithm for early automated diagnosis of plant diseases. Soa comprehensive dataset of groundnut leaf images was created in collaboration with a pathologist, facilitating the automated identification of plant diseases. The field of image classification has found CNN to be quite effective. In this study, we create such a method for disease identification and classification by utilizing a tri-CNN architecture consisting of DenseNet169, Inception, and Xception —that have been pre-trained on the ImageNet dataset using two created non-linear equations on decision scores from the before mentioned base learners,the outlined ensemble methodology generates ultimate predictions for the test samples by incorporating the scores of four conventional metrics for evaluation, namely recall, precision, accuracy, and f1-score, obtained from the base learners. Hence mentioned CNN customized models are used to train our model for obtaining better results. The proposed approach evaluated on real world groundnut leaf dataset. The model that was put forth attained accuracy rates of 98.46 %.

MULTI-LEAF DISEASE DETECTION AND RECOMMENDS PESTICIDE USING DEEP LEARNING

This research describes a proposed system that uses deep learning and image processing methods to identify and categorize plant leaf diseases. The approach that is suggested comprises of two categorization methods and compares them. The first approach comprised of multiple steps leading up to the classification stage and was based on the support vector machine (SVM) algorithm. Because they are the most prevalent plant species worldwide and in Iraq specifically, tomatoes, peppers, and potatoes are the specific plant species that we use in our work. Convolution neural networks (CNNs) were employed in the second approach for classification. Fifteen classes were identified using these two methods: three classes for healthy leaves and twelve classes for illnesses of various plants that were found, such as fungi, bacteria, etc. The comparisons outcome demonstrates that the CNN algorithm is preferred over the SVM algorithm in terms of speed and accuracy, creating a system that is reliable and accurate for the identification and categorization of plant leaf diseases. The effectiveness of the system is evaluated using evaluation measures such as accuracy, precision, recall, and F1-score. Additionally discussed are the moral and environmental aspects of using pesticides. This strategys practical use is illustrated by case studies and real-world examples, which also highlight how it may be used to increase agricultural yields, lower resource consumption, and support sustainable farming methods. Finally, this study highlights how CNNs can revolutionize both pesticide recommendation and the detection of multi-leaf diseases. The integration of advanced technology with agricultural methods in this study promotes sustainable farming practices and global food security. KEYWORDS: Deep Learning, Convolutional Neural Networks (CNNs), Multi-Leaf Disease Detection, Pesticide Recommendation, Agriculture, Plant Disease Diagnosis, Image Analysis, Data Augmentation, Transfer Learning.

A Machine Learning Approach to Pomegranate Leaf Disease Identification

Pomegranate is a fruit with the highest yield and the greatest geographic distribution in Asia. On the other hand, the plants are susceptible to a diverse range of illnesses as a consequence of a number of factors, which leads to the total destruction of the plants and a harvest that is significantly reduced. In order to avoid reductions in agricultural output, it is necessary to diagnose plant diseases as quickly as is practically possible. It is a difficult and time-consuming task to manually monitor the progress of diseases on pomegranate leaves. Therefore, Deep Learning is utilised in the diagnosis of diseases affecting pomegranate trees (DL). The goal of this study is to develop an algorithm for automatically diagnosing diseases affecting pomegranate plants based on images of the plant's leaf structures. The process of a disease detection system includes the gathering of data, the analysis of that data, the categorization of gathered images, and their subsequent deployment. The Mendeley database is utilised in order to generate images of pomegranate leaves in both healthy and diseased states. After that, the original, unaltered raw image is polished. Two different DL models, AlexNet and VGG-16, are put to use in this classification technique. To determine the optimal model, it is necessary to do accurate and loss-oriented measurements. According to the measurements, AlexNet does a good job of recognising diseases that affect leaf tissue. Later, an AlexNet-based smartphone app is developed to assist farmers in performing disease detection on pomegranates without the assistance of professionals. This software is intended to help farmers save time and money.

Heterogeneous domain adaptation method for tomato leaf disease classification base on CycleGAN

Despite the significant improvements in the detection and diagnosis of plant diseases at an early stage facilitated by deep learning technology, there are challenges associated with the generalization performance of deep learning models. These problems from the differences between in-field and in-lab data, as well as the heterogeneity of training and prediction data features. In the case of tomato leaf diseases, the PlantVillage dataset is widely used and has already demonstrated accuracy of more than 99%. However, using trained model based on this dataset to predict in-field data results in low accuracy due to domain differences and heterogeneous features. In this paper, we propose a domain adaptation method based on CycleGAN to solve this problem, followed by a preprocessing technique that utilizes both the OpenCV module and a segmentation model based on U-Net for the best generalization performance. The classification accuracy is evaluated by applying the DenseNet121 model trained on the PlantVillage dataset to the images generated by CycleGAN. Our results demonstrate, with an F1-score of 95.6%, that our domain adaptation method between the two domains is effective in mitigating the effect of domain shift.

Towards robust plant disease diagnosis with hard-sample re-mining strategy

With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based systems (e.g., EfficientNet-based), such as the ability to detect disease locations and superior classification performance. One drawback of these detection systems is dealing with unannotated healthy data with no real symptoms present. In practice, healthy plant data appear to be very similar to many disease data. Thus, those models often produce mis-detected boxes on healthy images. In addition, labeling new data for detection models is typically time-consuming. Hard-sample mining (HSM) is a common technique for re-training a model by using the mis-detected boxes as new training samples. However, blindly selecting an arbitrary amount of hard-sample for re-training will result in the degradation of diagnostic performance for other diseases due to the high similarity between disease and healthy data. In this paper, we propose a simple but effective training strategy called hard-sample re-mining (HSReM), which is designed to enhance the diagnostic performance of healthy data and simultaneously improve the performance of disease data by strategically selecting hard-sample training images at an appropriate level. Experiments based on two practical in-field eight-class cucumber and ten-class tomato datasets (42.7K and 35.6K images) show that our HSReM training strategy leads to a substantial improvement in the overall diagnostic performance on large-scale unseen data. Specifically, the object detection model trained using the HSReM strategy not only achieved superior results as compared to the classification-based state-of-the-art EfficientNetV2-Large model and the original object detection model, but also outperformed the model using the HSM strategy in multiple evaluation metrics.

Mobile app-based tomato disease identification with fine-tuned convolutional neural networks

Rapid detection and diagnosis of plant diseases is crucial for healthy crop growth and maintaining harvest quality. Many diseases, such as bacterial spot, late blight, and leaf mould, can affect tomato cultivation. In this paper, a mobile app-based system for intelligent disease detection on tomato leaves is developed using convolutional neural networks (CNNs). The study uses transfer learning approaches to fine-tune well-known CNN architectures like AlexNet, ResNet-50, SqueezeNet-1.1, VGG19, and DenseNet-121 on a dataset made up of images of tomato leaf from the PlantVillage dataset as well as self-captured images. Over 95% accuracy is attained by all trained CNN models, with DenseNet-121 leading the pack with a stellar 99.85%. Consequently, DenseNet-121 is proposed for use with a user-friendly mobile application to detect disease in tomato leaves. A robust user interface in English and two important Indian languages makes this application more accessible to a wider user base.

Smart Plant Disease Analysis and Management System Using Ai and Iot

Plant diseases are one of the major causes of crop yield reduction worldwide. Early detection and effective management of plant diseases will ensure a sustainable food supply and reduce famine. In this project, we propose a Plant Disease Management System (PDMS) that uses Artificial Intelligence (AI) and Internet of Things (IoT) technologies to diagnose and manage plant diseases. The proposed system consists of two main components: a plant disease diagnosis module and a disease management module. The plant disease diagnosis module uses AI algorithms to analyze images of plant leaves and identify the type of disease affecting the plant. The AI model is trained on a large dataset of plant images using deep learning techniques, such as Convolution Neural Networks (CNNs) and Transfer Learning.