Classification Models for Plant Diseases Diagnosis: A Review

Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved

A Detailed Review on Plant Leaf Disease Detection and Classification Methodologies using Deep Learning Techniques

The rapid emergence and evolution of deep learning methodologies in the field of plant disease classification and detection has resulted in significant progress. Their application has revolutionized the way agriculture is done. This paper provides an overview of the advancements in utilizing deep learning models to address the crucial task of identifying and categorizing plant diseases. By harnessing the power of deep convolutional neural networks (CNNs) and transfer learning, researchers have achieved remarkable accuracy in disease classification, often surpassing traditional methods. This study also delves into the challenges that persist in this field, such as the scarcity of labeled data and potential biases in models. To address these concerns, the integration of visualization techniques is explored, allowing for better model interpretation and transparency. The collaborative efforts of agricultural experts and machine learning researchers are deemed crucial for overcoming these challenges and driving the future direction of research. Looking ahead, the interdisciplinary approach is anticipated to play a pivotal role in refining deep learning models for plant disease detection. A seamless collaboration between domain-specific professionals, machine learning experts, and agricultural practitioners is essential to foster innovation, enhance the reliability of models, and create a sustainable agricultural ecosystem. With the integration of cutting-edge architectures, emerging technologies like edge computing, and broader datasets, the field is poised to bring about transformative changes in agricultural practices, bolstering crop health and productivity

A Survey on the State of Art Approaches for Disease Detection in Plants

Agriculture is the main factor for economy and contributes to GDP. The growth of the economy of many countries is based on agriculture. As a result, the yield factor, quality and volume of agricultural products, play a critical role in economic development. Plant diseases and pests have become a major determinant of crop yields throughout the years, as such illnesses in plants offer a serious threat and impediment to higher yields or production in the agriculture industry. As a result, From the outset, it becomes the major duty to correctly monitor the plants, to detect diseases thoroughly, and to determine methods of controlling or monitoring these plant diseases pests in order to achieve a higher rate of production growth and minimal crop damage. Using machine vision, deep learning methods and tools for extracting and classifying features, It could be possible to build a reliable disease detection system. Numerous researchers have created and deployed various ways for detecting plant diseases and pests. The potential of these methods has been examined in this work

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

Machine and Deep Learning Approaches for Plant Disease Detection: A Comprehensive Review

People have been using edible foods since ancient times, and they continue to be an essential component of a healthy diet and traditional food systems today. Food crops as a major source of human energy intake, and the challenges they face due to biotic and abiotic stress factors, such as pollution, insects, bacteria, and unfavourable weather conditions. Detecting plant diseases in the early stage is critical for ensuring a stable supply of healthy food, and traditional methods of disease detection by experts are lengthy and have some limitations. The use of Machine and Deep learning is a key aspect of precision farming for crop growth monitoring. Plenty ML strategies, including random forest and support vector machines (SVMs), Convolutional Neural Networks, Deep learning as well as image processing have been used to precisely detect, classify, and predict plant diseases. By leveraging machine learning algorithms, farmers and agricultural experts can accurately detect and diagnose crop diseases, enabling them to take appropriate measures to control and prevent further spread of the disease.  This article provides a comprehensive overview of the different AI approaches for plant disease identification and control, drawing on a range of research articles in the field. The application of machine learning in agriculture holds promise for improving crop health and increasing yields and represents an important area of innovation for sustainable agriculture in the future

Numerical Modeling and Design of Machine Learning Based Paddy Leaf Disease Detection System for Agricultural Applications

In order to satisfy the insatiable need for ever more bountiful harvests on the global market, the majority of countries deploy cutting-edge technologies to increase agricultural output. Only the most cutting-edge technologies can ensure an appropriate pace of food production. Abiotic stress factors that can affect plants at any stage of development include insects, diseases, drought, nutrient deficiencies, and weeds. On the amount and quality of agricultural production, this has a minimal effect. Identification of plant diseases is therefore essential but challenging and complicated. Paddy leaves must thus be closely watched in order to assess their health and look for disease symptoms. The productivity and production of the post-harvest period are significantly impacted by these illnesses. To gauge the severity of plant disease in the past, only visual examination (bare eye observation) methods have been employed. The skill of the analyst doing this analysis is essential to the caliber of the outcomes. Due to the large growing area and need for ongoing human monitoring, visual crop inspection takes a long time. Therefore, a system is required to replace human inspection. In order to identify the kind and severity of plant disease, image processing techniques are used in agriculture. This dissertation goes into great length regarding the many ailments that may be detected in rice fields using image processing. Identification and classification of the four rice plant diseases bacterial blight, sheath rot, blast, and brown spot are important to enhance yield. The other communicable diseases, such as stem rot, leaf scald, red stripe, and false smut, are not discussed in this paper. Despite the increased accuracy they offer, the categorization and optimization strategies utilized in this work lead it to take longer than typical to finish. It was evident that employing SVM techniques enabled superior performance results, but at a cost of substantial effort. K-means clustering is used in this paper segmentation process, which makes figuring out the cluster size, or K-value, more challenging. This clustering method operates best when used with images that are comparable in size and brightness. However, when the images have complicated sizes and intensity values, clustering is not particularly effective

Resnet-Based Approach For Detection And Classification Of Plant Leaf Diseases

Plant diseases may cause large yield losses, endangering both the stability of the economy and the supply of food. Convolutional Neural Networks (CNNs), in particular, are deep neural networks that have shown remarkable effectiveness in completing image categorization tasks, often outperforming human ability. It has numerous applications in voice processing, picture and video processing, and natural language processing (NLP). It has also grown into a centre for research on plant protection in agriculture, including the assessment of pest ranges and the diagnosis of plant diseases. In two plant phenotyping tasks, the function of a CNN (Convolutional Neural Networks) structure based on Residual Networks (ResNet) is investigated in this study. The majority of current studies on Species Recognition (SR) and plant infection detection have used balanced datasets for accuracy and experimentation as the evaluation criteria. This study, however, made use of an unbalanced dataset with an uneven number of pictures, organised the data into several test cases and classes, conducted data augmentation to improve accuracy, and—most importantly—used multiclass classifier assessment settings that were helpful for an asymmetric class distribution. Furthermore with all these frequent issues, the paper addresses selecting the size of the data collection, classifier depth, necessary training time, and assessing the efficacy of the classifier when using various test scenarios. The Species Recognising (SR) and Identifying of Health and Infection Leaves (IHIL) tasks in this study have shown substantial improvement in performance for the ResNet 20 (V2) architecture, with Precision of 91.84% & 84.00%, Recall of 91.67% and 83.14%, and F1 scores of 91.49% & 83.19%, respectively. &nbsp