Whale Optimized Deep Model for Paddy and Maize Leaf Disease Detection

Agriculture field has a high impact on our life. Agriculture is the most important sector of our Economy. Proper management leads to a profit in agricultural products. Farmers do not expertise in leaf disease, so they produce less production. Plant leaf diseases detection is the important because profit and loss are depending on production. CNN is the solution for leaf disease detection and classification. Main aim of this research is to detect the apple, grape, corn, potato and tomato plants leaf diseases. Plant leaf diseases are monitoring of large fields of crops disease detection, and thus automatically detected some feature of diseases as per that provide medical treatment. Proposed Deep CNN model has been compared with popular transfer learning approach such as VGG16. Plant leaf disease detection has wide range of applications available in various fields such as Biological Research and in Agriculture Institute. Plant leaf disease detection is the one of the required research topics as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. Key Words: Diseases, CNN, Deep Learning

Agricultural productivity depends heavily on the economy. This stands as the main reason for the detection of the plant disease in the field of agriculture, because diseases in plants are quite natural. Crop diseases serve as a major food supply threat. The identification of diseases can lead to more rapid interventions in order to reduce the effects of plant disease. Automatic detection of plant leaf disease is beneficial because of the limited requirement of overviewing in large areas of crops and detects the diseases at an earliest, i.e., when they attack the plant leaves. The performance and accuracy level were quite low in existing system. Modern developments in deep learning have made drastic improvements in accuracy of object recognition. The proposed study aims at classifying the defect in the leaves of different plants using the plant leaf image. This is achieved using Faster Region based convolution neural network. The proposed method has the potential to identify various diseases possible in the plants.

Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks

Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.

This paper outlines a way for identifying plant leaf infections and a method for locating infections with caution. The goal of the proposed research is to use image processing and support vector machines to analyze leaf disease (SVM). The diseases on the leaf are the primary cause of the rapid fall in leaf development. Because 85–95 percent of diseases occur on the leaf, the approach used to identify leaf disease. This research involves K- means bunching, which is the division calculation, as well as the Support Vector Machine. This proposed recognition model based extremely successful in perceiving leaf illnesses. Record Terms k-implies, Leaf illnesses, picture division, characterization, Threshold, picture handling.

A novel hierarchical framework for plant leaf disease detection using residual vision transformer

In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, tomato is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, tomato are a great source of protein that offer many health benefits, but there are a lot of diseases associated with tomato leaf which hinder its production. Thus, an accurate classification of tomato leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify leaf disease by using public dataset of leaf image and CNN model with the open source library TensorFlow. In this project, we proposed a method to classify tomato leaf disease and to find and describe the efficient network architecture (hyper parameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying tomato leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using CNN architecture check if we could get faster training times, higher accuracy and easier retraining. Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this project, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this project will be a valuable resource for researchers who study the detection of plant diseases. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

Maize leaf disease recognition based on improved MSRCR and OSCRNet

Detecting pests and diseases on maize leaves is challenging. This is especially true under complex conditions, such as variable lighting and occlusion. Current methods suffer from low detection accuracy. They also lack sufficient real-time performance. Hence, this study introduces the lightweight detection method YOLOv11-RCDWD based on an improved YOLOv11 model. The proposed approach enhances the YOLOv11 model by incorporating the RepLKNet module as the backbone, which significantly enhances the model’s capacity to capture characteristics of maize leaf pests and diseases. Additionally, the CBAM is embedded within the neck feature extraction network to further refine the feature representation to augment the model’s capability to identify and select essential features by introducing attention mechanisms in both the channel and spatial dimensions, thereby improving the accuracy of feature expression. We have also improved the model by incorporating the DynamicHead module, WIoU loss function, and DynamicATSS label assignment strategy, which collectively enhance detection accuracy, efficiency, and robustness through optimized attention mechanisms, better handling of low-quality samples, and dynamic sample selection during training. The experimental findings indicate that the improved YOLOv11-RCDWD model effectively detected pests and diseases on maize leaves. The precision reached 92.6%, while the recall was 85.4%. The F1 score was 88.9%, and the mAP@0.5 and mAP@0.5~0.95 demonstrated an improvement of 4.9% and 9.0% over the baseline YOLOv11s. Notably, the YOLOv11-RCDWD model significantly outperformed other architectures such as Faster R-CNN, SSD, and various models within the YOLO series, demonstrating superior capabilities in terms of detection speed, parameter count, computational efficiency, and memory utilization. This model achieves an optimal balance between detection performance and resource efficiency. Overall, the improved YOLOv11-RCDWD model significantly reduces detection time and memory usage while maintaining high detection accuracy, supporting the automated detection of maize pests and diseases, and offering a robust solution for intelligent monitoring of agricultural pests.

The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detection system is essential for accelerating crop diagnosis. Using machine learning and image processing, this paper describes a framework for detecting leaf illness. An image of a leaf can be used as an input for this framework. To begin, leaf photographs are preprocessed in order to remove noise from their images. The mean filter is used to filter out background noise. Histogram equalization is used to enhance the quality of the image. The division of a single image into multiple portions or segments is referred to as segmentation in photography. It assists in establishing the boundaries of the image. Segmenting the image is accomplished using the K-Means approach. Feature extraction is carried by using the principal component analysis. Following that, images are categorized using techniques such as RBF-SVM, SVM, random forest, and ID3.

Convolutional neural network (CNN) models have been used extensively in many image recognition tasks for their state-of-the-art performance in recent years. Researchers inspired by this success frequently prefer CNNs in the agricultural field, especially for disease detection and classification. Many CNN models have been proposed for plant leaf diseases and impressive performance results have been obtained. On the other hand, standard CNNs usually need millions of parameters in the network for computation, but it is difficult to implement them on embedded and mobile devices with limited resources. Therefore, it is important to obtain lighter models by decreasing the number of parameters in addition to the high performance of the models. In this paper, a new hybrid CNN approach based on Inception architecture and depthwise separable convolutions is proposed to reduce the number of parameters and computational cost for plant leaf disease detection and classification. Although the number of parameters is significantly reduced, the results show that the proposed approach has high accuracy performance. The proposed hybrid model has been trained and tested with k-fold cross-validation using a dataset of 50,136 images containing 30 classes from 14 different leaves, including healthy and diseased ones. The new model has achieved the best accuracy of 99.27% and an average accuracy of 99%, and provides about a 75% reduction in the number of parameters compared to the standard CNN.

A country's economy depends heavily on agriculture, and there are many varieties of crops that can be cultivated by farmers. The issue or problems arise when the farmers are unaware of a plant disease that is affecting their crops at the appropriate moment, thus harvests become afflicted. Most of the time farmers are not aware about the disease and its type when it is discovered.Given these challenges, research in the field of automatic leaf disease detection in agriculture is of significant importance, since it could offer benefits in the detection of bigger fields of crops and help to detect diseases since they appear on plants leaf. The examination to various pattern present above the plant leaves is necessary for study of plant disease.

The disease identification using machine learning algorithms is a promising approach for improving plant health and crop yield, and has the potential to revolutionize the agricultural industry. Leaf disease identification by machine learning algorithms is a promising field in plant pathology and precision agriculture leading to better crop yields and increased sustainability in agriculture. In Deep learning based EfficientnetB3 model for various plants leaves. Leaf disease identification by machine learning algorithms involves the use of advanced computational method to automatically identify and classify various types of plant diseases based on the symptoms observed on their leaves. The machine learning algorithms in leaf disease identification offers several advantages over traditional methods improved accuracy, speed, and scalability than the manual inspection to reduce the time-consuming and error-prone, especially in large agricultural fields.

Agriculture plays a crucial role in the economic growth of a country as it is one of the main means of subsistence. Recently, technological methods have been designed for the identification of plants and detection of their diseases in order to meet the new challenges facing farmers and their learning needs. This chapter provides an overview of various methods and techniques for feature extraction, segmentation and the classification of patterns of captured leaves in order to identify plant leaf diseases and the estimation of their severity. This chapter analyzes various automatic grading systems and parameters used in estimating the severity of different plant diseases and discusses a variety those plant diseases. The chapter also discusses the use of a radial basis function (RBF) kernel-based support vector machine (SVM) learning algorithm for the detection of these diseases, which include rust; tikka; powdery and downy mildew; late blight and early blight in groundnut, apple, potato and tomato plants. An analysis is made of the factors that stress plants; for example, water, pests and soil in green house plants. Leaf blast, brown spot and sheath rot detected in rice plants are discussed using a cluster validation algorithm. The chapter also deals with the assessment of severity of foliar diseases found in soybean plants (using segmentation), rice blast disease (using an SVM) and single disease severity level of plasmopara viticola in grape leaves (using a k-means clustering algorithm). The existing gaps in the technology for precision agriculture are discussed, and the requirements and demands to be met by agriculturists are presented. The whole impetus of this chapter is to motivate researchers to focus and develop efficient machine learning and classification techniques for leaf identification and disease detection to meet the new challenges in the field of agriculture.

Plant leaf disease detection is a crucial aspect of precision agriculture and crop management, helping to prevent crop losses and improves yield quality.Plants are very essential in our life they provide source of energy and overcome the matter of global warming.Plant disease is notable risk of nutrition security.Therefore, timely detection of risk is important.Leaf disease detection using the machine learning is an approach.Machine learning offers a worthy approach for making a classy and automatic algorithm using Convolutional Neural Network (CNN), Artificial Intelligence (AI), Image Processing and video processing, voice processing, Natural Language Processing, etc.This review report provides the comparative analysis of the different machine learning algorithms of diagnosis of different leaf disease.