Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification

Identifying disease from the images of the plant is one of the interesting research areas in computer and agriculture field. This paper presents a survey of different image processing and machine-learning techniques used in the identification of rice plant diseases based on images of disease infected rice plants. This paper presents not only survey of various techniques but also concisely discusses important concepts of image processing and machine learning applied to plant disease detection and classification. We carry out detailed study of 19 papers, covering the work on rice plant diseases and other different plants and fruits, and present a survey of these papers based on important criteria. These criteria include size of image dataset, no. of classes(diseases), preprocessing, segmentation techniques, types of classifiers, accuracy of classifiers etc. We utilize our survey and study to propose and design our work on detection and classification of rice plant diseases.

Agriculture accepts a basic part by virtue of the quick improvement of the general population and extended interest in food in India. Hence, it is required to increase harvest yield. One serious cause of low collect yield is an infection brought about by microorganisms, infection, and organisms. Plant disease investigation is one of the major and essential tasks in the part of cultivating. It tends to be forestalled by utilizing plant disease detection techniques. To monitor, observe or take care of plant diseases manually is a very complex task. It requires gigantic proportions of work, and moreover needs outrageous planning time; consequently, image processing is utilized to distinguish diseases of plants. Plant disease classification can be done by using machine learning algorithms which include steps like dataset creation, load pictures, pre-preparing, segmentation, feature extraction, training classifier, and classification. The main objective of this research is to construct one model, which classifies the healthy and diseased harvest leaves and predicts diseases of plants. In this paper, the researchers have trained a model to recognize some unique harvests and 26 diseases from the public dataset which contains 54,306 images of the diseases and healthy plant leaves that are collected under controlled conditions. This paper worked on the ResNets algorithm. A residual neural network (ResNet) is a subpart of the artificial neural network (ANN). ResNet algorithm contains a residual block that can be used to solve the problem of vanishing/exploding gradient. ResNet algorithm is also used for creating Residual Network. For the image classification, ResNets achieve a much well result. The ResNets techniques applied some of the parameters like scheduling learning rate, gradient clipping, and weight decay. Using the ResNet algorithm, the researchers expect high accuracy results and detecting more diseases from the various harvests.

The accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. As a result of the phenotype similarity of the hazarded plant after plant diseases and pests occur, as well as the interference of the external environment, traditional deep learning models often face the overfitting problem in phenotype recognition of plant diseases and pests, which leads to not only the slow convergence speed of the network, but also low recognition accuracy. Motivated by the above problems, the present study proposes a deep learning model EResNet-support vector machine (SVM) to alleviate the overfitting for the recognition and classification of plant diseases and pests. First, the feature extraction capability of the model is improved by increasing feature extraction layers in the convolutional neural network. Second, the order-reduced modules are embedded and a sparsely activated function is introduced to reduce model complexity and alleviate overfitting. Finally, a classifier fused by SVM and fully connected layers are introduced to transforms the original non-linear classification problem into a linear classification problem in high-dimensional space to further alleviate the overfitting and improve the recognition accuracy of plant diseases and pests. The ablation experiments further demonstrate that the fused structure can effectively alleviate the overfitting and improve the recognition accuracy. The experimental recognition results for typical plant diseases and pests show that the proposed EResNet-SVM model has 99.30% test accuracy for eight conditions (seven plant diseases and one normal), which is 5.90% higher than the original ResNet18. Compared with the classic AlexNet, GoogLeNet, Xception, SqueezeNet and DenseNet201 models, the accuracy of the EResNet-SVM model has improved by 5.10%, 7%, 8.10%, 6.20% and 1.90%, respectively. The testing accuracy of the EResNet-SVM model for 6 insect pests is 100%, which is 3.90% higher than that of the original ResNet18 model. This research provides not only useful references for alleviating the overfitting problem in deep learning, but also a theoretical and technical support for the intelligent detection and control of plant diseases and pests. © 2024 Society of Chemical Industry.

In order to mitigate decreases in agricultural yield and production, the identification of diseases in plants assumes paramount importance. The agricultural sector has been employing various methodologies rooted in machine learning and image processing to address these challenges. This comprehensive analysis focuses specifically on the detection of diseases in rice plants by leveraging a diverse array of machine learning and image processing techniques with input images of infected rice plants. Furthermore, we delve into significant concepts pertaining to machine learning and image processing that aid in the identification and categorization of plant diseases. Various classification methods such as the k-Nearest Neighbor Classifier (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Genetic Algorithm (GA), and others find applications in agricultural research endeavors. The selection of an appropriate classification method assumes critical importance as the quality of the output is contingent on the input data. The classification of plant leaf diseases finds utility across multiple domains, including agriculture and biological research. This paper presents an extensive exploration of rice plant diseases, encompassing aspects such as image dataset size, preprocessing techniques, segmentation methods, and classifiers.

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.

Plant disease detection is a critical task in agriculture, essential for ensuring crop health and productivity. Traditional methods in this context are often labor-intensive and prone to errors, highlighting the need for automated solutions. While computer vision-based solutions have been successfully deployed in recent years for plant disease identification and localization tasks, these often operate independently, leading to suboptimal performance. It is essential to develop an integrated solution combining these two tasks for improved efficiency and accuracy. This research proposes the innovative Plant Disease Localization and Classification model based on Vision Transformer (PDLC-ViT), which integrates co-scale, co-attention, and cross-attention mechanisms and a ViT, within a Multi-Task Learning (MTL) framework. The model was trained and evaluated on the Plant Village dataset. Key hyperparameters, including learning rate, batch size, dropout ratio, and regularization factor, were optimized through a thorough grid search. Early stopping based on validation loss was employed to prevent overfitting. The PDLC-ViT model demonstrated significant improvements in plant disease localization and classification tasks. The integration of co-scale, co-attention, and cross-attention mechanisms allowed the model to capture multi-scale dependencies and enhance feature learning, leading to superior performance compared to existing models. The PDLC-ViT model evaluated on two public datasets achieved an accuracy of 99.97%, a Mean Average Precision (MAP) of 99.18%, and a Mean Average Recall (MAR) of 99.11%. These results underscore the model's exceptional precision and recall, highlighting its robustness and reliability in detecting and classifying plant diseases. The PDLC-ViT model sets a new benchmark in plant disease detection, offering a reliable and advanced tool for agricultural applications. Its ability to integrate localization and classification tasks within an MTL framework promotes timely and accurate disease management, contributing to sustainable agriculture and food security.

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. Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review

With the rapid development of precision agriculture and smart agriculture, the need to build an automatic identification and detection system for diseases and insect pests is increasing. Using computers to correctly label plant diseases and insect pests is an important prerequisite for achieving accurate classification of plant diseases and insect pests and ensuring system performance. In order to improve the accuracy of computer classification of plant pests and diseases, this paper proposes an automatic pest identification method based on the Vision Transformer (ViT). In order to avoid training overfitting, the plant diseases and insect pests data sets are enhanced by methods such as Histogram Equalization, Laplacian, Gamma Transformation, CLAHE, Retinex-SSR, and Retinex-MSR. Then use the enhanced data set to train the constructed ViT neural network, so as to realize the automatic classification of plant diseases and insect pests. The simulation results show that the constructed ViT network has a test recognition accuracy rate of 96.71% on the plant disease and insect pest public data set Plant_Village, which is about 1.00% higher than the Plant disease and pest identification method based on traditional convolutional neural networks such as GoogleNet and EfficentNetV2.

A country's inventive growth is dependent on the agricultural sector. Agriculture, the foundation of all nations, offers food and raw resources. Agriculture is hugely important to humans as a food source. As a result, plant diseases detection has become a major concern. Traditional methods for identifying plant disease are available. However, agriculture professionals or plant pathologists have traditionally employed empty eye inspection to detect leaf disease. This approach of detecting plant leaf disease traditionally can be subjective, time-consuming, as well as expensive, and requires a lot of people and a lot of information about plant diseases. It is also possible to detect plant leaf diseases using an experimentally evaluated software solution. Currently, machine learning and deep learning are using in recent years. The agriculture sector is also not a exception for machine learning. In this paper, we applied "Convnets" for plant disease detection and classification. We collected a PlantViallge dataset from Kaggle. It contains images of 15 different classes of plant leaves of three different plants potato, pepper, tomato. We divided the dataset into three datasets and applied Convnets on three datasets. We achieved an accuracy of 98.3%,98.5%,95% for potato plant disease detection, pepper plant disease detection, tomato plant disease detection. Experimental results have shown that our model achieved a good accuracy rate for plant leaf disease detection and classification.

Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images

Rice plant diseases cause to reduce 37% of Rice productions annually. This happens due to late detection of diseases and lack of knowledge about Rice plant diseases. This could be overcome by developing a software application for farmers which is capable of identifying plant diseases through it and give solutions for those diseases. For this disease identification process, Convolutional Neural Networks can directly be used as it is very efficient in image identification and classification. Even though this approach has followed by many researchers for plant disease detection, comparatively low researches are conducted on identifying rice plant diseases. This paper gives a clear idea about existing rice plant diseases and how Deep Learning methodologies are used for identifying those diseases, also it reviews different type of technologies which were used in the literature by comparing each of them with their advantages and disadvantages, as the final conclusion of this review author could able to identify the most efficient methods in each steps of image detection process which will be important for identify rice plant diseases.

The agriculture industry significantly contributes to economic and population growth through the provision of high-quality food. Plant diseases decrease crop yields, which directly influences food production systems and leads to economic losses. Insufficient diagnosis and treatment of plant diseases can significantly affect agricultural productivity. Accurate identification is essential in order to prevent a severe decline in agricultural yield. Automatic detection models are crucial for accurate plant disease prediction and effective agricultural disease management. Deep Learning (DL) models are in great demand right now for identifying plant diseases because of how well they can anticipate and improve farmers’ crop yields. This study examines various DL algorithms for visual data-based plant disease detection and classification in great detail. Firstly, a brief overview is provided on various plant disease identification and classification systems developed by researchers, which utilize DL algorithms. Subsequently, a comprehensive analysis is undertaken to get deeper insights into the constraints of current algorithms and propose an innovative methodology for categorizing various forms of plant diseases, thereby contributing to the enhancement of agricultural output.

To identify novel advancements in plant diseases detection and classification systems employing Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL), this research compiled 111 peer-reviewed papers published between 2019 and early 2023. The literature was sourced from databases such as Scopus and Web of Science using keywords related to deep learning and leaf disease. A structured analysis of various plant disease classification models is presented through tables and graphics. This paper systematically reviews the model approaches employed, datasets utilized, countries involved, and the validation and evaluation methods applied in plant disease identification. Each algorithm is annotated with suitable processing techniques, such as image segmentation and feature extraction, along with standard experimental metrics, including the total number of training/testing datasets utilized, the quantity of disease images considered, and the classifier type employed. The findings of this study serve as a valuable resource for researchers seeking to identify specific plant diseases through a literature-based approach. Additionally, the implementation of mobile-based applications using the DL approach is expected to enhance agricultural productivity.

- The Plant Disease Detection and Classification project is designed to help farmers identify crop diseases at an early stage using images of plant leaves. By applying advanced machine learning (ML) and deep learning (DL) techniques, this system aims to automatically detect and classify various plant diseases with high accuracy. Early identification of plant diseases plays a vital role in improving crop yield, reducing unnecessary pesticide usage, and encouraging sustainable farming practices. Once a disease is detected, the system not only identifies it but also provides a brief description and recommends appropriate prevention and treatment methods. To enhance performance, the project uses data augmentation and normalization techniques—helping the model handle variations in lighting, angles, and environmental conditions. The system is trained on a diverse dataset containing images of both healthy and diseased leaves from different plant species, categorized into 38 different classes. The project incorporates various machine learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Random Forests, as well as a Convolutional Neural Network (CNN) for deep learning. Among these, CNNs have shown outstanding performance due to their ability to learn spatial features from images effectively. While existing research has shown promising results, real-world challenges such as varying image quality, lighting conditions, and background clutter still affect accuracy. This project aims to overcome these issues by training on large, diverse datasets and fine-tuning model parameters. Ultimately, this project hopes to empower farmers with an intelligent tool to monitor crop health, make informed decisions, and contribute to food security through precision agriculture. Keywords: Plant Disease Detection, Automated Disease Detection System, Machine Learning, Deep Learning, Data Augmentation, Data Normalization, Support Vector Machine, KNN Classifier, Convolutional Neural Network, Decision Trees, Random Forest

The agricultural sector contributes significantly to the gross domestic product of emerging nations. One of the primary factors that reduce agricultural productivity is plant diseases. Detecting plant diseases is a crucial step requiring attention for a productive economy and agriculture. Traditional techniques of crop disease detection are laborious and time-consuming, requiring a great deal of skill and effort. Academicians, researchers and practitioners have recently shown a great interest in the new area of automatic plant disease identification. As soon as they arise on plant leaves, early detection of plant diseases can be aided by machine learning (ML) and deep learning (DL). A comprehensive analysis has been conducted in this review paper to assess the viability to identify plant diseases applying machine learning models. This paper also considers the illnesses and viruses that affect different crops: black gram, chickpea, mungbean and potato. A detailed procedure has been done to recognise and classify plant diseases. The analysis of various ML and DL algorithms adopted by researchers to classify diseases has been explained. Finally, challenges are highlighted to recognise and classify plant diseases utilising ML and DL algorithms, particularly in legume crops. Developing and integrating real-time datasets on plant diseases using advanced models is the future for monitoring, detecting and managing plant ailments.