Advanced Bean Leaf Lesion Classification Using EfficientNetV2-S for Agricultural Applications

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

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

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

Plant leaf diseases are a key concern for agriculture and result in significant loss of crop yield and economic losses globally. It is vital to efficiently and accurately detect plant diseases to properly manage crops and control their diseases. This paper demonstrates a CNN-based image analysis model to automatically identify and classify plant leaf diseases from digital images. Deep learning is used in the proposed method to spontaneously learn hierarchical features from original image data, without the use of feature engineering. The model was trained and evaluated on a collection of high-resolution healthy and diseased leaf images collected from different plant species. Preprocessing (normalisation, noise filtering, and contrast increment) and data augmentation (rotations, scale changes, and flips) were also performed on the pre-processed images, and it was expected to achieve good generalisation and reduce overfitting. The CNN architecture was optimised using transfer learning in combination with hyperparameter tuning. Evaluation experiments showed that the framework attained a classification “accuracy of 96.2%, 95.8% precision, 96.5% recall, and 96.1% F1-score”. The model proved to be robust under varying light conditions and complex background settings, demonstrating its real-world applicability. In addition, the model’s lightweight architecture supports mobile and edge computing implementation, enabling real-time and on-site diagnostic capabilities. This method provides an automated, scalable system for plant disease detection, thus enabling early intervention, reducing chemical treatment reliance, and fostering sustainable agricultural practices, fostering environmentally friendly approaches. The results demonstrated the capability of CNN systems towards transforming the plant health monitoring practices in precision agriculture.

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.

Plant leaf diseases pose significant challenges to agricultural productivity, necessitating early detection and intervention strategies. Recent research has increasingly turned to artificial intelligence (AI) techniques, particularly image processing and deep learning, to address these challenges. The paper provides a technical view of the application of Artificial Intelligence in plant leaf disease detection. Key aspects covered include datasets, image processing methodologies, deep learning algorithms, and evaluation metrics commonly utilized in this domain. An emphasis is placed on the important role of annotated datasets in the training of AI models, facilitating the development of accurate classification systems. Image processing techniques, encompassing tasks such as enhancement, segmentation, and feature extraction, are discussed for their role in preprocessing leaf images to extract salient features. Deep learning algorithms, notably convolutional neural networks (CNNs), are highlighted for their effectiveness in automatically learning discriminative features from input images and achieving high accuracy rates in disease classification tasks. The paper illuminates the significance of employing evaluation metrics such as accuracy, precision, recall, and F1 score as quantitative benchmarks to gauge the efficacy of model performance. It highlights the triumphs observed across diverse studies harnessing AI methodologies for the detection of plant leaf diseases, emphasizing their transformative capacity in reshaping agricultural management paradigms. Future research directions are outlined, including the exploration of transfer learning methods to capitalize on pre-trained models and the integration of additional sensing technologies like hyperspectral imaging and drones for enhanced disease detection capabilities. These advancements are poised to further refine the accuracy and efficiency of plant leaf disease detection systems, thereby offering substantial benefits to agriculture and food production sectors.

Farming is the mainstream of the Indian economy. Plant leaf disease detection mainly focuses on the health of leaf plants and detecting diseases in their early stages is a tedious task. In this paper, we proposed the customized convolutional neural network (CCNN) model for predicting diseases of leaves of basil plants. We modified the traditional CNN model in a way that it deals efficiently with n-dimensional features data as well as unbalanced class data. Here, we predicted diseases of leaves of basil plants in four categories as fungal, downy mildew, fusarium wilt, and healthy. No such standard dataset is available for basil leaves so here we prepared our own 916 image dataset, and the model is trained and tested by different machine learning approaches However, CCNN has given more accurate results than other existing algorithms. The model detects diseases of leaves with an accuracy of 94.24% for four classes of basil plant leaves.

Accurate and effective classification of plant diseases is critical for increasing yield and quality in agricultural production, minimizing economic losses through early detection of diseases, and implementing sustainable agriculture approaches. In this study, an approach for the detection and classification of plant leaf diseases is presented. We compare the performance of machine learning and deep learning-based models, and we use GAN-based data synthesis methods on a dataset we created to improve the model performance. For machine learning methods, ResNet-based feature extraction is performed and XGBoost, Random Forest, SVM, and InceptionV3 models are evaluated. In contrast, AlexNet, VGG16, VGG19, DenseNet, and ResNet models are examined within the scope of deep learning. The study was analyzed in three classes Phytophthora Infestans, Potassium Deficiency, and Healthy, and tested on data obtained from 21 different plant species. According to the model performances obtained, the deep learning-based ResNet model showed the highest success in all performance metrics and achieved 99% accuracy, showing superior performance compared to other methods. In the study, a comprehensive evaluation of multiple classification, GAN-based data synthesis, machine learning, and deep learning models was carried out. A valuable contribution was made to the existing studies in the literature.

The bean leaves can be affected by several diseases, such as angular leaf spots and bean rust, which can cause big damage to bean crops and decrease their productivity. Thus, treating these diseases in their early stages can improve the quality and quantity of the product. Recently, several robotic frameworks based on image processing and artificial intelligence have been used to treat these diseases in an automated way. However, incorrect diagnosis of the infected leaf can lead to the use of chemical treatments for normal leaf thereby the issue will not be solved, and the process may be costly and harmful. To overcome these issues, a modern deep learning framework in robot vision for the early detection of bean leaves diseases is proposed. The proposed framework is composed of two primary stages, which detect the bean leaves in the input images and diagnosing the diseases within the detected leaves. The U-Net architecture based on a pre-trained ResNet34 encoder is employed for detecting the bean leaves in the input images captured in uncontrolled environmental conditions. In the classification stage, the performance of five diverse deep learning models (e.g., Densenet121, ResNet34, ResNet50, VGG-16, and VGG-19) is assessed accurately to identify the healthiness of bean leaves. The performance of the proposed framework is evaluated using a challenging and extensive dataset composed of 1295 images of three different classes (e.g., Healthy, Angular Leaf Spot, and Bean Rust). In the binary classification task, the best performance is achieved using the Densenet121 model with a CAR of 98.31%, Sensitivity of 99.03%, Specificity of 96.82%, Precision of 98.45%, F1-Score of 98.74%, and AUC of 100%. The higher CAR of 91.01% is obtained using the same model in the multi-classification task, with less than 2 s per image to produce the final decision.

The agriculture sector is undergoing a transformative evolution driven by the integration of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision (CV). These advancements are not only addressing long-standing challenges but also unlocking new possibilities for enhancing productivity, sustainability, and decision-making in agricultural practices. In this context, BotaniCare emerges as a comprehensive AI-based intelligent agricultural assistant, designed to support both farmers and agricultural researchers through a suite of innovative features and services. BotaniCare delivers end-to-end solutions encompassing crop cultivation guidance, early disease detection and prevention, interactive chatbot assistance, and analysis of the medicinal value of plants. By employing Computer Vision, the system is capable of real-time monitoring and assessment of plant health, identifying symptoms of disease, nutrient deficiencies, and pest infestations through image-based diagnostics. This empowers users to take timely and informed actions, minimizing crop loss and reducing dependency on manual inspections. At the core of BotaniCare is a robust Machine Learning framework that leverages vast datasets comprising geographical information, climate conditions, and soil profiles to recognize patterns and generate tailored recommendations. These predictive insights assist in optimizing planting schedules, irrigation plans, fertilizer usage, and crop selection strategies, ultimately improving yield and resource efficiency. Furthermore, BotaniCare integrates Generative AI technologies to provide personalized and context-aware conversational support. The embedded intelligent chatbot serves as a 24/7 virtual advisor, offering dynamic assistance in local languages and dialects, answering queries, delivering updates on weather or market trends, and guiding users through complex agricultural practices with ease. The architecture of the system combines cloud-based services, edge computing, and mobile accessibility to ensure scalability, low-latency interactions, and real-time decision support in both connected and remote environments. This paper explores the design and implementation methodology of BotaniCare, detailing the technical stack, data pipelines, training strategies, and deployment mechanisms involved in building the system. Keywords: AI in Agriculture, Crop Recommendation System, Plant Disease Detection, Computer Vision, Generative AI, Medicinal Plants, Smart Farming.

Crop disease is a serious concern for safety of food, but its fast detection still remains difficult in different parts of the world because of the lack of proper infrastructure. Automatic identification of plant diseases is necessary for food security, yield loss estimation and management of disease. With the worldwide increase in digital cameras and continuous improvement in computer vision domain, the automated techniques for detection of disease are highly in demands in precision agriculture, highly productive plant phenotype, smart greenhouse and much more. Working on an open dataset which includes 15200 images of crop leaves, a Residual Network (ResNet34) was trained to perform this task of classification. The proposed ResNet34 model accomplished a 99.40% accuracy on a test set, illustrating the viability of the proposed model. Overall, the process of training ResNet models on an open image dataset provides a sound way towards crop disease detection using automated networks on an enormous global scale.

The early and accurate detection of diseases in tomato plants is crucial for ensuring high crop yields and quality. Traditional methods of disease identification are often labor-intensive, time-consuming, and subject to human error. This research presents an enhanced classification system for detecting tomato leaf diseases using Convolutional Neural Networks (CNNs). Leveraging the power of deep learning, our approach significantly improves the accuracy and efficiency of disease identification compared to conventional techniques. We developed a CNN-based model that was trained and validated using a comprehensive dataset of tomato leaf images, encompassing various disease classes and healthy samples. The model architecture was meticulously designed to optimize feature extraction and classification performance. Extensive data augmentation techniques were employed to enhance the robustness and generalization capability of the model. Our enhanced classification system achieved remarkable accuracy, outperforming existing models in the literature. The results demonstrate the model's ability to effectively differentiate between multiple disease types and healthy leaves, even under challenging conditions such as varying lighting and background noise. This advancement holds great potential for integration into automated agricultural systems, providing farmers with a reliable tool for early disease detection and management.The implementation of this CNN-based improved classification system can lead to significant advancements in precision agriculture, minimizing crop losses and promoting sustainable farming practices. Future work will focus on extending this approach to other crops and refining the system for real-time field applications.

Plant disease management is an essential process to minimize loss in the field of agriculture. Plant leaf disease detection(PLDD) technology helps the formers to reduce the loss of quality yield production. This study focuses on detecting paddy and maize plant leaf disease-detecting methods. It contributes to a PLDD method to improve detection accuracy and overall performance. The PLDD method introduced a whales-optimized artificial neural network (WOANN) method for classifying the four Maize and four rice leaf disease-related classes. The WOANN uses the whale optimizer's food-searching functionalities to improve the performance and detection accuracy of the dense net model. This WOANN classifies the maize leaf light, maize grey leaf spot, maize common rust, rice bacterial leaf blight, rice bacterial leaf steak, rice brown spot, and healthy leaves of both Maize and paddy. It uses the Hilbert-Schmidt independent criterion lasso correlation algorithm to support the WOANN classifier in selecting the significant features. The performance analysis shows that the WOANN-based approach achieves a maximum of 99.35% detection accuracy for maize leaf disease and 99.13% for paddy leaf disease. Its efficiency analysis shows that the WOANN-based approaches achieve a maximum accuracy rate than comparison approaches.

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.

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.