A SCALABLE IMAGE-BASED FRAMEWORK FOR DETECTING AND MONITORING RICE LEAF DISEASES

Implementation of MobileNetV2-Based Deep Learning for Corn Leaf Disease Detection Using a Web-Based System

This study investigates the hypothesis that a lightweight Convolutional Neural Network (CNN), specifically MobileNetV2, can achieve competitive classification accuracy for corn leaf disease detection while maintaining computational efficiency suitable for real-time web-based deployment. Corn is a critical agricultural commodity, and plant diseases significantly threaten productivity, causing economic losses and reduced yields. Traditional identification methods depend on manual observation, which is time-consuming, subjective, and error-prone at scale. While recent deep learning approaches such as Vision Transformers and hybrid architectures have demonstrated high accuracy, their computational demands limit practical deployment in resource-constrained agricultural environments. To address this gap, this research employs MobileNetV2 with transfer learning from ImageNet, leveraging depthwise separable convolutions for efficient feature extraction. The dataset comprises 3,852 corn leaf images categorized into four classes: Healthy, Common Rust, Gray Leaf Spot, and Northern Leaf Blight. Preprocessing includes image resizing to 224×224 pixels, normalization, and 80:20 train-validation splitting. The model was trained using TensorFlow and deployed via a Streamlit-based web interface for real-time inference. Experimental results demonstrate a training accuracy of 96.76% and a validation accuracy of 92.86%, with inference confidence reaching 99.80% during testing. The primary contribution of this work lies in the empirical demonstration that MobileNetV2, without architectural modification, can serve as a practical and accessible diagnostic tool when integrated into a web-based deployment pipeline, offering a favorable trade-off between accuracy and computational cost compared to heavier architectures.

An Efficient Convolutional Neural Network Based Deep Learning Framework for Rice Leaf Disease Classification

Rice is one of the most popular and extensively grown crops. More than fifty percent of the global population consumes it as a staple food. However, a number of diseases affect the quality of crop which also affect the total production of the yield. Identification of such disorders is important for crop management. Traditional disease detection methods are time consuming and susceptible to human error because they rely on personal evaluation by professionals. In our work, we have built a task specific system. This system uses a convolutional neural network based framework for detection and classification of rice leaf diseases by looking at leaf pictures. Two datasets are used in this work. The rice leaf disease dataset contains 16000 images which are distributed into four classes with 4000 images in each class and additionally 139 images of brown spot class from A dataset of rice leaf disease, to add variation in the dataset, is used. Duplicate images were cleaned and removed. Images were preprocessed. For fair evaluation, the dataset was divided into training set (70%), validation set (15%), and testing set (15%). With 25 epochs, by employing Adam optimizer, model was trained. It is designed to extract visual features using four convolutional layers, five batch normalization layers, four pooling layers and two fully connected layers. To see how well trained model is performed, it is evaluated against some transfer learning models like MobileNetV2, ResNet50 and Vgg16 on the same dataset. Performance evaluation using accuracy, model size, time per image, and number of parameters showed that the proposed model achieved high accuracy and provided better discrimination between visually similar disease classes. Proposed model achieves 99.33% accuracy, has model size of 1.618 MB, 9.512 ms time per image, 424260 parameters. In contrast MobileNetV2 achieved 97.17% accuracy, has model size of 8.633 MB, 14.141 ms time per image and 2263108 parameters. ResNet50 achieved 44.93% accuracy, has model size of 90.01 MB, 64.661 ms time per image and 23595908 parameters. Vgg16 achieved 79.75% accuracy, has model size of 56.13 MB, 193.830 ms time per image and 14716740 parameters. Proposed model has performed well and doesn’t need numerous parameters, this shows that for practical implementation, it can be appropriate.

A Multimodal, Multi-Task, and Uncertainty-Aware Deep Learning Framework for Rice Leaf Disease Detection and Severity Estimation (RICE-MuSTA)

Background: Rice is a staple food crop for more than half of the world’s population and plays a central role in food security, particularly in Asian and African countries. However, rice productivity is severely affected by foliar diseases such as rice blast, bacterial blight, sheath blight, and brown spot. Delayed or inaccurate diagnosis of these diseases can lead to yield losses ranging from 10–40%, directly impacting farmers’ livelihoods. While recent advances in deep learning have enabled automated leaf disease detection, most existing approaches remain limited to image-only classification, lack severity assessment, ignore environmental uncertainty, and are unsuitable for real-time field deployment Methods: In this work, we introduce RICE-MuSTA, a framework designed to jointly address multimodality, severity estimation, and uncertainty in rice leaf disease monitoring. The framework integrates leaf image features extracted using convolutional neural networks (CNNs), global contextual representations learned via Vision Transformers (ViTs), and microclimate information encoded through a multilayer perceptron (MLP). Findings: The proposed model simultaneously performs disease classification and severity estimation while quantifying predictive uncertainty and providing lesion-level explainability using CAM-based heatmaps. To enable practical adoption, a teacher–student distillation and quantization strategy is employed, compressing the model into a lightweight architecture suitable for mobile and edge deployment.Novelty and applications: Across multiple datasets and evaluation settings, we observed consistent improvements in robustness, interpretability, and deployment efficiency.

Plant Leaf Disease Detection using EfficientNetB5 and Transfer Learning

Plant leaf diseases significantly reduce agricultural productivity and crop yield worldwide, making early and accurate detection essential to prevent large-scale crop damage. Traditional disease identification methods rely on manual inspection by experts, which is time-consuming, costly, and often inaccessible to farmers in rural areas. This paper proposes an AI-based leaf disease detection system using deep learning and transfer learning, in which EfficientNetB5 serves as a pretrained feature extractor to classify 38 plant disease categories spanning 14 crop species. Preprocessing includes HSV-based leaf segmentation, resizing to 456×456 pixels, and EfficientNet-specific normalization. A compact two-layer dense classifier is trained on the 2,048-dimensional feature vectors produced by the frozen backbone. The system achieves an overall validation accuracy of 96.49%, macro-average precision of 0.97, recall of 0.96, and F1-score of 0.96 on 2,280 held-out images. Beyond classification, the system provides actionable cure and precautionary recommendations for every detected disease, making it directly useful to smallholder farmers. Comparative analysis with ResNet50, VGG16, and MobileNetV2 confirms that EfficientNetB5 achieves the highest accuracy with a favorable parameter-to-performance ratio. Multi-class ROC evaluation further demonstrates strong discriminative capability across all disease categories.

A Robust Method for Real-Field Tomato Disease Classification Using FRCNN-Based Leaf Isolation and FCM-Guided Variability Estimation

Automated Detection and Classification of Vegetable Leaf Disease using Machine Learning Techniques

Plant leaf diseases are a major concern in agriculture because they reduce crop quality, lower productivity, and cause economic losses to farmers. Early detection of these diseases is important for protecting plants and improving overall crop management. However, manual identification through visual inspection is often slow and may produce inaccurate results, especially in the early stages of infection. To overcome this problem, this work proposes an automated leaf disease detection system using image processing and deep learning techniques. OpenCV is used for preprocessing the leaf images through resizing, noise removal, color normalization, and enhancement, while a Convolutional Neural Network (CNN) is trained to extract important features and classify the images into different disease categories. The dataset consists of healthy and diseased leaf images collected from publicly available sources and is divided into training, validation, and testing sets for effective model development. For practical implementation, the trained model is integrated into a Flask-based web application that allows users to upload leaf images and obtain predictions easily. The performance of the system is evaluated using accuracy, precision, recall, and F1-score, and the model achieved an accuracy of 95%. The results show that the proposed system can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.