Plant Leaf Disease Detection using EfficientNetB5 and Transfer Learning

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.

Advancements in Plant Leaf Disease Recognition: YOLO-Based Deep Learning Approaches

: Plant leaf diseases cause severe losses in crop yields and qualities, and account for considerable volume of losses to the agricultural output globally. Recognition of plant disease early and rightly is crucial to disease treatment and to reduce loss to the crop and to maintain agricultural sustainability. Plant disease that occurs on the leaves has been traditionally detected by farmers and experts with naked eyes by checking its symptoms like discoloration, spots and lesions. However, the process requires time, labour, expertise and is subjective, which renders it unusable for large-scale implemented agriculture. Recent years have seen the promising use of Artificial Intelligence (AI) as a tool for automated plant disease identification. The extraction of manually-crafted features from photographs of plant leaves, such as colour, texture, and form, is at the heart of many Machine Learning (ML) approaches used for disease classification. While these ML models have shown acceptable performance, they require significant manual feature engineering and can be poor at operating in real-world settings and with voluminous data. To address these issues, Deep Learning (DL) algorithms have found extensive usage in the identification and categorisation of plant leaf diseases. The You Only Look Once (YOLO) family of detection of objects models is making waves in the DL object detection space thanks to its impressive dual-tasking capabilities: object identification and multiple illness categorisation in a single pass, all at lightning speed and with pinpoint accuracy. For real-time disease identification in precision agriculture, YOLO stands out as an end-to-end feature learning and object recognition method, set apart from typical ML approaches. Understanding the DL models suggested for plant leaf disease detection and classification using the YOLO principle is the primary goal of this survey. It also provides a comparative and performance analysis of these models by examining their techniques, merits, demerits, datasets used, and evaluation metrics.

Automated Potato Tomato Disease Detection Using EfficientNetB3 Transfer Learning

Plant disease significantly influences agricultural production performance, particularly in valuable crops such as potatoes and tomatoes. The disease detection should be precise and automated in order to improve crop management and sustainable agriculture. Leaf disease classification for a number of diseases via multi-class approach using EfficientNetB3 architecture is proposed as the optimal structure of deep learning. Weights trained on ImageNet are used to facilitate transfer learning and feature extraction efficiency, while customized classifiers are designed to provide improved generalization, including batch normalization and dropout techniques. Input data diversity and robustness is provided via data augmentation techniques (rotation, translation, and horizontal flips). The adaptive learning approach together with Adamax optimizer are used to train the classifier. The classifier was tested using the PotatoTomato dataset from the PlantVillage repository which consists of six disease and healthy categories. The achieved accuracy score was 99.25%, while achieving precision and recall of various categories at 100%. The good performance in multi-classification proves the soundness of the classifier.

Plant Disease Detecting System Using CNN

Sugarcane Leaf Disease Detection and Classification Using Deep Learning

Real-Time Eggplant Leaf Disease Diagnosis Using Image Classification

Eggplant is an important horticultural crop whose productivity is often affected by various leaf diseases that reduce crop quality and yield. Manual identification of plant diseases relies heavily on human observation and experience, making it time-consuming and prone to misclassification, especially when symptoms appear visually similar. The system employed a convolutional neural network/transfer learning model to identify eggplant leaf diseases accurately and efficiently. The system utilizes a deep learning model trained on a dataset of 3,551 leaf images categorized into seven disease classes and one healthy class. Image preprocessing and augmentation techniques were applied to improve model performance and generalization. Experimental evaluation showed that the proposed model achieved a testing accuracy of approximately 82% with balanced precision and recall across all categories, indicating stable classification performance. The trained model was integrated into a web-based application that allows users to upload leaf images and obtain real-time diagnostic results along with recommended handling information. The findings demonstrate that the proposed system provides a practical and reliable solution for early disease detection and supports more efficient agricultural management. Future development may include expanding dataset diversity, improving model robustness, and deploying mobile-based applications to enhance accessibility and scalability in precision agriculture.