TL;DR
An advanced Rice Disease Detection model incorporating a multi-step approach of image augmentation, preprocessing, feature extraction, and hybrid detection is introduced, addressing both computational efficiency and detection precision.
The rice disease identification method proposed in this paper provides a reference for the deployment and application of the method in different light conditions in the field and achieves test average accuracy of 83.9% and a maximum validation accuracy of 88.0% on the testing sets.
Experimental results show that YOLOv12-CropNet finds a suitable balance between computational speed and accurate detection, and accuracy, F1-score, recall, and precision are important performance metrics that verify the model's resilience in challenging environmental and visual circumstances.
Artificial intelligence has become an effective tool for improving agricultural productivity through automated crop disease diagnosis. Brinjal (eggplant) cultivation suffers substantial yield losses from diseases such as Shoot and Fruit Borer, Wet Rot, Fruit Cracking, and Phomopsis Blight, yet reliable field-based diag...
This study reviews pre-2018 AI-based approaches, focusing on techniques such as image processing, feature extraction, and classification methods, and highlights models like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid systems combining traditional and modern techniques.
A scalable image-based framework for automatic rice leaf disease detection and monitoring using deep learning and cloud-enabled analytics and enables deployment on smartphones, drones, and edge devices for smart agriculture applications.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
