عنوان المشروع: LeafGuard AI: A Deep Learning System Based on ResNet50 and Large Language Models for Real-Time Plant Disease Detection and Bilingual Treatment Recommendation
الوصف:
Plant diseases represent a major threat to agricultural productivity and food security, especially in developing regions where access to agricultural experts is limited. This project presents LeafGuard AI, an intelligent deep learning-based system for real-time plant disease detection and bilingual treatment recommendation. The proposed system combines image classification using a custom-trained ResNet50 Convolutional Neural Network (CNN), cloud-based inference using FastAPI, and dynamic treatment generation using the Google Gemini Large Language Model (LLM). The model was trained and evaluated using the CCMT Dataset-Augmented, which includes images from four crop species: cashew, cassava, maize, and tomato. After collecting the four crop folders and merging duplicated class labels, the final dataset contained 105,252 images distributed across 18 disease, pest, and healthy classes. Images were resized to 224 × 224 pixels and augmented using random flipping, rotation, zooming, and translation to improve generalization. The ResNet50 model was used through transfer learning with a custom classification head consisting of GlobalAveragePooling2D, Dropout, and a Softmax output layer. After disease detection, the system invokes the Google Gemini API to generate structured treatment recommendations in both Arabic and English. These recommendations are cached locally using AsyncStorage to reduce repeated API calls and improve response time. The proposed system provides a practical end-to-end solution for precision agriculture by integrating plant disease classification, cloud deployment, multilingual treatment guidance, and cross-platform accessibility.
تاريخ رفع الملفات: 2026-07-05 07:48:47