This project aims developing system that can identify and categorize diseases in plant leaves from images. This process integrates various stages from data collection to deployment, utilizing advanced machine learning techniques to improve agricultural productivity and plant health.
Built with Expo, Expo Router, Reanimated 3 etc. PlantApp
web application that help people grow healthy plants
plant resistance gene analyzer for getting the dna and protein sequences.
functional gene assembly of plant resistance genes
AgroGuard is a deep-learning-based application that helps us identify different diseases in plants and provides timely cures.
This app helps detect and diagnose plant diseases using advanced image recognition and deep learning models like MobileNetV2. By analyzing plant photos, it provides accurate disease identification and timely insights for farmers and gardeners.
This Django-based REST API facilitates the detection of diseases on plant leaves, aiding in the identification of leaf diseases.
Transfer learning efficientNet to classification disease on plants. Build web application with React and FastAPI
Plant Disease Detection Using CNN
plant resistance gene miner for linking the resistance gene to genbank id.
A method for paprika disease severity assessment.
Eggplant biotic stress detection app using CNN
An Open Source Disease Detection Platform for Microimaged Plant Phenotypic Characteristics (Currently for Tomato, Cucurbit, and Grape)
Detection of disease in Plant using Plant Village dataset
Capstone project for GIZ Advance Data Science.
Plant disease detection using googlenet architecture
A Modern Indoor Plant App That Integrates AI-IOT-ML Together In One Single App
This project aims to develop a robust plant disease detection system using advanced machine learning techniques, primarily leveraging YOLO for object detection. The workflow includes data preprocessing, feature extraction, non-negative matrix factorization, fuzzy clustering, and model training.
A plant disease detection website
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