🌱 This project aims to automate plant health monitoring using computer vision and deep learning. It focuses on accurate disease detection and classification in plants through rigorous data preprocessing and robust model selection.

:seedling: PMDDS - Intelligent IoT-based plant health monitoring and disease detection system.

Plants health monitoring through iot and plants disease detection using machine learning in flutter

🍃 Production-ready: Just upload a photo of any plant or crop, the system takes care of the rest. Powered by advanced object detection and Multi-AI Agents, it identifies over 100+ species and autonomously fetches detailed insights like scientific name, history, health benefits and risks, ideal growing seasons, market prices, etc.🌾

Green Stems is an application built with AI capabilities for disease detection, health detection, flower species identification and soil identification for better care and nourishment of our plants.

AI-powered plant disease detection system using computer vision, deep learning, and ESP32-CAM for real-time crop health monitoring and analysis.

Digifarmer is a Flutter-based app providing AI-driven plant disease detection, health diagnostics, weather forecasts, and agricultural news. It helps farmers enhance decision-making, optimize crop management, and boost productivity.

Building React Native app with Firebase for full-stack capabilities. Features: User Auth, Real-time DB, Google Maps for Ambulance Booking, SOS, Plant Dictionary, ML for disease detection. End-to-end app development, prioritizing health and safety needs.

An AI-powered Crop Disease Detection Web App built with Flask and TensorFlow that identifies plant diseases from leaf images with high accuracy. Empowering farmers and researchers through deep learning–based plant health insights.

It is observed that a lot of crop gets wasted every year due to the illness spreading (reasons can be pests, bacterial or fungal infections, etc.) in the farms. Farmers face huge losses because of the lack of a handy and economical technology that can detect such diseases in the initial stages of occurrence. This system can prove a remedy to this issue by developing a prototype that recognizes the same at an early stage and thence providing them a way to cure it using appropriate pesticides, insecticides, etc. �The aim is to build a deployable system for plants’ disease detection by training a Convolutional Neural Network model that traces a particular disease in a particular plant species. It detects the plant disease by scanning the leaves of the species as it is trained for those illness occurring on the leaves.Leaf plays a major role in providing important information related to the health of a plant. This method is based on a technique that scans the leaf sample of the infected plant. These scanned samples will then be processed and the model will be trained to classify the diseases accordingly�. �

A user-friendly app to help connect Consumers and Farmers directly by eliminating the need of middlemen. Enhanced with Gordon - our AI ChatBot trained to give recipe suggestions, nutritional facts and everything related to food. Plant Health Detection system helps farmers monitor their crop health using CNN.

An AI-powered Crop Disease Detection Web App built with Flask and TensorFlow that identifies plant diseases from leaf images with high accuracy. Empowering farmers and researchers through deep learning–based plant health insights.

A comprehensive dataset of rice leaf images for disease classification tasks, designed for agricultural computer vision applications focusing on rice plant health monitoring and disease detection.

Detecting Diseases In Plants Is a Critical Step In Maintaining Healthy Crops And Ensuring Agricultural Productivity. It Involves Identifying Abnormal Changes In Plant Health Caused By Pathogens, Environmental Stress, Or Nutrient Deficiencies. Accurate Disease Detection Allows Timely Intervention, Reducing Potential Crop Losses

An intelligent AIoT system for plant health monitoring. Leverages ESP32 for environmental data collection, FastAPI & YOLO/CNN for tomato leaf disease detection, and a React-based Web Dashboard for real-time tracking.

This project is an automated agricultural environment management system built on the Raspberry Pi hardware platform. It integrates air sensors, soil sensors, and rain detection to provide a holistic view of plant health.

The Health Diagnosis Plugin is a cutting-edge WordPress plugin that integrates an AI-driven plant disease detection system with a modern Vue.js frontend. Utilizing Naive UI components and TypeScript, this plugin offers a seamless and user-friendly experience. Its PHP-based backend ensures robust WordPress compatibility, enabling users to embed.

Detection of Potato & Tomato plant healthiness by rectifying the plant's diseased & healthy leaf images using CNN and Mask RCNN approach.

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Carbomato is a plant health detection app that lets users to check the health of their plants by taking a photo of a leaf or uploading it from their gallery.

Plant disease identifying applications aim to help farmers, gardeners, and anyone interested in plant health to: * Early Detection: Identify diseases at their initial stages, allowing for timely intervention and preventing further spread.

This project leverages YOLOv8, a state-of-the-art object detection algorithm, to accurately identify and classify tomato plant diseases. By utilizing advanced deep learning techniques, this solution aims to aid farmers and agriculturists in early detection and management of plant diseases, thereby improving crop health and yield.

feasibility and practicality of using CNNs on a Raspberry Pi for on-site plant disease detection, providing farmers with an accessible and efficient tool to manage crop health effectively.

Leaf Algae Detection is a mobile application designed to help users identify plant diseases, specifically algae infections, using artificial intelligence. The app leverages machine learning models trained with Google’s Teachable Machine and TensorFlow to recognize various plant health issues through leaf images.

Plant Disease Detection is a web-based app using React for the frontend and Python (FastAPI, PyTorch) for the backend. It employs a ResNet-based model to identify plant diseases from images, offering farmers and researchers real-time, accurate diagnostics for improved crop health management.

AgroAid is a smart AI + IoT-based crop health monitoring and disease management system designed for farmers. It combines image-based plant disease detection using deep learning with real-time soil and weather monitoring through IoT sensors

The Plant Disease Detection System uses ML to identify crop diseases from leaf images. By analyzing features like color, shape, and texture with CNN models, it provides accurate diagnosis and treatment suggestions. Integrated with IoT sensors, it offers real-time crop health insights for smarter, sustainable farming.

plant health object detection model

Detecting plant health using neural networks.