Drone Based System for Plant Monitoring and Data Collection using Computer Vision. (Yolov4, DeepSORT, OpenCV)

No description available

A.I-based drone system that detect, monitors plant Diseases/Health and provides pest control solutions and spraying.

No description available

A drone-based system for capturing and analyzing aerial data to monitor plant health and detect diseases. Features include high-resolution imaging, machine learning for classification, and real-time data visualization. Ideal for precision agriculture and environmental monitoring, with modular and scalable design for customization.

Drone-based Plant Health Detection Using Computer Vision and Machine Learning A system that uses drone-captured images and color-based segmentation combined with machine learning to identify dried or diseased plants in agricultural fields for improved crop monitoring and management.

This project leverages computer vision and deep learning to detect plant diseases from drone-captured images, helping improve agricultural monitoring and diagnosis. The system utilizes a Convolutional Neural Network (CNN) model to classify diseases in plants, with a user-friendly interface and web-based deployment.

Autonomous drone-based precision agriculture system using Pixhawk 2.4.8 and Raspberry Pi 3B+. Implements MobileNetV2 for real-time weed classification and U-Net for plant disease segmentation (future deployment), enabling intelligent crop monitoring and scalable smart farming solutions.

Crop disease classification using a CNN trained on the PlantVillage dataset. The project covers data preprocessing, model training, evaluation, and prediction. It provides a simple and lightweight baseline for plant disease detection, with potential applications in drone-based and real-world agricultural monitoring systems.