Plant diseases causes many significant damages and losses in crops around the world. Some suitable measures on disease identification should be introduced to prevent damages and minimize losses. Early Detection of Disease helps in increasing the crop productivity as well as in minimizing expense. Technical approaches using machine learning and computer vision are actively researched to achieve intelligence farming by early detection on plant disease. The accuracy of object detection and recognition systems has been drastically improved by the recent development in Deep Neural Networks. By using these systems and implementation of computer vision and machine learning techniques, plant diseases can be detected. Here we have used transfer learning based approach to diagnose diseases of different plants using its images captured by camera devices either drone or smartphone. Our goal is to build a market oriented product for Plant Disease Detection, a smartphone app compatible with both smartphone camera and drone camera. The target group of the user is those who request a quick diagnosis on common leaf disease at any time of the day i.e. Farmers, agricultural industries, agricultural consultants and Government Agencies & Departments.

AI-powered crop disease detection analyzes plant images to quickly identify infections like blight or rust, enabling timely treatment. Using smartphone apps or drones, it helps farmers reduce losses and improve yields sustainably. This tech makes farming smarter and more efficient.

IntelliAgro Drone is a final year project that uses AI and a drone to help farmers detect crop diseases and spray only the affected plants. It uses the YOLO deep learning model for real-time detection and an ESP32-based system to control spraying. The drone streams live video to a web dashboard where detections are shown.

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.

Final Year Project - Disease Detection in cotton Plant using drone(Used different Machine learning algorithms)

i am a mechatronics engineer. This my final year project titled 'Disease Detection Potato Plant using Drone Imagery

An AI-Driven Platform for Real-Time Plant Disease Detection and Management Using Image Analysis and Drone-Based IoT Solutions

An AI/ML-based Plant Disease Detection System uses machine learning to identify diseases in plants. It works by analyzing images of plant leaves or other parts, typically taken by cameras or drones. The system uses trained models to recognize patterns and symptoms of various diseases, helping farmers detect issues early.

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.

An AI-driven crop disease detection system uses artificial intelligence and machine learning to identify plant diseases at an early stage by analyzing images of crop leaves, stems, or fruits. The system processes visual data captured through smartphones, drones, or cameras and compares it with trained models to accurately detect disease symptoms.

Developed an intelligent drone-based system using a pre-trained CNN model and Python for real-time image capture, processing, and early detection of mango plant diseases. This system achieved accurate and efficient disease classification, enabling timely preventive measures in agricultural practices.

IntelliAgro Drone is a final year project that uses AI and a drone to help farmers detect crop diseases and spray only the affected plants. It uses the YOLO deep learning model for real-time detection and an ESP32-based system to control spraying. The drone streams live video to a web dashboard where detections are shown.

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.