Agriculture Robot Using Image Processing

Background: Agriculture is a cornerstone of the economy, but traditional farming is labor-intensive and time-consuming. Advances in technology, such as Convolutional Neural Networks (CNNs) and robotics, can help address challenges like disease detection, weed control, and labor shortages. Tomatoes, a key crop, benefit from proper management using these innovations. Objective: This project aims to design and develop an agriculture robot using IoT and Machine Learning to automate farming tasks, detect tomato plant diseases, and manage weeds more efficiently. Methods: The robot is controlled by an ESP32 microcontroller connected via Wi-Fi for real-time monitoring. The system uses CNNs for disease detection in tomato plants, along with a custom-trained dataset using the TensorFlow framework. Mechanical arms perform tasks like seeding, watering, spraying fertilizers, and harvesting. A user interface for disease detection was designed for real time disease detection. Results: The model achieved an accuracy of 95.0% for disease detection, 95.56% for fruit detection, and 95.14% for plant detection. These results highlight the effectiveness of the robot in managing tomato crops. Conclusion: The agriculture robot provides a comprehensive solution to key farming challenges, enhancing efficiency through automation and improving crop health monitoring. The integration of IoT and ML offers a pathway toward sustainable and profitable agriculture, particularly in managing tomatoes. Further improvements and scaling could expand its application to other crops and farming tasks. Keywords: Machine Learning, Internet of Things, Agricultural Robot, Disease Detection, Convolutional Neural Networks, Mechanical Tasks.