This project was created in SDAIA T5 bootcamp with:
- Yazeed Alshurify @Yazeedssh
- Lena Alenazi @lenaAlenazi
- Jawaher Alfaifi
The increasing prevalence of plant diseases is a major concern for global agricultural production. Plant diseases cause significant production losses, estimated to reduce farmers’ production by up to 70%. Farmers lose an estimated $220 billion annually due to lost agricultural production caused by plant diseases. Addressing this challenge requires rapid and accurate disease detection methods to reduce these losses.
Current methods for detecting and treating plant diseases are time-consuming, require a lot of manual labor, and can lead to delays in treatment processes or (ineffective) treatment of infected plants. As a result, farmers face significant economic losses and challenges in maintaining agricultural productivity. In contrast, automated plant disease detection robots can help reduce these losses by quickly and accurately detecting plant diseases in agricultural fields.
Building a robot capable of improving agricultural operations through the use of modern technologies. The robot moves in a specific path (Line follower robot) and contains a microprocessor (Raspberry-pi) and is equipped with a camera (Raspberry-pi cam) to monitor plants in agricultural fields. Through the camera, we can analyze and recognize images of plants (damaged plant - undamaged plant). Deep learning techniques (pre-trained model) are used, which helps in detecting signs of damage or disease in plants, and the damaged or diseased plant will be automatically treated with the treated materials, which helps in increasing agricultural productivity and improving the quality of crops in farms and agricultural fields.