Low-cost Sensor and Deep Learning-based Methodology to Spray the Farms with Controlled or Limited Pesticides with Equal Distribution

Traditional pesticide spraying methods often result in excessive chemical usage and uneven distribution, which increases cost and harms the environment. This project proposes a Low-Cost Sensor and Deep Learning-based Methodology to ensure controlled and uniform pesticide spraying across farms. A camera captures real-time field images, and a lightweight deep learning model identifies crop areas that actually require spraying. Based on this detection, sensor-driven spray nozzles automatically adjust the pesticide flow using flow and pressure sensors to maintain equal distribution. The system applies pesticides only where needed, reducing wastage and improving efficiency. By using inexpensive hardware and optimized algorithms, the solution remains affordable for farmers while supporting precision agriculture. This methodology provides a practical and eco-friendly approach for achieving uniform, controlled, and cost-effective pesticide application