Project Description: Yellow Leaf Disease Detection and Autonomous Aerial Spraying Mechanism for Arecanut
This project tackles yellow leaf disease in arecanut plantations by integrating AI-driven leaf classification with drone-based autonomous pesticide spraying. A deep learning ResNet-50 model, trained on categorized areca leaf images, detects disease in real time. When detection occurs, an ESP32-CAM setup activates a spraying mechanism targeting only infected areas, minimizing chemical waste and labor.
Hardware includes a 12V DC pump, a relay-controlled circuit, a custom nozzle for even pesticide distribution, and CAD-designed mounts. The spraying mechanism is drone-compatible, lightweight, and weather-resistant. The solution achieves high classification accuracy (99.35%) and was designed with environmental and economic sustainability in mind.
The entire system was validated through field trials and is adaptable to other crops. The innovation is backed by a patent application and selected for IEEE ICRASET 2024 presentation.
📘 IEEE Publication: Read the Paper on IEEE Xplore