AI for pest detection

Ho, Jun Han (2025) AI for pest detection. Final Year Project, UTAR.

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Abstract

Agricultural pest management is critical for ensuring crop health and yield, yet traditional detection methods are often labor-intensive and imprecise. This Final Year Project proposes an Artificial Intelligence (AI) based pest detection system integrated into a mobile application, empowering users to monitor plant health efficiently. The system utilizes a convolutional neural network (CNN) specificallyYOLOv5, trained on a dataset encompassing three common pest categories tentatively Whiteflies, and caterpillar, alongside healthy plant samples. Through the mobile app, users capture images of plants using their smartphone camera. The AI model, running on-device through TensorFlow Lite, analyzes the image to classify the plant as healthy or unhealthy. If unhealthy, it identifies the specific pest type and provides a tailored solution. Results are displayed within the app, and the detection data including plant status, pest type, solution, and confidence score are encoded into a QR code. This QR code enables seamless data sharing, such as with agricultural experts or record-keeping systems. Preliminary testing on a diverse test set achieved an accuracy above 85%, validating the system’s effectiveness. This mobile solution offers a portable, user-friendly tool for pest management, enhancing precision agriculture through AI and innovative data transfer. Area of Study: Artificial Intelligence and Application Development Keywords: Agriculture, Pest Detection, Pest Control, Classification, Mobile Application and Deep Learning,

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