Real-Time Detection of Bud Degeneration in Oil Palms Using an Unmanned Aerial Vehicle

This paper presents a novel methodology for the early detection of oil palm bud degeneration based on computer vision. The proposed system uses the YOLO algorithm to detect diseased plants within the bud by analyzing images captured by a drone within the crop. Our system uses a drone equipped with a Jetson Nano embedded system to obtain complete images of crops with a 75% reduction in time and with 40% more accuracy compared to the traditional method. As a result, our system achieves a precision of 92% and a recall of 96%, indicating a high detection rate and a low false-positive rate. In real-time detection, the system is able to effectively detect diseased plants by monitoring an entire hectare of crops in 25 min. The system is also able to detect diseased plants other than those it was trained on with 43% precision. These results suggest that our methodology provides an effective and reliable means of early detection of bud degeneration in oil palm crops, which can prevent the spread of pests and improve crop production.