Yolo-Drone: Detection Paddy Crop Infected Using Object Detection Algorithm Yolo and Drone Image - Masykur, Fauzan; Prasetyo, Angga; Zulkarnain, Ismail Abdurrozaq; Kumalasari, Ellisia; Utomo, Pradityo (JOIV : International Journal on Informatics Visualization, 2025)
Crop failure is an undesirable result of rice planting for every farmer because it disrupts the economic stability of the family. One of the factors of crop failure in the rice planting process is the disease attack factor, which causes infection. Infected plants will interfere with the growth of rice, not optimally, because the green leaf substance, which is key to processing sunlight's nutrients, is unable to function. After all, it is covered by infection. Infection in the leaves covers the green leaf substance, or chlorophyll, so that the leaves are unable to absorb nutrients from sunlight. This problem is a separate concern in overcoming rice plant infections, which will result in crop failure. This paper discusses the detection of infected rice plants, particularly leaf infections, using drone camera images. Unmanned aircraft, also known as drones, fly above rice fields to capture images of rice plants, which are then used as datasets in training models to detect infected and healthy rice plants. The detection of disease presence in rice leaves is carried out using the You Only Look Once version 8 (YOLOv8) object detection algorithm, with a model trained using Google Colab Pro+. The results of training the model to detect healthy and infected plant leaves are the primary objectives of this study. The YOLOv8 object detection model, when applied to detect rice plants with two classes (healthy and infected), shows quite good results. This is indicated by the recall, precision, and F1-score values (0.99, 0.814, 0.90) approaching 1 in all classes.