Deep Learning for Medium-Scale Agricultural Crop Detection through Aerial View Images

Abstract

This research project focuses on utilizing two state-of-the-art YOLOv4-based deep learning models, for large-scale agricultural crop detection using Unmanned Aerial Vehicles (UAVs). The objective is to develop an accurate and efficient crop detection system capable of identifying chili crops, eggplant crops, and empty polybags in agricultural fields. Crops detection is important for the development of a robotic vision in maximize the productivity and efficiency in agriculture associate with the development of concept Industry 4.0. This study seek to explore the comparison between YOLOv4 and YOLOv4 tiny model in term of mean average precision (mAP), precision, recall, F1-score, detection time and memory consumption. A custom dataset with 300 images was collected and annotated into total bounding boxes of 23335 with 6969 chili tree, 15402 eggplant tree and 964 empty polybag. The dataset was separated into train, validation and test set with the ratio of 70:20:10. The dataset was trained into YOLOv4 and YOLOv4 tiny with 2000 iterations. The result has shown that the YOLOv4 has the higher mean AP of 91.49% with 244.2mb memory storage consumption while YOLOv4 tiny achieve lower mean AP of 71.83% with 22.4mb. In summary, this research has significated the implementation of deep learning models to perform large-scale agricultural crop detection and can be further develop into automation industrial 4.0 of local agricultural sector.