Automatic Detection of Oil Palm Growth Rate Status with YOLOv5

Abstract

Oil palm plantations are essential for Indonesia as a source of foreign exchange and a provider of employment opportunities. However, large-scale land clearing is considered a cause of deforestation, which harms the environment and society. So, it is necessary to manage plantations that are sustainable and still maintain the preservation of forests and biodiversity. One solution is to apply remote sensing technology. The research was conducted to develop a multi-class detection method for the growth rate of oil palm trees, with five categories: healthy palm, dead palm, yellowish palm, mismanaged palm, and smallish palm. The deep learning-based object detection method, YOLO Version 5 (YOLOv5), is used. This study compares the YOLOv5 network models, namely YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. Parameter setting is also carried out in the BCE (Binary Cross Entropy) with Logits Loss Function to handle the problem of unbalanced data distribution in each class. The YOLOv5 model with the highest mAP value is the YOLOv5l and YOLOv5x, the YOLOv5x requires longer training time. In this study, hyperparameter optimization was also carried out using hyperparameter evolution techniques. However, it has yet to provide increased results because the experiments conducted in this study are still limited.