Performance Evaluation of Deep Learning Algorithms for Young and Mature Oil Palm Tree Detection

Oil palm trees one of the most essential economic crops in Malaysia have an economic lifespan of 20-30 years. Estimating oil palm tree age automatically through computer vision would be beneficial for plantation management. In this work, the object detection technique is proposed by applying high-resolution satellite imagery, tested with four different deep learning architectures, namely SSD, Faster R-CNN, CenterNet, and EfficientDet. The models are trained using TensorFlow Object Detection API and assessed with performance metrics and visual inspection. It is possible to produce automated oil palm trees detection model on age range estimation, either young or mature based on the crown size. Faster R-CNN is identified as the best model with total loss of 0.0047, mAP of 0.391 and mAR of 0.492, all with IoU threshold from 0.5 to 0.95 with a step size of 0.05. Parameter tuning was done on the best model and further improvement is possible with the increasing batch size.