ECTI TRANSACTIONS ON COMPUTER INFORMATION TECHNOLOGYVolume 15, No. 01, Month APRIL, Year 2021, Pages 13 - 23
Rubber tree tapping position detection on trunkcovered rgb-d images for automation platform
Rattachai Wongtanawijit, Thanate Khaorapapong
Abstract Download PDFRecently, an autonomous vehicle with 3D sensors for the rubber tree (Hevea brasiliensis) plantation navigation has been presented. Therefore, we present a machine vision for detecting the tapping position and the rubber juice collecting cup in the images, which can be deployed onto an autonomous platform. Firstly, we show an RGB-D image acquisition technique using artificial lights for capturing a rubber tree in the low-light. Then, we present two tapping position detection algorithms which are the color-feature based with sliding window algorithm and the novel deep object detector. To perform the detection on our custom dataset, we build a Faster-RCNN with the pre-trained MobileNetV2 using the fine-tuning technique. The results show that the deep detector outperforms our conventional detector which gives 0.92 average precision on our dataset.
Object Detection, Hevea brasiliensis, Image Acquisition, Rubber Tapping, Computer Vision