Classification of Coconut Trees Within Plantations from UAV Images Using Deep Learning with Faster R-CNN and Mask R-CNN
Morakot Worachairungreung, Nayot Kulpanich, Pornperm SAE-NGOW, Kunyaphat Thanakunwutthirot, Kawinphop Anurak, Phonpat Hemwan
Abstract
Agriculture currently serves as a crucial food source for the global population. However, coconut farming, in particular, demands extensive care and maintenance. This research aims to classify coconut trees across various plantation areas utilizing deep learning techniques, specifically through Faster R-CNN and Mask R-CNN models, based on unmanned aerial vehicle (UAV) imagery. The data collected by both types of RGB UAVs was used for the classification of coconut trees in experimental plots. For the analysis process, aerial photographs obtained from unmanned aerial vehicles, merged with the principles of aerial photography measurement, were analyzed. The research findings revealed that both Faster R-CNN and Mask R-CNN were capable of effectively classifying image data. Nevertheless, to achieve higher accuracy in results, it is essential that the characteristics of the test plots closely align with each other. This study points towards the adoption of a high-resolution tool, ensuring clearer images that facilitate more accurate classification of coconut trees across extensive areas. Consequently, this could lead to more efficient management and maintenance of coconut plantations. Thus, this approach can substantially enhance the efficiency of managing coconut plantations. Doi: 10.28991/HEF-2024-05-04-02 Full Text: PDF