CocoSense: Coconut Tree Detection and Localization using YOLOv7

¹ Graduate Programs and Computer Engineering Department, Technological Institute of the Philippines Manila, 363 P. Casal St. Quiapo, Manila
² Computer Engineering Department, Technological Institute of the Philippines, Manila
³ Graduate Programs and Electrical Engineering Department, Technological Institute of the Philippines Manila, 363 P. Casal St. Quiapo, Manila
⁴ Technocore CATALYST and Advanced Batteries Center, 938 Aurora Blvd., Cubao, Quezon City

^* Corresponding author: rufo.marasigan.jr@tip.edu.ph

Abstract

Coconut farming in the Philippines often needs help with challenges in efficient tree monitoring, directly affecting its productivity and sustainability. Although prevalent, traditional methodologies, such as field surveys, exhibit labor intensiveness and potential data inaccuracy constraints. This study sought to leverage the capabilities of the YOLOv7 object detection algorithm to enhance coconut tree monitoring. Our objectives centered on (1) precise detection of coconut trees using orthophotos, (2) their enumeration, and (3) generating accurate coordinates for each tree. The DJI Phantom 4 RTK unmanned aerial vehicle (UAV) was used to capture high-resolution images of the study area in Tiaong, Quezon. Post-acquisition, these images underwent processing and annotation to generate datasets for training the YOLOv7 model. The algorithm's output shows a remarkable 98% accuracy rate in tree detection, with an average localization accuracy of 86.30%. The results demonstrate the potential of YOLOv7 in accurately detecting and localizing coconut trees under diverse environmental conditions.