Ching-Lung Fan1,*, Yu-Jen Chung2
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1925-1945, 2025, DOI:10.32604/cmc.2025.059245
- 17 February 2025
Abstract This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle (UAV) imagery by integrating the Visible Atmospherically Resistant Index (VARI) with deep learning models. The primary challenge addressed is the detection of bananas interplanted with betel nuts, a scenario where traditional image processing techniques struggle due to color similarities and canopy overlap. The research explores the effectiveness of three deep learning models—Single Shot MultiBox Detector (SSD), You Only Look Once version 3 (YOLOv3), and Faster Region-Based Convolutional Neural Network (Faster RCNN)—using Red, Green, Blue (RGB) and VARI images for banana detection. Results More >