@Article{cmc.2025.059245,
AUTHOR = {Ching-Lung Fan, Yu-Jen Chung},
TITLE = {Integrating Image Processing Technology and Deep Learning to Identify Crops in UAV Orthoimages},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {82},
YEAR = {2025},
NUMBER = {2},
PAGES = {1925--1945},
URL = {http://www.techscience.com/cmc/v82n2/59494},
ISSN = {1546-2226},
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 show that VARI significantly improves detection accuracy, with YOLOv3 achieving the best performance, achieving a precision of 73.77%, recall of 100%, and reduced training time by 95 seconds. Additionally, the average Intersection over Union (IoU) increased by 4%–25% across models with VARI-enhanced images. This study confirms that incorporating VARI improves the performance of deep learning models, offering a promising solution for precise crop detection in complex agricultural environments.},
DOI = {10.32604/cmc.2025.059245}
}