Comparative Analysis of Deep Learning Models for Banana Plant Detection in UAV RGB and Grayscale Imagery

Efficient banana crop detection is crucial for precision agriculture; however, traditional remote sensing methods often lack the spatial resolution required for accurate identification. This study utilizes low-altitude Unmanned Aerial Vehicle (UAV) images and deep learning-based object detection models to enhance banana plant detection. A comparative analysis of Faster Region-Based Convolutional Neural Network (Faster R-CNN), You Only Look Once Version 3 (YOLOv3), Retina Network (RetinaNet), and Single Shot MultiBox Detector (SSD) was conducted to evaluate their effectiveness. Results show that RetinaNet achieved the highest detection accuracy, with a precision of 96.67%, a recall of 71.67%, and an F1 score of 81.33%. The study further highlights the impact of scale variation, occlusion, and vegetation density on detection performance. Unlike previous studies, this research systematically evaluates multi-scale object detection models for banana plant identification, offering insights into the advantages of UAV-based deep learning applications in agriculture. In addition, this study compares five evaluation metrics across the four detection models using both RGB and grayscale images. Specifically, RetinaNet exhibited the best overall performance with grayscale images, achieving the highest values across all five metrics. Compared to its performance with RGB images, these results represent a marked improvement, confirming the potential of grayscale preprocessing to enhance detection capability.