Plant recognition and counting of Amorphophallus konjac based on UAV RGB imagery and deep learning

Abstract

Quantifying the number of Amorphophallus konjac (Konjac) plants can provide valuable insights for yield prediction. Early monitoring of the plant population facilitates timely adjustments in cultivation practices, ultimately leading to improved productivity of Konjac. The majority of research employed deep learning (DL) for plant counting using original images derived from unmanned aerial vehicle (UAV) or ground-based platforms, but this method may lack adaptability to different scenarios and face challenges in achieving plant counting over large areas. This study systematically evaluated the performance of UAV-based original images, the generated orthomosaic, and the combination of both for the detection and counting of the Konjac plant. We proposed an innovative approach by integrating three Convolutional Block Attention Modules (CBAM) into YOLOv5 and utilizing the combined dataset of original images and orthomosaic, which exhibited the highest accuracy performance in Konjac plants recognition (Precision = 94.3 %, Recall = 96.0 %, F1-Score = 95.1 %). Our findings illustrate that the orthomosaic generated from original images acquired via UAV outperformed individual original images in terms of accuracy for counting Konjac plants across expansive areas. This study provides new insight into the recognition and counting of various crop plants across large-scale regions, presenting a practical and efficient approach.