Unmanned Aerial Vehicle Image Data-Driven Detection of Tobacco Plant Height

Abstract

The study and application of high-throughput plant height data acquisition technology for tobacco crops are limited. A tobacco plant height detection method based on deep learning and UAV tilting photography is proposed to address this. Firstly, tilting photography by drone is used to obtain high-throughput plant height data, and a 3D model of plants within the region is generated to extract elevation information. Then, an improved YOLOv5 algorithm with a CBAM attention mechanism embedded in the C3 module of the backbone network is proposed. This algorithm is designed to reduce shallow noise information, enhance the detection capability of small and dense targets, and achieve target detection of plants in the region. This will result in estimated plant heights for each plant. The experimental results show that the CBAM-YOLOv5 model significantly improves the recognition effect, increasing the AP value for tobacoo crop recognition to 98.04%. Compared with the original YOLOv5 model, the AP value of our model has increased by 0.7%. The plant target detection has a high correlation between the estimated values calculated from the 3D model and the measured values, with a linear slope of 0.9544 and R2-value of 0.4971, achieving the detection of high-throughput plant height data for tobacco crops.