Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages

Plant height, as an essential phenotypic trait, is widely used in plant breeding, growth monitoring and yield forecasting. As a state-of-the-art technology for tracking diverse structural parameters non-destructively and in situ, terrestrial laser scanning (TLS) can generate three-dimensional geometric information with unprecedented accuracy, particularly with respect to the extraction of plant height. However, few studies have investigated the optimum TLS-derived metrics to retrieve plant height through the entire growth stages. Moreover, in terms of the multi-scan TLS mode, including the number and position of scanning site and scanning angular resolution, have rarely been studied in connection with plant height estimation. In this study, the measurement of plant height as a function of the number of scanning site (2, 4, 6, 8, 10), scanning position (Strategy 1: scanner at the edges of the field; Strategy 2: scanner within the field) and scan step angle (0.04 degrees, 0.06 degrees and 0.08 degrees) were assessed in field tests during the key growth stages of wheat. Specifically, comprehensive analysis was conducted on the effects of scanning number, position and angular resolution on the accuracy and efficiency of estimating wheat height. The results indicated that TLS with H-95 can be an effective alternative to monitor short crops like wheat during the entire growth stages, and the height at which wheat can be accurately detected by TLS was as low as 0.18 m, particularly at 0.42-0.9 m with the highest accuracy. Furthermore, the optimum scan step angle was 0.06 degrees, and the optimum scanning position was based on scanning from the four corners of study area (Strategy 1). Increasing the number of scanning site and angular resolution did not always improve accuracy. Overall, our results provide valuable method guidance in effectively acquiring the agricultural parameter for researchers involved in crop breeding and management strategies, and have potential for effective support of LiDAR deployment at the larger field scale to enhance agricultural production.