Abstract
A LiDAR-based high-throughput phenotyping (HTP) system was developed for cotton plant phenotyping in the field. The HTP system consists of a 2D LiDAR and an RTK-GPS mounted on a high clearance tractor. The LiDAR scanned three rows of cotton plots simultaneously from the top and the RTK-GPS was used to provide the spatial coordinates of the point cloud during data collection. Configuration parameters of the system were optimized to ensure the best data quality. A height profile for each plot was extracted from the dense three dimensional point clouds; then the maximum height and height distribution of each plot were derived. In lab tests, single plants were scanned by LiDAR using 0.5° angular resolution and results showed an R2 value of 1.00 (RMSE = 3.46 mm) in comparison to manual measurements. In field tests using the same angular resolution; the LiDAR-based HTP system achieved average R2 values of 0.98 (RMSE = 65 mm) for cotton plot height estimation; compared to manual measurements. This HTP system is particularly useful for large field application because it provides highly accurate measurements; and the efficiency is greatly improved compared to similar studies using the side view scan.
Highlights
Geometric characteristics of crops have great scientific value for crop breeders and geneticists [1,2]
When the plants have not germinated with the ground base level when the the plants fully have developed at a maximum height of 1623 mm
This angular resolution would plants fully have developed at a maximum height of 1623ofmm
Summary
Geometric characteristics of crops (height, crown size, and volume) have great scientific value for crop breeders and geneticists [1,2]. These characteristics are useful for quantitative analysis of genotype–environment interactions, which is essential for increasing crop performance [3,4,5], and for improving crop management strategies such as fertilization, irrigation, and optimization of harvesting [6,7,8]. Manual measurements of crop traits, which are still often used in practical phenomic applications, have significant limitations and drawbacks These methods are time-consuming and labor intensive, which inevitably increases cost [9,12]. Manually obtained measurements are subject to human error due to fatigue and distractions during data collection
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