Evaluating Sensor Fusion and Flight Parameters for Enhanced Plant Height Measurement in Dry Peas

Abstract

Plant height is an important trait for evaluating plant lodging, drought, and stress. Standard measurement techniques are expensive, laborious, and error-prone. Although UAS-based sensors and digital aerial photogrammetry have been tested on plants with an erect growth habit, further study is needed in the application of these technologies to prostrate crops such as dry peas. This study has compared the performance of LiDAR, RGB, and multispectral sensors across different flight configurations (altitudes, speeds), and image overlaps over dry pea plots to identify the optimal setup for accurate plant height estimation. Data were assessed to determine the effect of sensor fusion on plant height accuracy using LiDAR’s digital terrain model (DTM) as the base layer, and digital surface models (DSMs) generated from RGB and multispectral sensors. All sensors, particularly RGB, tended to underestimate plant height at higher flight altitudes. However, RMSE and MAE values showed no significant difference, indicating that higher flight altitudes can reduce data collection time and cost without sacrificing accuracy. Multispectral and LiDAR sensors were more sensitive to changes in flight speed than RGB sensors; However, RMSE and MAE values did not vary significantly across the tested speeds. Increased image overlap resulted in improved accuracy across all sensors. The Wilcoxon–Mann–Whitney test showed no significant difference between sensor fusion and individual sensors. Although LiDAR provided the highest accuracy of dry peas height estimation, it was not consistent across all canopy structures. Therefore, future research should focus on the integrating machine learning models with LiDAR to improve plant height estimation in dry peas.

Code and data availability

The supplied blocks describe UAS-based plant height phenotyping of dry peas (LiDAR, RGB, multispectral sensors, ground-truth measurements, ArcGIS/Pix4D processing, statistical analyses), but contain no public phenotype dataset, imagery, author analysis code, trained models, or supplement with an availability statement.

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