Abstract
BackgroundPlant height is an important morphological and developmental phenotype that directly indicates overall plant growth and is widely predictive of final grain yield and biomass. Currently, manually measuring plant height is laborious and has become a bottleneck for genetics and breeding programs. The goal of this research was to evaluate the performance of five different sensing technologies for field-based high throughput plant phenotyping (HTPP) of sorghum [Sorghum bicolor (L.) Moench] height. With this purpose, (1) an ultrasonic sensor, (2) a LIDAR-Lite v2 sensor, (3) a Kinect v2 camera, (4) an imaging array of four high-resolution cameras were evaluated on a ground vehicle platform, and (5) a digital camera was evaluated on an unmanned aerial vehicle platform to obtain the performance baselines to measure the plant height in the field. Plot-level height was extracted by averaging different percentiles of elevation observations within each plot. Measurements were taken on 80 single-row plots of a US × Chinese sorghum recombinant inbred line population. The performance of each sensing technology was also qualitatively evaluated through comparison of device cost, measurement resolution, and ease and efficiency of data analysis.ResultsWe found the heights measured by the ultrasonic sensor, the LIDAR-Lite v2 sensor, the Kinect v2 camera, and the imaging array had high correlation with the manual measurements (r ≥ 0.90), while the heights measured by remote imaging had good, but relatively lower correlation to the manual measurements (r = 0.73).ConclusionThese results confirmed the ability of the proposed methodologies for accurate and efficient HTPP of plant height and can be extended to a range of crops. The evaluation approach discussed here can guide the field-based HTPP research in general.
Highlights
Plant height is an important morphological and developmental phenotype that directly indicates overall plant growth and is widely predictive of final grain yield and biomass
From the results the LIDAR-Lite v2 (LL2) sensor provided, it could be further evaluated whether a light detection and ranging (LiDAR) with a higher sampling rate and a potential 3D point cloud of the canopy structure can be used for extracting more accurate plant height
Using the data collection approaches and the data processing methods introduced in this study, we found the plot-level height values measured by the ultrasonic sensor, the LL2 sensor, the Kinect camera, and the proximal imaging by four digital single-lens reflex (DSLR) cameras were all highly
Summary
Plant height is an important morphological and developmental phenotype that directly indicates overall plant growth and is widely predictive of final grain yield and biomass. The goal of this research was to evaluate the performance of five different sensing technologies for field-based high throughput plant phenotyping (HTPP) of sorghum [Sorghum bicolor (L.) Moench] height. As plant height is conventionally measured using measuring sticks in the field, this manual data collection is laborious and timeconsuming, not scalable for large field experiments or many repeated measures at high temporal resolution. Due to these major challenges, proximal sensing technologies become a practical solution to implement HTPP of plant height
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