Systematic approach to validate and implement digital phenotyping tool for soybean: A case study with PlantEye

Abstract

Agriculturally beneficial soil microorganisms (biologicals) have long been recognized as a component to enhance crop growth and yield. However, efficacy and predictability of the microbes have been low because of a knowledge gap between lab-based selection and field performance. Bayer's Crop Science research group focuses on a key prerequisite to close this gap by understanding the plant physiological parameters improved by the application of several hundred biologicals as seed coatings and then selecting the best performers using desirable phenotypes at greenhouse prior to field screening. In such large scale screening efforts, the challenge lies in obtaining manual phenotyping measurements, which is both tedious and inefficient. Digital phenotyping tools were adopted to address this challenge. PlantEye (PE) laser scanner was selected as the high-throughput phenotyping technology for evaluation. Specifically, suitability of PE was evaluated in the greenhouse to screen biological seed treatments in soybean [Glycine max (L.) Merr.]. The adoption of PE digital phenotyping technology was based on successfully evaluating and establishing its comparability to a standard manual measurement using a leaf area meter (i.e., reference method). Initial study performed in soybean showed a strong correlation (0.91) between reference and PE measurements. However, an important bottleneck is defining criteria for validating the results in the context of application to a screening program. In this study, exploratory analyses were performed to evaluate effect of experimental parameters (e.g., soil type, plant location in greenhouse) on plant phenotypic variation. In addition, a standard methodology is proposed to establish the comparability between reference method and PE by formulating a quantitative approach. The approach can be applied to PE as well as similar digital phenotyping tools.

Tracking succession by means of 3D scans of plant communities in a glacier forefield to infer assembly processes

“…2). PlantEye was previously mainly applied in agronomy studies for the phenotyping of crop plant individuals and it was verified that the results of DWCP estimates correlate very well with manual measurements (Maphosa et al 2017, Manavalan et al 2021). It is equipped with an active sensor that projects a near‐infrared (940 nm) laser line vertically onto the plant canopy and captures the reflection of the laser and the reflectance in red, green, blue and near‐infrared wavelength with an inbuilt camera (Kjaer and Ottosen 2015, Maphosa et al 2017).…”

Investigating the water availability hypothesis of pot binding: small pots and infrequent irrigation confound the effects of drought stress in potato (Solanum tuberosum L.)

“…Previous experiments have found positive correlations between PlantEye measurements of leaf area and manually collected reference measurements, including in soybean, R 2 = 0.89 to 0.91 ( Manavalan et al., 2021 ); peanut, R 2 = 0.94; cowpea, R 2 = 0.93; and pearl millet, R 2 = 0.86 ( Vadez et al., 2015 ). However, these studies focussed on early plant growth to maximise the sample size.…”

Protein hydrolysates enhance recovery from drought stress in tomato plants: phenomic and metabolomic insights

“…In this study, omics technologies such as high-throughput plant phenotyping and metabolomics analysis were used to evaluate the effects of two plant-derived protein hydrolysates (PHs) on relieving drought stress on tomato plants. Phenospex platform was primarily used in plant phenotyping to validate the instrument performing phenotypic measurements ( Kjaer and Ottosen, 2015 ; Manavalan et al., 2021 ) or to study the crop response to stress such as wheat under salt stress ( Lancelot et al., 2016 ) and okra under flooding stress ( Schafleitner et al., 2021 ). In the current trial, the first goal was to investigate the relationship between the most important morpho-physiological traits recorded by the two multispectral 3D laser scanners.…”