|SANKARAN, SINDHU - Washington State University
|ZHOU, JIANFENG - University Of Missouri
|KHOT, LAV - Washington State University
|TRAPP, JENNIFER - Washington State University
|MNDOLWA, ENINKA - Washington State University
|Miklas, Phillip - Phil
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/29/2018
Publication Date: 6/6/2018
Citation: Sankaran, S., Zhou, J., Khot, L., Trapp, J., Mndolwa, E., Miklas, P.N. 2018. High-throughput field phenotyping in dry bean using small unmanned aerial vehicle based multispectral imagery. Computers and Electronics in Agriculture. 151:84-92. https://doi.org/10.1016/j.compag.2018.05.034.
Interpretive Summary: Dry beans are an important food crop globally. They provide protein, fiber, nutrients, and promote health benefits in human diets. Dry bean production is sensitive to drought, low soil fertility and other stresses. Our goal is to use plant breeding to develop dry beans that are more tolerant to abiotic stresses. Plant responses to drought and other stresses are difficult to measure. High throughput phenotyping of stress tolerance is needed. This study investigates the use of unmanned aerial vehicles equipped with cameras that easily capture multispectral images of thousands of drybean lines at a time which may then be useful for predicting health and vigor of individual lines. High correlations between actual performance based on seed yield and estimated performance based on multispectral image data were obtained which suggests that UAVs can be used to collect data that will facilitate selection and development of dry beans with enhanced stress tolerance. Further validation of this new tool for high throughput phenotyping dry bean lines for tolerance to stresses is under investigation.
Technical Abstract: Phenotyping traits in large field crop trials with numerous breeding lines is an arduous task. Unmanned aerial vehicle (UAV) based remote sensing is currently being investigated for high-throughput agricultural field phenotyping applications. The system is conducive for rapid assessment of crop response to the environment, at a desired spatial and temporal resolution. Our objective was to evaluate UAV-based multispectral remote sensing towards monitoring the response of dry bean lines to drought and low nitrogen stress (i.e. two trials and two seasons) under field conditions. A semi-automated image processing protocol was developed to extract image features such as: (i) average green normalized difference vegetation index (GNDVI); and (ii) canopy area (total number of plant pixels) from individual plots. The data were acquired at mid pod fill and late pod fill growth stages in 2014 season, and at flowering, mid pod fill, and late pod fill growth stages in 2015 season. The relationships between remotely sensed image features with that of crop response variables such as seed yield, days to flowering, days to harvest maturity, days to seed fill, and biomass rating (for drought trial only) were assessed temporally. In general, in drought experiment, both average GNDVI and canopy area were significantly correlated with seed yield in all trials. The GNDVI and canopy area at flowering growth stages and GNDVI at mid pod fill stage were consistently highly correlated (r > 0.73) with seed yield. GNDVI at flowering (r of -0.54 to -0.73) and mid pod fill (r of -0.52 to -0.73) stages was highly correlated with biomass rating. Thus, this image feature (GNDVI) can be useful as a viable phenotype for capturing biomass differences as well. A pilot thermal imaging of the sample breeding plots in drought trials also indicated its potential in capturing the temperature differences resulting from drought stress. For the nitrogen stress experiment, the correlations between remotely sensed image features and response variables were lower than in the drought experiment. These low correlations result, in part, from a plant physiological response to nitrogen stress that did not induce differential vegetative growth of the canopy, thus influencing image features.