Phenomics for Evaluating Drought Tolerance in Different Wheat Varieties Towards Sustainable Crop Production
Creators: name: Veloo, Kesevan
Publisher: name: Washington State University
Year: 2023
Subjects: Breeding program
Crop responses
Genotype-Environment Interaction
Agriculture
Internet of Things
Machine Learning

description: Water scarcity poses a critical challenge to sustainable agriculture, necessitating innovative strategies in wheat breeding and data acquisition techniques. This is particularly relevant in the rain-fed regions of the Pacific Northwest (PNW) of the United States, a key area for wheat production. This thesis explores the integration of advanced sensor technologies with phenomic approaches to evaluate drought tolerance in diverse wheat varieties, aiming to enhance crop production efficiency and sustainability under water-limited conditions.
description: The first objective of this research focuses on the development, implementation, and validation of AGIcam+, a sensor system based on Raspberry Pi technology. Equipped with multispectral and thermal sensors, AGIcam+ complements conventional unmanned aerial vehicles (UAVs) to facilitate comprehensive crop surveillance. It emphasizes key growth stages – preheading, heading, and post-heading – under various drought stress scenarios. The analysis of multispectral and thermal data, particularly the normalized difference vegetation index (NDVI) and the 95th percentile temperature data during the heading stages, revealed significant Pearson’s correlation coefficients ranging between 0.81-0.88 and 0.81-0.95 (both P &lt; 0.01), respectively. Yield prediction models, developed using datasets from both AGIcam+ and UAVs, demonstrated the comparability of AGIcam+ in yield estimation. In the 2023 spring wheat trial, AGIcam+ yielded results closely aligned with UAVs (AGIcam+: R2 = 0.79, RMSE = 891 kg/ha; UAV: R2 = 0.86, RMSE = 719 kg/ha), showcasing its effectiveness.
description: Complementing these findings, the second objective aims to identify key digital traits closely correlated with yield. Multivariate statistical analyses, specifically additive main effects and multiplicative interaction (AMMI) and genotype main effect + genotype by environment interaction (GGE) analyses, were conducted on both winter and spring wheat varieties. This study concentrated on three significant digital traits – NDVI, green normalized difference vegetation index (GNDVI), and crop temperature obtained from UAV-based thermal imagery. When compared against yield metrics and using rankings derived from performance and stability in both wheat varieties, NDVI and GNDVI emerged as the strongest digital traits correlated with yield.
description: This thesis significantly contributes to the field of agricultural research technology, particularly in the context of wheat cultivation under drought conditions in the PNW. It establishes AGIcam+ as a precise tool for crop monitoring, comparable in its efficacy to UAV assessments. The study underscores the importance of digital traits captured through these technologies and their strong correlation with yield. By examining genotype-by-environment interactions and bridging data gaps, this research provides valuable insights for wheat breeding programs focused on developing resilient, high-yielding cultivars suitable for sustainable agriculture in water-limited environments.