Integrating High Throughput Phenotyping Approaches to Characterize Plant Traits and Support Nutrient Management Research

Over the past decade, considerable research has addressed the social, economic, and agronomic challenges posed by imbalanced nutrient applications, particularly nitrogen and, more recently, sulfur. In response, high throughput plant phenotyping, as one of the latest approaches, has contributed to the development of innovative techniques for assessing key plant phenotypes under stress over time in both field and greenhouse environments. However, there is a limited number of studies applying these techniques for nutrient management, particularly in scenarios where two abiotic stressors coexist, such as the combination of water stress and nitrogen deficiency, or concurrent nitrogen and sulfur deficiencies. In chapter 2, high throughput plant phenotyping is leveraged to assess current nutrient management practices in rainfed and irrigated maize production for the field seasons of 2022 and 2023. Multimodal datasets from the NU-Spidercam Field Phenotyping facility site revealed a significant association between irrigation and improved crop productivity in the 2022 field season (ρ < 0.05). Interestingly, in the 2023 field season, the observed relationships between irrigation and crop productivity were inconsistent due to environmental factors that critically influenced the establishment of the plant populations. This study’s innovative approach highlights the value of advanced remote sensing and plant phenotyping in enhancing trait evaluation for more efficient nitrogen utilization. In chapter 3, high throughput plant phenotyping is used to investigate the interaction between nitrogen and sulfur levels in maize grown at the at the Greenhouse Innovation Center in Lincoln, Nebraska, (United States). Advanced imaging techniques were performed using visible and hyperspectral images to generate phenotypic time series and identify spectral features. Plant height and biomass accumulation revealed dynamic growth patterns, emphasizing a shift in resource allocation. Notably, integrating canopy location to extract the first derivative from hyperspectral images yielded initial insights for advancing nutrient-based stress detection (ρ < 0.05), considering where chlorosis develops due to nutrient deficiency. Collectively, these studies showcase novel applications of high throughput plant phenotyping to enhance nutrient management strategies in maize systems. Adviser: Joe D. Luck