PhD Oral Defence: The use of spectral reflectance data to assess plant stress and improve irrigation water management
PhD Oral Defence of Samuel Ihuoma, Bioresource Engineering
Early identification and prediction of plant abiotic stress is essential to ensure sustainable agricultural management. Traditional methods of estimating plant stress are destructive, labour intensive, and time-consuming. These methods often do not represent the heterogeneity of soil and crop parameters at large spatial scales. Spectral reflectance data provide near real-time and non-destructive estimates of plant stress, and account for spatial and temporal variability of crops and soil. This thesis focused on the use of spectral reflectance data to estimate plant water and nitrogen stresses to minimize crop physiological damage and improve yield of high-value vegetable crops. The results identified the Photochemical Reflectance Indices (PRI) centered at 553 nm (PRI553) for detecting water stress in bell pepper, while the PRI centered at 550 nm (PRI550) detected water stress in tomato crops. The Renormalized Difference Vegetation Index (RDVI) and Transformed Chlorophyll Absorption in Reflectance Index (TCARI) were suitable for assessing nitrogen stress in tomato crops. Also, spectral reflectance indices acquired from Sentinel-2 satellite mission were suitable for estimating crop evapotranspiration and irrigation water requirements. This thesis concludes that leaf spectral data are advantageous over conventional methods of crop stress assessment for improving irrigation water management.