The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)

This study was conducted to evaluate the feasibility of a mobile phone-based thermal and UAV-based multispectral imaging to assess the irrigation performance of African eggplant. The study used a randomized block design (RBD) with sub-plots being irrigated at 100% (I100), 80% (I80) and 60% (I60) of the calculated crop water requirements using drip. The leaf moisture content was monitored at different soil moisture conditions at early, vegetative and full vegetative stages. The results showed that, the crop water stress index (CWSI) derived from the mobile phone-based thermal images is sensitive to leaf moisture content (LMC) in I80 and I60 at all vegetative stages. The UAV-derived Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI) correlated with LMC at the vegetative and full vegetative stages for all three irrigation treatments. In cases where eggplant is irrigated under normal conditions, the use of NDVI or OSAVI at full vegetative stages will be able to predict eggplant yields. In cases where, eggplant is grown under deficit irrigation, CWSI can be used at vegetative or full vegetative stages next to NDVI or OSAVI depending on available resources. •CWSI under sub-humid areas has a good correlation with plant water status.•NIR, red, and green bands are sensitive in detecting leaf moisture content.•Canopy biochemical properties influences high reflectance of NIR band.•The NDVI and OSAVI distinguishes best the leaf moisture content.•Vegetation indices maps indicate spatial variations of plant water condition.