Correlation between Ground Measurements and UAV Sensed Vegetation Indices for Yield Prediction of Common Bean Grown under Different Irrigation Treatments and Sowing Periods

• Published in: Water (Basel) Vol. 14; no. 22; p. 3786
• Main Authors: Lipovac, Aleksa, Bezdan, Atila, Moravčević, Djordje, Djurović, Nevenka, Ćosić, Marija, Benka, Pavel, Stričević, Ružica
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Agricultural production; Beans; Chlorophyll; Climate change; common bean; Common beans; Comparative analysis; Crop yields; Drone aircraft; Drought; Environmental aspects; Evapotranspiration; Experiments; Farming; Growth; Irrigation; Irrigation water; late sowing; Leaf area; Leaf area index; Measurement; Monitoring; Multispectral photography; Normalized difference vegetative index; Phaseolus vulgaris; Predictions; Rain; Remote sensing; Satellites; Soil moisture; Sowing; Transpiration; Unmanned aerial vehicles; Vegetation; vegetation indices; Water shortages; Water stress; yield prediction; Serbia
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w14223786

The objective of this study is to assess the possibility of using unmanned aerial vehicle (UAV) multispectral imagery for rapid monitoring, water stress detection and yield prediction under different sowing periods and irrigation treatments of common bean (Phaseolus vulgaris, L). The study used a two-factorial split-plot design, divided into subplots. There were three sowing periods (plots; I—mid April, II—end of May/beginning of June, III—third decade of June/beginning of July) and three levels of irrigation (subplots; full irrigation (F)—providing 100% of crop evapotranspiration (ETc), deficit irrigation (R)—providing 80% of ETc, and deficit irrigation (S) providing—60% of ETc). Canopy cover (CC), leaf area index (LAI), transpiration (T) and soil moisture (Sm) were monitored in all treatments during the growth period. A multispectral camera was mounted on a drone on seven occasions during two years of research which provided raw multispectral images. The NDVI (Normalized Difference Vegetation Index), MCARI1 (Modified Chlorophyll Absorption in Reflectance Index), NDRE (Normalized Difference Red Edge), GNDVI (Green Normalized Difference Vegetation Index) and Optimized Soil Adjusted Vegetation Index (OSAVI) were computed from the images. The results indicated that NDVI, MCARI1 and GNDVI derived from the UAV are sensitive to water stress in S treatments, while mild water stress among the R treatments could not be detected. The NDVI and MCARI1 of the II-S treatment predicted yields better (r2 = 0.65, y = 4.01 tha−1; r2 = 0.70, y = 4.28 tha−1) than of III-S (r2 = 0.012, y = 3.54 tha−1; r2 = 0.020, y = 3.7 tha−1). The use of NDVI and MCARI will be able to predict common bean yields under deficit irrigation conditions. However, remote sensing methods did not reveal pest invasion, so good yield predictions require observations in the field. Generally, a low-flying UAV proved to be useful for monitoring crop status and predicting yield and water stress in different irrigation regimes and sowing period.