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

Abstract

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.

Monitoring Drought Stress in Common Bean Using Chlorophyll Fluorescence and Multispectral Imaging

“…Although the increased reflection of visible light indicates a decrease in chlorophyll content and light absorption, our results showed no decrease in the CHI under drought treatments. Similarly, the NDVI, which is often considered as one of the best indices for monitoring plant water deficits [ 26 , 47 ], decreased only after prolonged drought in our study. Thus, the drought-induced increase in R Blue is probably more related to an increased ARI (anthocyanin content) and less related to the decrease in chlorophyll content.…”

“…In this study with different irrigation treatments of the common bean, the LAI varied significantly. This suggests that water availability is one of the most important factors for crop development [ 47 ]. Because DV is calculated from total leaf area and plant height (which was not affected by drought treatments), it can be stated that the earliest and most pronounced effect of drought on the common bean morphology is reduced leaf area and LAI.…”

AquaCrop Model Performance in Yield, Biomass, and Water Requirement Simulations of Common Bean Grown under Different Irrigation Treatments and Sowing Periods

“…The results obtained for the validation data set indicated a moderate correlation (r varied from 0.41-0.8), with an NRMSE of 10-30.1%, and d varying from 0.46 to 0.72. In the same experiment, a strong correlation was noted between the normalized difference vegetation index (NDVI) and CC, leaf area index and transpiration, and a weak and negative correlation with soil moisture [36]. A sample collected close to a drip will certainly exhibit a higher soil moisture.…”

“…There were three sowing periods: standard I, mid-April, consistent with the climatic conditions in Serbia); late spring II, end of May/beginning of June; and summer III, third decade of June/beginning of July. The soil characteristics of the experimental site are described in detail in [36]. The climate input data (maximum and minimum air temperatures, maximum and minimum relative humidity, net radiation, wind velocity, and precipitation) were measured daily in the field by a micrometeorological station, and the data were validated against the nearest meteorological station of the first order in Surčin, at a distance of 20 km.…”

“…The soil was drilled and sampled by layer, at 0-20, 20-40, and 40-60 cm. The canopy cover sampling methods are described in detail in [36]. The yield and harvest index were recorded when the common bean was physiologically ripe and contained 10% of moisture.…”