Cotton Yield Estimation Using Very High-Resolution Digital Images Acquired with a Low-Cost Small Unmanned Aerial Vehicle

Yield estimation is a critical task in crop management. Traditional methods are costly, time-consuming, and difficult to expand to a relatively large field. Remote sensing can provide quick coverage over a field at any scale. Satellite remote sensing is used for large-scale earth observation. Remote sensing with manned airplanes at relatively high altitudes (>500 m) has difficulty achieving the spatial resolution required for field-scale precision farming. Ground-based systems are typically used for point measurements and are restricted to field conditions. Unmanned aerial vehicles (UAVs) provide a unique platform for high-resolution remote sensing, and UAV-based remote sensing systems can be used to estimate crop yield in a cost-effective manner. The objective of this study was to develop and evaluate new methods for estimation of cotton yield for precision cotton farming. Experimental plots were laid out in a cotton field near Stoneville, Mississippi, in 2014. Nitrogen fertilizer was applied to the plots at five different rates to generate cotton yield variation. Two methods were employed to estimate cotton yield using very high-resolution digital images (2.7 cm pixel-1) acquired from an inexpensive small multirotor UAV: (1) using three-dimensional point cloud data derived from multiple digital images of the cotton field to estimate cotton plant height and hence estimate yield, and (2) segmenting cotton boll signatures from the background of the digital images of the defoliated cotton field just prior to harvest and then estimating yield with the estimated cotton plot unit coverage. The results indicated that low-altitude remote sensing with an inexpensive small UAV can be used to estimate cotton yield accurately through estimation of plant height (R2 = 0.43, compared with R2 = 0.42 for yield estimation through manually measured plant height). The results further indicated that the method can offer reliable cotton yield estimation through estimation of cotton boll coverage in each plot with Laplacian image processing while considering a few plots with poor light condition as outliers (R2 = 0.83). This study could benefit yield estimation of cotton, with similar methods used for other crops, in agricultural research and crop production.