Integration of RGB-based vegetation index, crop surface model and object-based image analysis approach for sugarcane yield estimation using unmanned aerial vehicle

Abstract

Estimation of yield is a major challenge in the production of many agricultural crops, including sugarcane. Mapping the spatial variability of plant height (PH) and the stalk density is important for accurate sugarcane yield estimation, and this estimation can aid in the planning of upcoming labor- and cost-intensive actions like harvesting, milling, and forward selling decisions. The objective of this research is to assess the potential of a consumer-grade red-green-blue (RGB) camera mounted on an unmanned aerial vehicle (UAV) for sugarcane yield estimation with minimal field dataset. The study mapped the spatial variability of PH and stalk density at the grid level (4 m × 4 m) on a farm. The average PH was estimated at the grid level by masking the sugarcane area. An object-based image analysis (OBIA) approach was used to extract the sugarcane area by integrating the plant height model (PHM), extracted by subtracting the digital elevation model (DEM) from the crop surface model (CSM). Both CSM and DEM were generated from UAV images, where CSM was produced approximately one month before the harvest and the DEM after the sugarcane was harvested. The PHM improved the overall accuracy of classification from 61.98% to 87.45%. The UAV estimated PH showed a high correlation (r = 0.95) with ground observed PH, with an average overestimation of 0.10 m. An ordinary least square (OLS) linear regression model was developed to estimate millable stalk height (MSH) from PH, weight from estimated MSH, and stalk density from vegetation indices (VIs) at the grid-level. Excess green (ExG) derived from RGB showed R2 of 0.754 with the stalk density. Likewise, R2 of 0.798 and 0.775 were obtained between MSH and PH, and weight and MSH. Eventually, the yield was estimated by integrating the variability of PH and stalk density and weight information. The estimated yield from ExG (200.66 tons) was close to the actual harvest yield (192.1 tons). The very high-resolution RGB-based images from the UAV and OBIA approach demonstrate significant potential for mapping the spatial variability of PH and stalk density and for estimating sugarcane yield. This can aid growers and millers in decision making.