Bell pepper yield estimation using time series unmanned air vehicle multispectral vegetation indexes and canopy volume

Abstract

Accurate and timely crop yield estimation prior to harvest is important for agricultural management, agricultural economy, and food security. In many cases, the farmers estimate the yield visually. Further, several crop simulation models have been developed to estimate yield accurately. However, these are not used efficiently because of their requirements for enormous amounts of data and their inability to show the spatial differences of yield in the field. Recently, the rapid development of unmanned air vehicle (UAV) technologies has shown great potential to estimate crop yield accurately and show the spatial heterogeneity in the field. We estimate the bell pepper yield with time series, high-resolution UAV multispectral images. To do so, canopy volume and five different spectral vegetation indices used widely were calculated. Seven UAV flight missions were conducted between June and August of 2019. Various linear regression models were developed to estimate the bell pepper yield based on the canopy volume values and vegetation indices. The results showed that the bell pepper canopy volume fit the yield best with the minimum estimation error [coefficient of determination ( R2 ) = 0.93 and root mean square error ( RMSE ) = 2.30 tons ha − 1]. In addition, a significant correlation was found between the enhanced vegetation index and bell pepper yield (R2 = 0.87 and RMSE = 3.16 tons ha − 1).