Evaluation of Sunflower Seed Moisture Content by Spectral Characteristics of Inflorescences in the VNIR

Sunflowers are one of the most important agricultural crops in the world. Given the high importance of sunflower products in the world market and the scale of their cultivation, the introduction of precision farming technologies into its culture can have a significant economic and environmental effect. This study demonstrated the fundamental possibility of developing a technology for rapid, remote, and non-invasive assessment of sunflower seed moisture to determine the optimal timing for desiccation and harvesting. It has been shown that the moisture content of sunflower seeds can be assessed with high accuracy based on the spectral characteristics of the underside of the inflorescences obtained using a hyperspectral camera in the visible and near-infrared range (VNIR) (from 450 to 950 nm). Random forest regression (RFR) was used to predict sunflower seed moisture. The model performed excellently on the training data (R2c = 1.00; MAEc = 0.58; RMSEc = 0.74, MAPEc = 1.29) and with a high performance on the testing data (R2t = 0.98, MAEt = 2.99, RMSEt = 3.28, MAPEt = 12.22). The most significant vegetation indices for determining moisture are CCI, Booch, Datt3, Datt4, LSIRed, modPRI, SR5, TCARI, and TCARI2.