Work

High-throughput estimation of wheat tiller density using UAV-derived spectral-textural features and attention-based deep learning

Year: 2026

Type: article

Abstract: Wheat tiller density (TD) is a critical early-season indicator of crop growth and a key determinant of yield potential. However, current TD assessments still rely on manual counting, which is slow, costly, and difficult to scale. This study evaluates the use of Unmanned Aerial Vehicle (UAV)-acquired multispectral imagery combined with machine and deep learning-based predictive models for high-throughput estimation of wheat TD. A total of 721 TD observations were collected across five sites in South Dakota and paired with high-resolution UAV multispectral imagery. UAV-derived spectral and textural features were used to train a Random Forest regression (RFR) model and three deep learning models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), and an attention-based CNN (Atten-CNN). Our results show that, on a held-out test set, Atten-CNN achieved the best performance (R² = 0.81; RMSE% = 15.97%), outperforming CNN (R² = 0.77; RMSE% = 17.74%), as well as RFR and DNN. Model interpretation using SHapley Additive exPlanations (SHAP) indicated that predictions were driven primarily by red-edge and pigment sensitive spectral features led by CRI_2 (Carotenoid Reflectance Index 2) and NDRE (Normalized Difference Red Edge Index), with additional contributions from GLCM (Gray Level Co-occurrence Matrix)-based canopy texture features, particularly band-wise correlation, variance, and entropy matrix. By integrating spectral and textural UAV features within an interpretable attention-based CNN and validating across breeding and commercial fields, this study demonstrates a practical, scalable framework for early-season wheat TD estimation.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2026.102521