An Enhanced Faster R-CNN for High-Throughput Winter Wheat Spike Monitoring to Improved Yield Prediction and Water Use Efficiency

This study develops an innovative unmanned aerial vehicle (UAV)-based intelligent system for winter wheat yield prediction, addressing the inefficiencies of traditional manual counting methods (with approximately 15% error rate) and enabling quantitative analysis of water–fertilizer interactions. By integrating an enhanced Faster Region-Based Convolutional Neural Network (Faster R-CNN) architecture with multi-source data fusion and machine learning, the system significantly improves both spike detection accuracy and yield forecasting performance. Field experiments during the 2022–2023 growing season captured high-resolution multispectral imagery for varied irrigation regimes and fertilization treatments. The optimized detection model incorporates ResNet-50 as the backbone feature extraction network, with residual connections and channel attention mechanisms, achieving a mean average precision (mAP) of 91.2% (calculated at IoU threshold 0.5) and 88.72% recall while reducing computational complexity. The model outperformed YOLOv8 by a statistically significant 2.1% margin (p < 0.05). Using model-generated spike counts as input, the random forest (RF) model regressor demonstrated superior yield prediction performance (R2 = 0.82, RMSE = 324.42 kg·ha−1), exceeding the Partial Least Squares Regression (PLSR) (R2 +46%, RMSE-44.3%), Least Squares Support Vector Machine (LSSVM) (R2 + 32.3%, RMSE-32.4%), Support Vector Regression (SVR) (R2 + 30.2%, RMSE-29.6%), and Backpropagation (BP) Neural Network (R2+22.4%, RMSE-24.4%) models. Analysis of different water–fertilizer treatments revealed that while organic fertilizer under full irrigation (750 m3 ha−1) conditions achieved maximum yield benefit (13,679.26 CNY·ha−1), it showed relatively low water productivity (WP = 7.43 kg·m−3). Conversely, under deficit irrigation (450 m3 ha−1) conditions, the 3:7 organic/inorganic fertilizer treatment achieved optimal WP (11.65 kg m−3) and WUE (20.16 kg∙ha−1∙mm−1) while increasing yield benefit by 25.46% compared to organic fertilizer alone. This research establishes an integrated technical framework for high-throughput spike monitoring and yield estimation, providing actionable insights for synergistic water–fertilizer management strategies in sustainable precision agriculture.