Estimating the full-period rice leaf area index using CNN-LSTM-Attention and multispectral images from unmanned aerial vehicles

The results show that the CLA model outperforms other approaches in estimating rice LAI throughout the entire growing period, achieving a coefficient of determination (R²) of 0.92 and a relative root mean square error (RRMSE) below 9%, significantly better than linear regression and machine learning methods. Moreover, the CLA model maintains high stability and accuracy across different LAI ranges, with notably reduced errors for low LAI values (one to three), effectively mitigating the influence of soil background. This research offers an efficient and accurate technological approach for rice growth monitoring and holds significant implications for precision agricultural management.