Improving chili pepper LAI prediction with TPE-2BVIs and UAV hyperspectral imagery

Abstract

Leaf area index (LAI) of chili peppers is an important indicator of plant growth and productivity. Accurate monitoring of LAI is crucial for optimizing growth conditions, and improving crop yields. However, existing remote sensing-based methods for LAI prediction often face low accuracy, especially in high-density vegetation and across different growth stages. These limitations primarily stem from issues such as multicollinearity and the constraints of spectral information processing mechanisms, which affect the stability and accuracy of predictions. To address these challenges, this study proposed a novel prediction method combining a tree-structured parzen estimator (TPE)-optimized two-band vegetation indices (TPE-2BVIs) with random forest regression (RFR). The TPE-2BVIs band optimization algorithm effectively extracts latent spectral information, enhancing LAI prediction stability and accuracy by addressing multicollinearity and nonlinear model complexity. Experimental results show that: (1) hyperspectral images provide more detailed spectral information compared to multispectral images, significantly improving the accuracy of LAI prediction; (2) the use of 2BVIs alleviates the effects of spectral saturation and dynamic variations during different growth stages, further improving prediction accuracy; (3) the proposed TPE-2BVIs band optimization method significantly enhances both the performance and stability of the model. When combined with RFR, the model achieves R² = 0.887, RMSE = 0.520, and NRMSE = 7.554 %. The TPE-2BVIs band optimization algorithm introduced in this study effectively extracts latent spectral information, overcoming the limitations of multicollinearity and the complexity of nonlinear models in traditional methods. This approach significantly improves the stability and accuracy of LAI predictions. The proposed method provides an innovative solution for remote sensing vegetation monitoring and agricultural applications, offering broad potential for estimating phenotypic parameters under diverse environmental conditions.