Improving rice leaf area index monitoring accuracy via robot-integrated multi-sensors and meteorological data fusion with explainable machine learning

Abstract

Accurate and non-destructive estimation of rice Leaf Area Index (LAI) is vital for crop growth assessment and yield prediction. Close-range, non-contact optical methods are commonly used for LAI monitoring. However, their accuracy is often limited by the platform, and most rely on single-source data prone to saturation effects and background interference. To overcome these limitations, this study develops a phenotyping robot-based multi-source, high-resolution data fusion approach for field-scale LAI monitoring. A phenotyping robot equipped with multispectral and high-resolution RGB cameras was used to collect vegetation indices, color indices, texture features, and canopy coverage extracted from high-resolution RGB imagery. These features were further combined with meteorological variables to build machine learning models. The Random Forest model achieved the best performance (R² = 0.92, RMSE = 0.302). SHAP (Shapley Additive Explanations) was applied to interpret the model and quantify the importance of multispectral features, RGB-derived texture information, canopy coverage and meteorological factors. Canopy coverage, NDVI and Clgreen were identified as the key factors for improving model performance, and the complementary mechanism between canopy coverage and other features can alleviate the saturation effect in the high LAI stage. The results show that combining high-resolution remote sensing data from robots with meteorological data can effectively mitigate the saturation effect and soil background interference in LAI estimation, and significantly improve the accuracy of LAI estimation. This study provides a practical and scalable framework for field phenotyping and offers technical support for precise rice cultivation and smart agriculture.

Plant phenotyping relevance

ロボット搭載マルチセンサーと画像特徴量融合によるイネLAI推定手法を開発・評価しており、表現型取得・推定が研究の中心である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.