Reinforcement Intelligence for Spectral Enhancement (RISE): A novel feature extraction method for hyperspectral prediction of sugar content in Citrus reticulata 'Chun Jian'

Abstract

This study proposes a reinforcement learning-based hyperspectral feature band selection method, Reinforcement Intelligence for Spectral Enhancement (RISE), for the non-destructive detection of sugar content in citrus. The band selection problem is modelled as a Markov decision process, and an optimal feature optimisation strategy is learned through a deep Q network. A total of 120 citrus pulp samples with sugar content ranging from 6.40 to 10.81 °Brix were collected. Hyperspectral data (388.34–1036.34 nm) containing 256 continuous bands were collected in the experiment, and compared with the traditional CARS (9 bands selected) and BOSS (10 bands selected) algorithms. The results show that the RISE algorithm selected 19 characteristic bands that obtained the best prediction performance (R² = 0.84, RPD = 2.51) on the PLSR model, and maintained consistent performance across multiple prediction models including SVR (R² = 0.85, RPD = 2.57), Random Forest (R² = 0.84, RPD = 2.47) and XGBoost (R² = 0.84, RPD = 2.53). The visualization of the spatial distribution of sugar content in citrus fruits based on the RISE algorithm revealed a gradient distribution feature that decreases from the outside to the inside. The study confirms the application potential of the RISE algorithm in non-destructive testing of agricultural product quality and provides a new technical path for hyperspectral imaging technology in agriculture.

Plant phenotyping relevance

柑橘果实糖含量是植物器官性状，研究核心是开发并比较基于高光谱数据的特征波段选择与无损性状预测方法，而非例行测量。

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