用轻量模型和低成本传感器实现水果成熟度与硬度无损检测
Non-Destructive Prediction of Fruit Ripeness and Firmness Using Hyperspectral Imaging and Lightweight Machine Learning Models
- 采用20种经典机器学习算法，结合光谱预处理提升预测精度
- 仅需3个可见波段即可达到94%全谱精度，显著降低硬件要求
- 树模型性能超越复杂深度网络，适合农业场景落地应用
采后水果品质评估对减少食物浪费至关重要，但可靠的无损方法通常依赖昂贵的高光谱相机和计算密集型深度学习模型。这些系统需GPU资源、大规模训练数据及领域知识，限制了其在多数农业实际场景中的可行性。本研究系统评估了20种经典机器学习算法在高光谱成像数据上的表现，针对五种水果物种同时进行成熟度分类与硬度预测，采用交叉验证实验设计与贝叶斯超参数优化。结果表明，数据预处理策略（如类别平衡与光谱变换）对预测精度的影响与算法选择相当。树基模型性能优于文献中报道的Fruit-HSNet等先进深度学习模型。此外，仅需三个可见光波段即可恢复超过94%的全谱精度，证明低成本多光谱传感器与轻量级机器学习模型可作为高成本高光谱设备和复杂深度学习方法的实用替代方案，适用于实际水果品质分选。
原文摘要 · Abstract (English)
Post-harvest fruit quality assessment is essential for reducing food waste, yet reliable non-destructive methods typically depend on expensive hyperspectral cameras and computationally intensive deep learning models. These systems typically require GPU resources, large-scale training data, and domain expertise, limiting their feasibility for many real-world agricultural settings. This study systematically evaluates 20 classical machine learning algorithms on hyperspectral imaging data for simultaneous ripeness classification and firmness prediction across five fruit species, using cross-validated experimental design with Bayesian hyperparameter optimization. Data preprocessing strategy, particularly class balancing and spectral transformations, contributes as much to prediction accuracy as algorithm choice. Our results show that tree-based machine learning models can outperform state-of-the-art deep earning models reported in Fruit-HSNet. Moreover, the findings indicate that only three visible-range wavelengths are needed to recover over 94% of full-spectrum accuracy, demonstrating that low-cost multispectral sensors combined with lightweight machine learning models can serve as practical alternatives to expensive hyperspectral cameras and complex deep learning approaches for practical fruit quality sorting.
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