用AI预测不同作物对光谱转换膜的生长响应，准确率达91.4%。
Data-driven Prediction of Species-Specific Plant Responses to Spectral-Shifting Films from Leaf Phenotypic and Photosynthetic Traits
- 结合叶表型与光合特性，用变分自编码器扩充数据训练模型
- 作物平均增产22.5%，神经网络分类准确率高达91.4%
- 适合设施农业中光环境调控与智能种植决策参考
在温室中使用光谱转换膜（SF）将绿光转化为红光，不同作物的生长响应差异显著。然而，产量提升与各物种特定的生物物理特征密切相关。仅考虑单一属性难以全面理解光照质量调整与作物生长的关系。本研究旨在通过人工智能，综合多种叶表型与光合性状及日光积分，建立作物生长结果与光谱调节之间的关联。2021至2024年间，在覆盖普通薄膜（PEF）或光谱转换膜（SF）的温室中种植了多种叶菜、果菜和根茎类作物，测量了叶片反射率、单位面积叶干重、叶绿素含量、日光积分及光饱和点等指标，共收集210个数据点。由于数据量不足，采用变分自编码器进行数据增强。多数作物在SF下产量平均提高22.5%。基于这些数据，训练了逻辑回归、决策树、随机森林、XGBoost和前馈神经网络（FFNN）等模型，目标是二分类判断SF是否显著影响产量。其中FFNN在未参与训练的测试集上达到91.4%的高分类准确率。本研究通过整合叶表型、光合生理与环境因素，提升了预测太阳光谱变化对作物影响的能力。
原文摘要 · Abstract (English)
The application of spectral-shifting films in greenhouses to shift green light to red light has shown variable growth responses across crop species. However, the yield enhancement of crops under altered light quality is related to the collective effects of the specific biophysical characteristics of each species. Considering only one attribute of a crop has limitations in understanding the relationship between sunlight quality adjustments and crop growth performance. Therefore, this study aims to comprehensively link multiple plant phenotypic traits and daily light integral considering the physiological responses of crops to their growth outcomes under SF using artificial intelligence. Between 2021 and 2024, various leafy, fruiting, and root crops were grown in greenhouses covered with either PEF or SF, and leaf reflectance, leaf mass per area, chlorophyll content, daily light integral, and light saturation point were measured from the plants cultivated in each condition. 210 data points were collected, but there was insufficient data to train deep learning models, so a variational autoencoder was used for data augmentation. Most crop yields showed an average increase of 22.5% under SF. These data were used to train several models, including logistic regression, decision tree, random forest, XGBoost, and feedforward neural network (FFNN), aiming to binary classify whether there was a significant effect on yield with SF application. The FFNN achieved a high classification accuracy of 91.4% on a test dataset that was not used for training. This study provide insight into the complex interactions between leaf phenotypic and photosynthetic traits, environmental conditions, and solar spectral components by improving the ability to predict solar spectral shift effects using SF.
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