Investigating the Effects of Full-Spectrum LED Lighting on Strawberry Traits Using Correlation Analysis and Time-Series Prediction.

Abstract

In crop cultivation, particularly in controlled environmental agriculture, light quality is one of the most critical factors affecting crop growth and harvest. Many scholars have studied the effects of light quality on strawberry traits, but they have used relatively simple light components and considered only a small number of light qualities and traits in each experiment, and the results were not complete or objective. In order to comprehensively investigate the effects of different light qualities from 350 nm to 1000 nm on strawberry traits to better predict the future growth trend of strawberries under different light qualities, we proposed a new approach. We introduced Spearman's rank correlation coefficient to handle complex light quality variations and multiple traits, preprocessed the cultivation data through the CEEDMAN method, and predicted them using the Informer network. We took 500 strawberry plants as samples and cultivated them in 72 groups of dynamically changing light qualities. Then, we recorded the growth changes and formed training and testing sets. Finally, we discussed the correlation between light quality and plant trait changes in consistency with current studies, and the proposed prediction model achieved the best performance in the prediction task of nine plant traits compared with the comparison models. Thus, the validity of the proposed method and model was demonstrated.

Plant phenotyping relevance

植物9形質の将来予測を目的に、データ前処理法とInformer予測モデルを中心的に提案・評価しており、形質取得・推定手法が実験の単なる routine 測定ではない。

Code and data availability

The supplied blocks describe strawberry phenotyping under 72 LED light-quality combinations and Informer-based prediction, but contain no public dataset deposit, no author analysis code or model availability statement, and no repository identifiers. No paper-specific public asset is actionable.

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