Functional data analysis-based yield modeling in year-round crop cultivation

Abstract

Crop yield prediction is essential for effective agricultural management. We introduce a methodology for modeling the relationship between environmental parameters and crop yield in longitudinal crop cultivation, exemplified by strawberry and tomato production based on year-round cultivation. Employing functional data analysis (FDA), we developed a model to assess the impact of these factors on crop yield, particularly in the face of environmental fluctuation. Specifically, we demonstrated that a varying-coefficient functional regression model (VCFRM) is utilized to analyze time-series data, enabling to visualize seasonal shifts and the dynamic interplay between environmental conditions such as solar radiation and temperature and crop yield. The interpretability of our FDA-based model yields insights for optimizing growth parameters, thereby augmenting resource efficiency and sustainability. Our results demonstrate the feasibility of VCFRM-based yield modeling, offering strategies for stable, efficient crop production, pivotal in addressing the challenges of climate adaptability in plant factory-based horticulture.

Acknowledgements

We would like to thank Heiwado farm for providing us with the data for cultivation of strawberries and Higashibaba farm for providing us with those of tomatoes. This work was supported by PRESTO (grant no. JPMJPR16O6 to H.M.) of the Japan Science and Technology Agency and KAKENHI (grant no. 19 K11858 to H.M.) of the Japan Society for the Promotion of Science. This work was also partially supported by CREST (grant no. JPMJCR16O4 to K.M.) of the Japan Science and Technology Agency and by Cabinet Office, Government of Japan, Moonshot Research and Development Program for Agriculture, Forestry and Fisheries (funding agency: Bio-oriented Technology Research Advancement Institution, No. JPJ009237 to K.M.).

Conflict of interest statement

The authors declare that they have no conflict of interest.

Supplementary Data

Supplementary data is available at Horticulture Research online.

Data availability

The data underlying this article cannot be shared publicly due to the trade secret.

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