Version 1
: Received: 28 June 2024 / Approved: 28 June 2024 / Online: 1 July 2024 (04:02:41 CEST)
How to cite:
Liu, S.; Zhao, Y.; Li, T.; Zu, L.; Chang, S. Research on Multi-Step Fruit Color Prediction Model of Tomato in Solar Greenhouse Based on Time Series Data. Preprints2024, 2024062037. https://doi.org/10.20944/preprints202406.2037.v1
Liu, S.; Zhao, Y.; Li, T.; Zu, L.; Chang, S. Research on Multi-Step Fruit Color Prediction Model of Tomato in Solar Greenhouse Based on Time Series Data. Preprints 2024, 2024062037. https://doi.org/10.20944/preprints202406.2037.v1
Liu, S.; Zhao, Y.; Li, T.; Zu, L.; Chang, S. Research on Multi-Step Fruit Color Prediction Model of Tomato in Solar Greenhouse Based on Time Series Data. Preprints2024, 2024062037. https://doi.org/10.20944/preprints202406.2037.v1
APA Style
Liu, S., Zhao, Y., Li, T., Zu, L., & Chang, S. (2024). Research on Multi-Step Fruit Color Prediction Model of Tomato in Solar Greenhouse Based on Time Series Data. Preprints. https://doi.org/10.20944/preprints202406.2037.v1
Chicago/Turabian Style
Liu, S., Linlu Zu and Siyuan Chang. 2024 "Research on Multi-Step Fruit Color Prediction Model of Tomato in Solar Greenhouse Based on Time Series Data" Preprints. https://doi.org/10.20944/preprints202406.2037.v1
Abstract
Color change is the most obvious characteristic of tomato ripening stage and an important indicator of tomato ripening condition, which directly affects the commodity value of tomato. To visualize the color change of tomato fruit in mature stage, a gated recurrent unit network with an encoder-decoder structure that dynamically simulates tomato growth and development with time-dependent lines using tomato color and shape as real-time data was proposed in this paper. Firstly, the .json file was converted into a mask.png file, the tomato mask was extracted, and the tomato was separated from the complex background environment, and the tomato growth and development data set was successfully constructed. Then, a network of gated recurrent units with encoder-decoder structure was constructed to predict the future growth trend of tomato under different greenhouse temperatures. The experimental results showed that for the gated recurrent unit network of encoder-decoder structure proposed, when the hidden layer number was 1 and hidden layer number was 512, a high consistency and similarity between the model predicted image sequence and the actual growth and development image sequence would be realized, and the structural similarity index measure was 0.746. It was proved that when the average temperature was 24.93℃, the average soil temperature was 24.06℃, and the average light intensity was 11.26 Klux, the environment was the most suitable for tomato growth. The environmental data-driven tomato growth model was constructed to explore the growth status of tomato under different environmental conditions, thus to understand the growth status of tomato in time. The statement provided a theoretical foundation for determining the optimal greenhouse environmental conditions to achieve tomato maturity. And it offered recommendations for investigating the growth cycle of tomatoes, as well as technical assistance for standardized cultivation in solar greenhouses.
Keywords
solar greenhouse; internet of things; tomato growth model; deep learning; multi-step space-time prediction
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
