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A Smart Farm DNN Survival Model Considering Tomato Farm Effect

Author

Listed:
  • Jihun Kim

    (Department of Statistics, Pukyong National University, Busan 48513, Republic of Korea)

  • Il Do Ha

    (Department of Statistics, Pukyong National University, Busan 48513, Republic of Korea)

  • Sookhee Kwon

    (Department of Statistics, Pukyong National University, Busan 48513, Republic of Korea)

  • Ikhoon Jang

    (Institute of Technology, Jinong Inc., Anyang 14067, Republic of Korea)

  • Myung Hwan Na

    (Department of Mathematics/Statistics, Chonnam National University, Gwangju 61186, Republic of Korea)

Abstract

Recently, smart farming research based on artificial intelligence (AI) has been widely applied in the field of agriculture to improve crop cultivation and management. Predicting the harvest time (time-to-harvest) of crops is important in smart farming to solve problems such as planning the production schedule of crops and optimizing the yield and quality. This helps farmers plan their labor and resources more efficiently. In this paper, our concern is to predict the time-to-harvest (i.e., survival time) of tomatoes on a smart farm. For this, it is first necessary to develop a deep learning modeling approach that takes into account the farm effect on the tomato plants, as each farm has multiple tomato plant subjects and outcomes on the same farm can be correlated. In this paper, we propose deep neural network (DNN) survival models to account for the farm effect as a fixed effect using one-hot encoding. The tomato data used in our study were collected on a weekly basis using the Internet of Things (IoT). We compare the predictive performance of our proposed method with that of existing DNN and statistical survival modeling methods. The results show that our proposed DNN method outperforms the existing methods in terms of the root mean squared error (RMSE), concordance index (C-index), and Brier score.

Suggested Citation

  • Jihun Kim & Il Do Ha & Sookhee Kwon & Ikhoon Jang & Myung Hwan Na, 2023. "A Smart Farm DNN Survival Model Considering Tomato Farm Effect," Agriculture, MDPI, vol. 13(9), pages 1-14, September.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1782-:d:1236117
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    References listed on IDEAS

    as
    1. Roei Grimberg & Meir Teitel & Shay Ozer & Asher Levi & Avi Levy, 2022. "Estimation of Greenhouse Tomato Foliage Temperature Using DNN and ML Models," Agriculture, MDPI, vol. 12(7), pages 1-12, July.
    2. Shu-Chu Liu & Quan-Ying Jian & Hsien-Yin Wen & Chih-Hung Chung, 2022. "A Crop Harvest Time Prediction Model for Better Sustainability, Integrating Feature Selection and Artificial Intelligence Methods," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
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