A Smart Farm DNN Survival Model Considering Tomato Farm Effect

Jihun Kim, Il Do Ha (), Sookhee Kwon, Ikhoon Jang and Myung Hwan Na
Additional contact information
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

Agriculture, 2023, vol. 13, issue 9, 1-14

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.

Keywords: DNN model; farm effect; one-hot encoding; survival model; time-to-harvest (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/9/1782/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/9/1782/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:9:p:1782-:d:1236117

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().