Prediction of smart farm tomato harvest time: Comparison of machine learning and deep learning approaches†
Jihun Kim1 · Sookhee Kwon2 · Il Do Ha3 · Myung Hwan Na4
123Department of Statistics, Pukyong National University
3Department of Artificial Intelligence Convergence, Pukyong National University
4Department of Mathematics/Statistics, Chonnam National University
3Department of Artificial Intelligence Convergence, Pukyong National University
4Department of Mathematics/Statistics, Chonnam National University
Correspondence to: 1 Graduate student, Department of Statistics, Pukyong National University, Busan 48513, Korea.
2 Researcher, Department of Statistics, Pukyong National University, Busan 48513, Korea. e-mail: habaqueen@naver.com
3 Professor, Department of Statistics, Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Korea.
4 Professor, Department of Mathematics/Statistics, Chonnam National University, Gwangju 61186, Korea.
† This work was supported by the Research Program of Rural Development Administration (Project No. PJ0153372021).
2 Researcher, Department of Statistics, Pukyong National University, Busan 48513, Korea. e-mail: habaqueen@naver.com
3 Professor, Department of Statistics, Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Korea.
4 Professor, Department of Mathematics/Statistics, Chonnam National University, Gwangju 61186, Korea.
† This work was supported by the Research Program of Rural Development Administration (Project No. PJ0153372021).
Received January 26, 2022; Revised March 10, 2022; Accepted March 15, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- In this paper, we present the prediction results of the harvest time from tomato big data collected by week using the Internet of Things (IoT) in the field of smart farms. Here, the harvest time is defined as the time from fruiting to harvest. For the prediction of tomato harvest time, we consider the three powerful prediction methods, deep learning, random forest and XGBoost. In addition, we also consider a classical linear regression model. We compare and analyze the prediction results among the four models. This study is expected to contribute the profit of farmers by harvesting tomatoes at the appropriate time by predicting the harvest time of tomatoes.
- Keywords : Deep learning model, harvest time, random forest model, smart farm, XG-Boost model.
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