Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

Mahmudul Hasan; Md Abu Marjan; Md Palash Uddin; Masud Ibn Afjal; Seifedine Kardy; Shaoqi Ma; Yunyoung Nam

doi:10.3389/fpls.2023.1234555

Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

Front Plant Sci. 2023 Aug 10:14:1234555. doi: 10.3389/fpls.2023.1234555. eCollection 2023.

Authors

Mahmudul Hasan¹, Md Abu Marjan¹, Md Palash Uddin^{1

2}, Masud Ibn Afjal¹, Seifedine Kardy^{3

4

5}, Shaoqi Ma⁶, Yunyoung Nam⁶

Affiliations

¹ Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
² School of Information Technology, Deakin University, Geelong, VIC, Australia.
³ Department of Applied Data Science, Noroff University College, Kristiansand, Norway.
⁴ Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates.
⁵ Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
⁶ Department of ICT Convergence, Soonchunhyang University, Asan, Republic of Korea.

Abstract

Agriculture is the most critical sector for food supply on the earth, and it is also responsible for supplying raw materials for other industrial productions. Currently, the growth in agricultural production is not sufficient to keep up with the growing population, which may result in a food shortfall for the world's inhabitants. As a result, increasing food production is crucial for developing nations with limited land and resources. It is essential to select a suitable crop for a specific region to increase its production rate. Effective crop production forecasting in that area based on historical data, including environmental and cultivation areas, and crop production amount, is required. However, the data for such forecasting are not publicly available. As such, in this paper, we take a case study of a developing country, Bangladesh, whose economy relies on agriculture. We first gather and preprocess the data from the relevant research institutions of Bangladesh and then propose an ensemble machine learning approach, called K-nearest Neighbor Random Forest Ridge Regression (KRR), to effectively predict the production of the major crops (three different kinds of rice, potato, and wheat). KRR is designed after investigating five existing traditional machine learning (Support Vector Regression, Naïve Bayes, and Ridge Regression) and ensemble learning (Random Forest and CatBoost) algorithms. We consider four classical evaluation metrics, i.e., mean absolute error, mean square error (MSE), root MSE, and R ², to evaluate the performance of the proposed KRR over the other machine learning models. It shows 0.009 MSE, 99% R ² for Aus; 0.92 MSE, 90% R ² for Aman; 0.246 MSE, 99% R ² for Boro; 0.062 MSE, 99% R ² for wheat; and 0.016 MSE, 99% R ² for potato production prediction. The Diebold-Mariano test is conducted to check the robustness of the proposed ensemble model, KRR. In most cases, it shows 1% and 5% significance compared to the benchmark ML models. Lastly, we design a recommender system that suggests suitable crops for a specific land area for cultivation in the next season. We believe that the proposed paradigm will help the farmers and personnel in the agricultural sector leverage proper crop cultivation and production.

Keywords: agricultural data processing; crop prediction; crop production; ensemble learning; machine learning.

Grants and funding

This research was supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0012724, The Competency Development Program for Industry Specialist), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00218176), and the Soonchunhyang University Research Fund.