Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation - Hasan, Mahmudul; Marjan, Md Abu; Uddin, Md Palash; Afjal, Masud Ibn; Kardy, Seifedine; Ma, Shaoqi; Nam, Yunyoung (Frontiers in Plant Science, 2023)
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, agricultural production is not expanding sufficiently in response to 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, 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 Neighbour 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 R2 , 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. Diebold-Mariano test is conducted to check the robustness of the proposed ensemble model, KRR. In most cases, it shows 1% and 5% significant than the benchmark ML models. At last, we design a recommender system to suggest suitable crops for a specific land area for cultivation in the next season. We believe that the proposed paradigm will help farmers and personnel in the agricultural sector leverage proper crop cultivation and production.