1. Advancing food sustainability: a case study on improving rice yield prediction in Sri Lanka using weather-based, feature-engineered machine learning models.
- Author
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Amarasinghe, Aminda, Sangarasekara, Ishini, Silva, Nuwan De, Ariyaratne, Mojith, Amarasinghe, Ruwanga, Bogahawatte, Jinendra, Alawatugoda, Janaka, and Herath, Damayanthi
- Abstract
Food sustainability is crucial aspect in achieving several United Nations (UN) Sustainable Development Goals (SDGs). By integrating advanced technologies for reliable and accurate decision-making, we can advance food sustainability and, consequently, make significant advances toward achieving the UN SDGs. Rice, a staple crop in many Asian and some African nations, is crucial to Sri Lanka as well. Serving as the primary food for most Sri Lankans, it plays a vital role in sustaining the livelihoods of over 1.8 million farmers. In Sri Lanka, rice is grown during two distinct seasons of the year (Yala and Maha). This study focuses on ML with feature engineering for rice yield prediction using weather data: Rainfall, Maximum temperature, Minimum temperature, and Radiation. The data from two districts in Yala and Maha seasons collected from 1982 to 2019 were used for evaluating two sets of models respectively. Data were pre-processed to handle the outliers and missing values and scaled using normalization. The machine learning models considered are Linear Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Random Forest (RF). The performance of these models was evaluated using metrics: Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), and Mean Absolute Error (MAE). The results demonstrate that Random Forest Regression with less number of features can yield comparable results compared to the original set of features.Article Highlights: Rice is a staple food and vital to the livelihoods of millions of people worldwide. Therefore, accurate and timely prediction of rice yield is essential for global food security. The study integrates machine learning techniques and feature engineering on weather data (including rainfall and temperature) to improve rice yield predictions, thus contributing to food sustainability and progress toward the UN Sustainable Development Goals (SDGs). Among the machine learning models evaluated (Linear Regression, Support Vector Machine, k-Nearest Neighbour, and Random Forest), Random Forest Regression demonstrated that fewer features could produce results comparable to those using a full set of features, highlighting its efficiency in rice yield prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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