The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

Abstract

Recently, annual agricultural data have been highly volatile as a result of climate change and national economic trends. Therefore, such data might not be enough to develop good agricultural policies for stabilizing agricultural output. A good agricultural output prediction model to assist agricultural policymaking has thus become essential. However, the highly volatile data would affect the prediction model’s performance. For this reason, this study proposes a marriage in honey bees optimization/support vector regression (MBO/SVR) model to minimize the effects of highly volatile data (outliers) and enhance prediction accuracy. We verified the performance of the MBO/SVR model by using the annual total agricultural output collected from the official Agricultural Statistics Yearbook of the Council of Agriculture, Taiwan. Taiwan’s annual total agricultural output integrates agricultural, livestock and poultry, fishery, and forest products. The results indicated that the MBO/SVR model had a lower mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), and relative root mean squared error (r-RMSE) than those of the models it was compared to. Furthermore, the MBO/SVR model predicted long-term agricultural output more accurately and achieved higher directional symmetry (DS) than the other models. Accordingly, the MBO/SVR model is a robust, high-prediction-accuracy model for predicting long-term agricultural output to assist agricultural policymaking.

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

“…Machine learning is an evolutionary area of algorithms, hardware and storage systems working in smarter ways for several applications, such as (a) abnormal behavior proactive detection for reasonable solutions in advance; (b) creating events models based on system training in order to forecast the values of a future inquiry; (c) testing the future inquiry based on the understating of the created event model and (d) computing the individual loss reserve [12]. Thus, different researchers have used the advantage of machine learning for automated wheat diseases classification, estimation of the long-term agricultural output and prediction of soil organic carbon and available phosphorus [13][14][15]. Therefore, there are many benefits and advantages to using machine learning methods in computing the individual loss reserve regarding ML techniques, making such methods more feasible, with more accurate pricing, claims triage, loss prevention, a deep dive in changes in loss reserves and frequent monitoring to calculate claims reserves on individual claims data (ICR) [16][17][18][19].…”