Enhancing guava harvest forecasting in Bangladesh through supervised machine learning models

Abstract

Accurate forecasting of guava harvest is essential for efficient resource allocation, market planning, and mitigating post-harvest losses. In Bangladesh, the guava industry faces challenges in predicting harvest yields due to the complex interaction of various environmental factors. This study proposes a novel approach to enhance guava harvest forecasting in Bangladesh through the application of supervised ML models. The research leverages historical guava production data and corresponding meteorological variables, including temperature, humidity, precipitation, and solar radiation. These variables are used as input features for training and testing several supervised ML models, such as linear regression, decision trees, random forests, support vector machines, and artificial neural networks. A comprehensive dataset comprising guava production records and meteorological data from multiple regions in Bangladesh is collected and preprocessed. Feature engineering techniques are employed to extract relevant information from the data and optimize model performance. The dataset is then divided into training and testing sets for model development and evaluation. Performance metrics such as MAE, RMSE, MSE are used to assess the accuracy and reliability of the machine learning models. Where the highest accuracy 84.72% is achieved by DTR. And the lowest accuracy is achieved by LinR accuracy of 43.07%. The models' forecasting capabilities are compared, and the most effective model is identified. The results demonstrate that the supervised machine learning models exhibit promising performance in guava harvest forecasting, outperforming traditional statistical methods. The selected model achieves high accuracy and provides valuable insights into the influence of meteorological variables on guava production. The findings of this study have significant implications for the guava industry in Bangladesh, helping to enhance productivity, reduce wastage, and promote sustainable agricultural practices. Moreover, the methodology presented are extended to other regions and crops, facilitating improved harvest forecasting in diverse agricultural contexts.