Forecasting Agricultural Waste Composting Maturity Through Machine Learning: A Mini Review

Abstract

Abstract The effective management of agricultural waste through composting is essential for promoting sustainable waste practices and enhancing soil quality. Forecasting the maturity of compost is of utmost importance to assess its suitability and efficacy in enhancing soil as an amendment. Recently, the application of machine learning methods has risen as a robust solution for projecting compost maturity, showcasing enhanced precision and efficiency in contrast to conventional techniques. This article seeks to offer a comprehensive summary of the present research landscape concerning the utilization of machine learning in foreseeing the maturity of compost derived from agricultural waste. It provides insight into the methods used, challenges faced, and prospective paths for future investigation.