Irene Nindita Pradnya, Uswatun Hasanah, Sarwi Asri, Nadya Alfa Cahaya Imani, Kakalia Putri Auralita, Anggun Enjelita
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. © Published under licence by IOP Publishing Ltd.
Department of Chemical Engineering, Faculty of Engineering, Universitas Negeri Semarang, Kampus Sekaran, Gunungpati, Semarang, Indonesia; Department of Computer Engineering, Faculty of Engineering, Universitas Negeri Semarang, Kampus Sekaran, Gunungpati, Semarang, Indonesia; Department of Mechanical Engineering, Faculty of Engineering, Universitas Negeri Semarang, Kampus Sekaran, Gunungpati, Semarang, Indonesia
