IPECM Platform: An open-source software for greenhouse environment regulation using machine learning and optimization algorithm

Protected agriculture has emerged as a key solution to address the pressing issue of food scarcity. To enhance crop yield, environmental regulation techniques have been widely employed in protected production. However, the absence of user-friendly, data-driven tools for developing regulation models remains a challenge. This study aims to propose IPECM, an independent and user-friendly software platform for processing and analyzing crop photosynthetic rate (Pn) data and formulating environmental regulation targets. The platform provides functionalities, such as Pn prediction model development, environmental regulation model development and result visualization, supporting various machine learning algorithms and regulation target obtaining algorithms. The IPECM Platform's application is demonstrated through examples of light intensity regulation for cucumber growth and CO2 concentration regulation for tomato growth. The results showcase the software's ability to handle photosynthetic data of any dimension, with the established Pn prediction model achieving a coefficient of determination of 0.98 and a root mean square error lower than 1 mu mol center dot m(-2)center dot s (-1). The established regulation models can achieve maximum Pn or optimal energy utilization efficiency according to user requirements. IPECM Platform is an independent, automated, and open-source software for protected environmental regulation modeling, providing both the modeling process and results visualization. It offers valuable services for protected agriculture research, eliminating the need for programming knowledge.