Knowledge model-based decision support system for maize management

Abstract

Based on the relationship between crops and circumstances, a dynamic knowledge model for maize management with wide applicability was developed using the system method and mathematical modeling technique. With soft component characteristics incorporated, a component and digital knowledge model-based decision support system for maize management was established on the Visual C++ platform. This system realized six major functions: target yield calculation, design of pre-sowing plan, prediction of regular indices, real-time management control, expert knowledge reference and system administration. Cases were studied on the target yield knowledge model with data sets that include different eco-sites, yield levels of the last three years, and fertilizer and water management levels. The results indicated that this system overcomes the shortcomings of traditional expert systems and planting patterns, such as site-specific conditions and narrow applicability, and can be used more under different conditions and environments. This system provides a scientific knowledge system and a broad decision-making tool for maize management.