ABSTRACT
Recently, the knowledge-based system approach is gaining importance to ensure profitable and quality crop production compared to experience-based conventional methods. Knowledge-based systems provide insights on maintaining soil quality and reducing environmental degradation. Agriculture systems are very complex in nature due to involvement of numerous factors like plant and soil that determine the crop yield. Computerized decision support systems use dynamic simulation models. It considers the combined influence of several factors like soil, plant, growth, biotic, abiotic, and climatic systems in various interactions to accurately predict the crop yield. It combines the agriculture technical data processing in order to gain the knowledge embedded in crop growth models along with economic and environmental key considerations that impact the system. These comprehensive models produce a large set of potential outcomes and provide a balanced understanding of the whole system. Crop growth models are used to estimate potential crop yield, to estimate sensitivity of crop production to climatic change, to forecast yields etc. Process oriented crop models help in decision making and understanding agricultural systems and significantly contribute sustainable agriculture. In this chapter, crop models and decision support systems to predict crop yield and provide soil recommendations is proposed. The proposed system aids in strategic decision making and classification of seed and plant categories. Hyperspectral images are used to find additional information like plant health.
