Agricultural producers, consultants, service providers, and industry representatives are faced with crop management and cropping system decisions throughout the growing season. The need of the agricultural person to manage and predict a crop behavior over a wide range of planting dates, geographies and crops has become increasingly important as the need (value) for good, timely decisions and a decision making process has greatly increased. Use of crop simulation models incorporating local climatic conditions with management operations may increase the agricultural person's ability to make more timely and educated decisions. Soils vary in texture, drainage, native fertility, organic matter, structure, temperature, etc. All of these factors have an impact on how well a crop's root system can develop and take in essential elements for growth. Scientific crop growth simulation models have traditionally been used to address research problems, answer questions and most importantly, to increase knowledge on crop growth, development and yield. Agriculture comprises much of the Indian land area and is critical to environmental economic and social sustainability. Fuzzy system for maximum yield from crop is a system to predict the name of crop that will give maximum yield and this crop will be more suitable for a particular type of soil & atmospheric condition. Maximum yield from crops depends on the various soil parameters. In this system total 15 important soil parameters and 22 crops are considered. This system uses the hardware part, which is interfaced with PC to form a Intelligent system for prediction of crop. The software used is MATLAB with Fuzzy logic toolbox. The hardware part comprises of transducer/sensors, ADC 0809 is used to convert the analog quantity to its equivalent digital quantity, which is given to the system model. Input to the system is soil parameters, which are sensed using transducers and are converted in to equivalent digital values using ADC. The hardware part is interfaced to PC using RS232 bus. The output of a system is one most suitable crop depending on the current parameter of soil.

1. John Yen & Reze Langaris " Fuzzy logic intelligence and control and information"
2. Bart Kosko " Neural networks and fuzzy systems"
3. George J. Klir " Fuzzy sets and fuzzy logic theory and applications"
4. Timothy J. Ross " Fuzzy logic with engineering applications"
5. Rudra Pratap " Introduction to MATLAB"
6. H. J. Zimmerman "Fuzzy sets theory and its applications"
7. User's Guide " Fuzzy logic Toolbox"
8. Krishi- Sanvardini 2003-2004(PKV Akola)