Agrotechnology Transfer Essay

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From the above discussion it is evident that Decision Support System for the Agrotechnology Transfer (DSSAT) is now a very important type of DSS providing services to millions of farmers and food processing industries around the world. Even with such high importance it hasn’t been able to attract significant attention from the researchers a decade ago. But evolution of technologies and constant stress over modernization of agricultural methods by domain experts has shifted the paradigm. There has been a tremendous development and very significant research in past five years. Techniques like precision agriculture are now a very hot area for researchers. It is a technique where they utilize information technology to manage and plan crops. It is often categorized as agricultural management systems which employ advanced sensor technology and data from geo sensing satellites and crop history database to help farmers take better decisions. They are also used as monitoring tools to analyze crop and land data. Precision agriculture techniques also help farmers make better decision by providing them with forecasts on crop prediction, fertility, rainfall and market demand. They collaborate with geo sensing satellites for land resources exploration, environment monitoring and protection, city planning, crop production evaluation, disaster prevention and mitigation, experiment of spatial scientific.
There has been recent research on integrating agro systems with internet and other very important public databases. Going online can help farmers get real time data on temperature, humidity, wind, atmospheric and rainfall data. Information like Ph values of soil, humidity, electrical conductivity can be measured using sensor technologies employed....

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...echnology in the agricultural sector has created opportunities for farmers to apply data from remote sensing satellites to their crops. Data mining especially been very handy in helping systems make better suggestions to farmers based on the legacy data. Crop predictions and quality forecasts are now more accurate. Artificial intelligence and neural networks have been employed to build adaptive systems that teach themselves from the results entered by the farmers. The systems are now “situation-aware”.
There has also been lot of research in building systems that can monitor and investigate soil fertility and nutrition levels. Soil fertility data with dynamic, spatial and temporal characteristics of soil are fed to the systems which then use data mining techniques to come to analyze and diagnose soil problems. Such information can be very critical for success of crop.

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