An Integrated Decision-Support System for Increasing Crop Yield Based on Progressive Machine Learning and Sensor Data
Keywords:
DSS, soil nutrient, fertilizer, Machine learning, precision farmingAbstract
The utilization of data has created a massive data tsunami that has affected almost every sector of the economy. The information wave has been amplified by a very significant amount as a direct result of the growing number of man-machine and machine-based digital data handling tools. There has been a significant rise in the number of digital applications in the agricultural industry. These applications are designed to provide improved services to both producers and consumers. The process of crop cultivation is carried out regularly in order to evaluate the various natural factors that affect the development and maintenance of a soil. Although soil has nutrients that are needed for proper cultivation the nutrient levels in the soil are decreasing due to the use of more fertilizer. This issue then led to the reduction of crop production. However, the uncertainty regarding the multiple factors that affect the soil’s character can lead to poor decision-making. The ability of agronomists and farmers make informed decision is dependent on the accurate climate and soil data. The concept of precision agriculture is connected to the supervision of the production of crops. DSS is responsible for the collection, organization and evaluation of a wide variety of data types using a variety of mathematical models. various type of crops and environmental data that are collected through sensors in order to improve the decision support system used in agricultural production. To increase the crop yield, an integrated decision support system (DSS) is proposed that takes into account the various components like soil nutrient value, crop details, fertilizer ratio used to predict the ideal crop, fertilizer and rainfall for a region.
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