Global Journal of Computer Science and Technology, G: Interdisciplinary, Volume 23 Issue 1
Applications of Emerging Smart Technologies in Farming Systems: A Review © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue I Version I 60 ( ) Year 2023 G capacity (AWC), uncertainty and variability Irrigation , optimal crop water allocations GA models 4 . Weed and Nitrogen Management Classification of multispectral images Precision Agriculture (Distinguishing between chemical fertilizer and manure treatments) spatial decision support system (SDSS) o) Data Mining Techniques a Boon for Modern Agriculture Research Data mining is a highly interdisciplinary field that includes statistics, machine learning, databases, pattern recognition, and other disciplines ( Choudhary V K et al. 2013). Data mining is the time-consuming process of discovering authentic, relevant, potentially useful, and eventually visual patterns in data . The pattern should be novel and potentially beneficial, resulting in some benefit to the user or activity (Choudhary VK et al. 2011). Furthermore, if not instantly, then after some post- processing, the pattern should be understandable. Data mining is the process of extracting hidden predictive information from large databases. (Robert P. Schumaker et al. 2010). Data mining techniques forecast future trends and behaviours, allowing enterprises to make more informed decisions. The automated, prospective analysis provided by data mining goes beyond the retrospective analysis provided by decision support system tools. Agriculture data mining is a relatively new field of research. It entails the application of data mining techniques to agroeco systems. For example, the Naive Bayes data mining technique was developed to categorize soils using massive experimental soil profile datasets. Data miners use the decision tree method and clustering approaches (based on partitioning algorithms and hierarchical algorithms for forecasting soil fertility) to find information on productive agricultural land. ( Hassina AitIssad et al. 2019 ) p) Application of Data Mining in Smart Farming i. Grading Segregation of Fruits and Vegetables Fruits and vegetables are frequently classified into different price ranges based on size, color, and water content. These external variables, however, cannot be used to assess the quality of fruits and vegetables properly. Data mining can help us solve this problem by capturing images of fruits and vegetables at the packaging line. These images are then further analyzed to estimate the product's quality accurately. Furthermore, data from various specimens help to develop a more precise prediction of the quality of fruits and vegetables. These images can be fed into a deep layered Convolutional neural network for large-scale image recognition. ( Bagal Yash V et al. 2020) ii. Maximizing Yield Depending on the Quality of the Soil Assessing soil quality is necessary to hike the agricultural income from farmers' land. Assessing soil quality analyzes the amounts of minerals and nutrients in the soil, the alkalinity, salinity, moisture content, and other variables that also affect the soil quality. Data Genetic Algorithm (GA) Decision Tree (DT )
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