A farmer can use machine learning to make decisions about what crops to sow, how to care for those crops throughout the growing season, and how to predict crop yields. According to the World Health Organization, agriculture is essential to the nation’s quick economic development. Food security, access, and adoption are the three cornerstones of the organization. Without a doubt, the main priority is to ensure that there is enough food for everyone. Increasing agricultural yield can help ensure a sufficient supply. The country-wide variation in crop yields is substantial. As a result, this will be the foundation for research into whether cluster analysis can be used to identify crop yield patterns in a field. Previous study investigations were only marginally successful in accomplishing their primary intended objectives because of unstable conditions and imprecise methodology. The vast majority of farmers base their predictions of crop yield on prior observations of crop growth in their farms, which can be deceptive. Standard preprocessing methods and random cluster value selection are not always reliable, according to the literature. The proposed study overcomes the shortcomings of conventional methodology by highlighting the significance of machine learning-based classification/partitioning and hierarchical approaches in offering a trained analysis of yield prediction in the state of Karnataka. The dataset used for the study was collected from the ICAR-Taralabalu Krishi Vigyan Kendra, Davangere, Karnataka. In the two dataset analysis techniques employed in the study to find anomalies, crop area, and crop production are significant variables. Crop area and crop yield are important variables in the two dataset analysis methods used in the study to detect anomalies. The study emphasizes the importance of a mathematical model and algorithm for identifying yield trends, which can assist farmers in selecting crops that have a large seasonal impact on yield productivity.

In Precision Agriculture, a Decision Support System (DSS) is the ultimate stage that incorporates the basic findings derived from earlier procedures. In many cases, a DSS that is exclusively focused on data-driven methodologies might be highly influenced by data sources if these data resources do not provide any sense of intended conclusion. As a result, on-farm experiment inputs may result in incorrect site-specific crop management in the end. Since data is an essential element of DSS, irregular patterns such as outliers in spatial data that can change the nature of expected outcomes must be avoided during data-driven manipulations. Many of the approaches developed to detect outliers were not designed to deal with masking and swamping effects. With this consideration, the work presented here uses two iterative techniques to locate and remove spatial outliers based on their neighbourhood relationship. As a result of this technique, the masking and swamping effects are reduced. The methods we use are iterative, implying that each iteration discovers and eliminates the expected number of outlying observations. R-Studio is used to demonstrate the use of iterative approaches. The efficacy of both iterative procedures was analysed and compared using one of the current graphical approaches, such as the semivariogram. The research specifically looks at how well these strategies perform on a real-world dataset incorporating spatial observations. The statistical iterative techniques outperformed the graphical approach, according to the findings.