Data Mining in Agriculture

Data mining is an evolving research field within agriculture, offering significant advantages for managing large datasets. Various data mining techniques have been developed to address the unique challenges present in agriculture. One notable tool for this domain is WEKA, although proficiency in handling such tools and techniques is essential. In agriculture, classification methods such as K-Nearest Neighbor (IBK), Jrip, J48, Neural Network (ANN), clustering methods including the Elbow method, Silhouette method, Density-based clustering, Hierarchical clustering, and K-means clustering, as well as association rule mining and regression analysis (MLR), are commonly employed. These techniques are utilized for tasks such as weather and yield prediction, soil data classification, and soil fertility analysis. The objective of this work is to identify suitable data models that can achieve high accuracy and generality in predicting yield, prices, soil fertility, weather patterns, and rainfall. To accomplish this, various data mining techniques are being assessed.