Comprehensive Study of Data Mining Applications in Agriculture

Abstract

Due to sensor and satellite data, the agriculture business is transforming rapidly. Data collection is driving this transformation, bringing unprecedented opportunity to improve agricultural operations, resource usage, and harvest yields. These data-driven insights might transform agriculture, boosting hopes for sustainable and efficient farming. Data mining, a sophisticated method for extracting insights from massive datasets, has great potential for unearthing new and relevant agricultural data. Data mining allows farmers to use large data to make better choices, increasing production and sustainability.

Based on the content and case study, a comprehensive approach to data mining in agriculture includes preprocessing data to handle outliers and ensure consistency, exploratory data analysis to identify trends and correlations, decision trees, association rule mining, and predictive modeling like Random Forests. Cluster analysis and pattern recognition improve agricultural data interpretation, helping farmers choose the best methods and control diseases and pests. As seen in the case study, this complete methodology gives the agriculture sector practical information to maximize crop productivity and sustainability.