Crops Analysis And Classification Using Machine Learning Techniques Based on Soil and Environmental Characteristics

In the evolving landscape of modern agriculture, the fusion of technology with traditional methods is essential. This study explores the transformative potential of machine learning in crop classification, leveraging soil and environmental data. The K-Nearest Neighbors (KNN) model achieved an impressive 97.5% accuracy in pattern recognition, while ensemble models, such as Random Forest, Naïve Bayes, and XGBoost, excelled with a remarkable 99% accuracy, surpassing conventional methods. This research underscores the profound impact of machine learning in advancing agriculture, facilitating precision farming, and addressing global concerns like food security and sustainability. Additionally, the practical implications of this research are significant for farmers, providing them with the means to select crops based on their environmental characteristics, optimizing resource allocation and crop management, and contributing to agricultural sustainability in an era of environmental unpredictability and climate change.