Application of ML in Crop Selection for Precision Agriculture

Agriculture is a major industry contributing significantly to economic development. It is essential to study the agriculture system to provide food security. Despite technological advancements such as vertical farming, traditional farming methods, and beliefs still dominate the sector. By examining essential factors like soil composition, pH level, humidity, and rainfall using a variety of machine learning models, such as kernel naive bayes, Gaussian naive bayes, linear support vector machine (SVM), quadratic discriminant, quadratic SVM, this research aims to offer crop suggestions to farmers. Accurate crop prediction is essential for optimizing farming practices and ensuring a stable food supply. Machine learning algorithms effectively predict crop yields by utilizing historical weather patterns, soil quality, and crop production data. The study can help in improving crop management and ensuring food security.