Applying Machine Learning to Soil Classification, Analysis, and Crop Suitability Recommendations in Agriculture

Abstract

Raising agricultural yields to meet the demands of a growing population is a top priority. A number of variables, including soil quality, rainfall, and environmental conditions, affect the agricultural yields of the majority of Indian farmers, who tend to own scattered farmland. India loses almost 5.3 billion tons of soil every year on average. Land that is in poor condition can no longer generate enough food. Soil fertility, which is dependent on nutrient levels, is a limiting factor in Indian agriculture; furthermore, soil might be optimal for growing certain crops but disastrous for others. Soil fertility, cultivation strategy, and crop yield predictions may all be informed by the soil's physical, chemical, and biological characteristics. Soil testing, potential agricultural yields, ML, supervised learning, and categorization are all relevant terms.