An Efficient Machine Learning Approaches for Crop Recommendation based on Soil Characteristics

Abstract

Farming is a major industry in most poor nations. Modern agriculture is continually progressing in terms of farming methods and agricultural innovations. Farmers may find it difficult to adjust to ever-evolving market, consumer, and policy demands. Among the challenges that farmers face, (i) Fixing the climate crisis brought on by deforestation and factory emissions (ii) Crop development may be hampered by deficiencies in soil nutrients brought on by a lack of minerals including potassium, N, and phosphorus. (iii) Farmers should avoid planting the same crops year after year without experimenting with anything new. They just throw on a bunch of fertilizers, regardless of how much or how good a quality they are. The purpose of this research is to determine which crop prediction model is the most effective at helping farmers make informed decisions about which crops to grow given a variety of environmental and agronomic variables. In this article, Selection Model is used to analyze the well-known algorithms including K-Nearest Neighbor.