Intelligent Farming Techniques Using Machine Learning

Abstract

Agriculture is critical to India’s socioeconomic system. Agriculture is one of the most important industries in the Indian economy, accounting for more than 18% of GDP. Almost 58% of India’s population relies largely on agriculture for a living, making India a prominent participant in the global agriculture business. Farmers plant the same crop every season rather than farming various sorts in different seasons. They also utilize extra fertilizers without understanding their exact composition or dose. Giving farmers timely access to insightful information would allow them to apply best practices and manage their property more effectively, reducing losses and increasing revenues. The proposed method assists farmers in selecting the best crop for their requirements while accounting for all aspects such as sowing season, soil, geographic location, and the best fertilizer to seed based on soil and weather conditions. This improves agricultural productivity and revenues. As a consequence, farmers may use our technology to produce fresh crops throughout the year at a better profit while reducing soil deterioration. This is possible because to the use of several machine learning algorithms. This strategy is implemented utilizing machine learning (ML) algorithms such as Decision tree, Random forest, Nave bayes, and KNN