Crop Recommendation in Precision Agriculture using Supervised Learning Algorithms

Abstract

Farming plays a significant role in the production and employment sector of India. One of the common problems faced by farmers is determining the plant based on soil kind and its conditions, sowing season, and geographical location. The soil testing program starts with the collection of a soil sample from an agriculture field. It is the important factor of economy in India and till now many of the methods followed by agriculture is outdated. Precision agriculture has provided a solution to this problem for farmers. Agriculture has become more significant due to precision agriculture. It will be used to research data of soil characteristics, soil types, crop yield data collection and suggests the farmers the right crop based on their site-specific parameters. This problem is solved by proposing a crop recommendation system through an ensemble model with majority of voting technique using Data collection, K- Nearest Neighbours, Logistic regression and Support vector machine as learners to recommend a crop for the site-specific parameters with high accuracy and efficiency. Therefore, our proposed work would help farmers in sowing the right seed, plant identification based on soil requirements to increase productivity and acquire profit out of such a technique. This application can be used to increase crop yield and also recommend suitable crop their respective soil.