A Machine Learning Based Crop Recommendation System: A Survey

Abstract

Agriculture and related industries are the most important sources of employment and income in rural Areas. The agricultural industry in the country also makes a significant contribution. Productivity as a percentage of GDP (GDP). The agricultural industry is a huge source of wealth for the country. However, when compared to other agricultural products, the yield per hectare is disappointing. Standards that are accepted all over the world. There are numerous reasons why marginal farmers in India have a higher suicide rate. This is a study paper. Farmers are recommended an effective and user-friendly yield prediction system. The proposed system connects Farmers through a smartphone app. GPS technology aid in user identification and location. The user specifies the area and type of soil in which they want to work, and machine learning algorithms enable the selection of the most profitable user-selected crop yield prediction or crop list. Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Multivariate Linear Network (MLN), and a combination of regression and KNN are used to estimate crop yields. The Random Forest produced the best results of the three. The rate of accuracy is 95%. Aside from that, the system recommends the best options available. It's time to start using chemical fertilizer to boost output