Recent Machine Learning Advances in Agriculture

Abstract

The economy of India is significantly influenced by agriculture. The majority of Indians are either directly or indirectly dependent on agriculture. Therefore, it cannot be denied that agriculture plays an important role in the nation. So it is important for former to make best decision while growing any crop based on multiple factors. In this era of advancement there are number of technologies which can be used in agriculture field one of these is Machine Learning (ML). In this paper we have summarized different ML algorithms that are implemented by different researchers in their studies for Crop recommendation, yield prediction. We have concluded that Random Forest, Support Vector Machine, Naïve Bayes, Neural Network, Decision Tree, Knearest neighbor, XGBoost(eXtreme Gradient boosting), Multivariate Linear Regression, Logistic Regression, Chi-Square Automatic Interaction Detection (CHAID) and Sliding Window non-Linear Regression comes with maximum accuracy. This prediction has been done based on various factors such as soil type, temperature, rainfall, pH value of soil, Nitrogen Phosphorus Potassium(NPK) content of soil , sowing season, porosity of soil ,erosion ,water holding, drainage, crop yield, crop consumed , humidity and location parameters. Furthermore in future ML algorithms can be utilized in various sectors of Agriculture like disease detection in crops, soil suitability recommendation and prediction of retail prices.