Prediction of Accuracy and Performance of Crop Production by Using Machine Learning Algorithm

Machine learning is an emergent field of research in crop production analysis. The present study aims at shedding light on machine learning in agriculture along with “crop management,” “water management,” “soil management,” “fertilizer management.” Production prediction is a very important issue in agriculture. Any farmer wants to know what his expected production is about to expect. In the past, production predictions were made by considering farmers’ experience in specific fields and crops. Based on existing data, performance prediction is a major problem to be solved. Machine learning technology is the best choice for this purpose. Different machine learning techniques are used and evaluated in agriculture to estimate crop productions in the coming year. The aim of this paper proposes and implements a system that can predict crop productions based on previous data. This is achieved by applying machine learning algorithms like Naïve Bayes, generalized linear model, deep learning, decision tree, and random forest to agricultural data and recommending suitable fertilizers for each specific crop and it has been observed that random forest algorithm achieved better accuracy and performance to predict future crop productions.