Enhancing crops production based on environmental status using machine learning techniques

Abstract

Bangladesh is an agricultural country. As the economy is based on agriculture highly, there should be progress in this sector. To make progress in agriculture the productivity must be increased. These days, productivity is low due to various factors. One of them is not nding suitable crops for a particular land. In this way, the crops are not produced at the maximum amount. Hence, productivity of agriculture depends on multiple parameters on the basis of location. The suitable crop for a particular location is necessary for agriculture to bring the most productivity. Here we have designed a model that predicts productivity with given parameters, and also recommends the suitable crop based on those parameters. In terms of Machine Learning for the prediction and the recommendation, we have applied multiple algorithms like k-nearest neighbor, support vector machines, random forest, na ve Bayes' classi er and logistic regression, collaborative ltering and fuzzy K-Nearest neighbor. After training the dataset and applying algorithms, for prediction we have made a comparison by analyzing the precision. On the other hand, for recommendation we have used collaborative ltering system and fuzzy k-nearest neighbor. These algorithms are mainly used to take users data as input and test with the trained data that is already in the system and will lter out the best 5 crops as output.