AGRICO - Smart Way of Farming

Abstract

Abstract: Research related to agriculture is growing rapidly, the challenges that lie ahead is solved with the help of advancement in technology. It is found that it is very beneficial for the economic growth and development of any nation. Especially in India, it requires the need for good research in order to improve agricultural productivity. In order to enhance the growth and solve the problems in the agricultural sector, the scientists use a variety of data mining methods. Different data mining techniques, like classification or prediction can be used to predict diseases in crops, and losses incurred as a result of these diseases. The diseases can be bacterial, fungal or viral. Some of the common plant diseases are bacterial wilt, black knot, curly top, etc. These diseases are caused by a variety of insects and micro- organisms. Our main focus in this research is on early identification of the diseases and helps the farmers in taking the decision to use the fertilizers that helps to protect the crops so that the diseases are eliminated in the early stage of production and so the farmers can get maximum yield. Ensemble method combines several classifiers to produce one finest predictive model and it is a very important technique in Machine Learning. In this paper, ensemble methods are used to predict the crop disease and an analyse has been done with the help of different classifiers such as Decision Trees, Naive Bayes Classifier, Random Forests, Support Vector Machine and K- Nearest Neighbour. Ensemble models, improves the performance of the classifiers that are weak. Te proposed machine learning approach that aims at predicting the best yielded crop for a particular region by analysing various atmospheric factors like rainfall, temperature, etc., and land factors like soil type including past records of crops grown. Finally, our system is expected to predict the best yielded crop based on dataset we have collected.