Crop Yield Analysis Using Machine Learning Techniques

Abstract

Agriculture is an important application in India. The modern technology can change the situation of the farmers and decision making in agriculture field in a better way. Python is used as a front end for analysing the agricultural data set. Spyder IDE is used for data analysis and predicting the crop production. The parameters included in the data set are state name, district name, crop, average rainfall, average temperature and production for the season from January to December for the years 1977 to 2013. The machine learning algorithms like K-means clustering and support vector machine are being used. K-means clustering is an unsupervised learning algorithm which is being used for categorising the items into K groups of similarity. Support Vector Machine is one of the most popular supervised learning algorithm, which is used for classification in machine learning. The goal of SVM algorithm is to create the decision boundary or hyperplane that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. The result is represented using appropriate graphs.