Machine Learning Methods for the Prediction of Paddy Productivity in Nepal
Abstract
Machine Learning techniques have got a rich focus on agriculture management systems due to its significant improvement in classification algorithms. The agricultural data is difficult to study because they consist of different attributes such as geographic locations, soil types, and seasonal conditions. It is challenging to identify the most important attribute that affects the prediction of agriculture yields such as paddy productions.?? This study is mainly focused on the prediction of paddy productivity of a particular geographic location (Kanchanpur District) which is also categorized as a super zone for paddy cultivation by the Nepal Government. This study aims to collect the agriculture data using manual questionnaire designed with the help of agriculture experts and measure the performance of four machine learning algorithms, namely, Support Vector Machine, Neural Network, Na??ve Bayes and Decision Tree for the prediction of paddy productivity (low, medium and high). From the result analysis, it was seen that Decision Tree (SimpleCart) was able to classify 80.19% of the data correctly which was better than SVM, Na??ve Bayes and Neural Network in comparison to results of evaluation metrics.
Keywords???Paddy Productivity, Feature Selection, Machine Learning, Classifications.
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