Dimensionality Reduction Using Principal Component Analysis and Multi-Label Fuzzy Classification for Rice Crop Disease in Rural Areas of India

Abstract

There is an expected growth of increase in the worlds’ food production to feed the growing population in the coming years. In this paper, the authors have taken into account the dataset depicting the rice crop disease and built a model so as to predict the early occurrence of the disease. In data mining models developed for the agricultural domain, researchers have done enormous amount of work that have benefited the farmers in overcoming the obstacles they face in effective and income gaining ways. This paper develops a model that reduces the computational cost involved using Principal Component Analysis (PCA) and multi-label fuzzy classifier. The model can help the farmers to identify the type of disease in the rice crop and take necessary steps to eradicate them as quickly as possible even at the early stage of the infection on the crops.