Cotton Plant Disease Classification Using Data Mining Technique and Augmented Fast RCNN Algorithm

Abstract

Agriculture research is continuously developing as a consequence of technical improvements and difficulties that are being confronted. Implementation of this process is likely to result in a major acceleration in economic expansion in any nation. Specifically for cotton plant disease categorization, a thorough research programme is urgently required to boost agricultural output. Address agricultural challenges using data mining approaches to boost agricultural production.By using data mining approaches such as classification, we are able to predict crop diseases, production, and losses. As a result, farmers are able to make informed decisions. In this paper, proposed a prediction and classification model using Data mining technique with the combination of Augmented Fast Mask RCNN (AFMRCNN) for producing better accuracy in classification of cotton plant disease.According to the analysis of the experiment, the proposed algorithm provides more representative results and efficient detection of cotton plant disease than other existing methods.