Evaluation and Categorisation of Hispa Rice Disease Severity Levels Using CNN-RF Model

This study aims to detect the severity levels of Hispa Rice diseases. The study classifies the six different severity levels. This can assist with limiting harvest misfortunes and increment the yield acquired. Since India is an agricultural-based economy. A study was led in the nearby areas of Punjab and Haryana. A complete number of 4,000 pictures has been gathered by utilizing essential and optional assets. The gathered information is pre-handled by following advances like information cleaning, information quality evaluation, and information change. A convolutional brain organization (CNN) calculation is utilized to remove qualities from pre-handled pictures. Six convolutional layers are utilized in the CNN model. CNN model recognizes the highlights for infection order. The range of the F1-Score is from 86.03% of the healthy class to 89.41.% The macro average of the F1-Score is 91.53%. The range of the precision for this confusion matrix is 86.97 to 91.05. A profound learning test is completed using the Python library brings about a 96% recognition precision. Pushing ahead Utilizing work, we mean to complete the indistinguishable investigation utilizing while at the same time taking drugs. This research planned to examine the different boondocks bug the board innovations which will help for maintainable rice creation.