IDENTIFICATION OF PADDY DISEASE ALONG ITS PROCESSING TIME
DOI:
https://doi.org/10.55197/qjssh.v4i3.251Keywords:
paddy disease, pre-processing, feature extraction, performance, SNR, efficiencyAbstract
Most tropical and subtropical nations in the world eat rice as their main meal. This involves hectare-sized paddy fields, whose upkeep and care becomes a tiresome undertaking for the farmers. The caregivers are unable to recognize ailments and are unable to finish the time-consuming task of crop care in a timely manner. This research offers a suggestion for a rapid identification of paddy into unhealthy plants as a result of this laborious operation. The purpose of this work is to offer a system for diagnosing and detecting paddy illness based on performance testing and image processing methods. The system, which was developed in C++ and tested using a dataset of contaminated leaf images, is used. The several varieties of spotted leaves caused by paddy disease are provided in this dataset. Image acquisition, pre-processing, feature extraction, and performance measurement are the system's four key processes. These steps each conduct their own tasks. In addition, this research provides a definitive description of the paddy illness. The image is shrunk and made grayscale in the first phase because it is the procedure for carrying out the subsequent actions. The sick areas are then divided into groups and given labels. As a result, the processing time and signal-to-noise ratio are calculated and summarized in the last phase. According to the study's findings, the suggested system can accurately and efficiently identify and categorize paddy disease in plants.
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