A Critical Investigation on Disease Detection of Oryza Sativa (Rice) Using Image Processing Techniques
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Abstract
Rice is the most essential part of food intake in daily bias, which is single of the furthermost essential food sources for most of the world’s population. In such conditions, farmers struggle to identify disease in Rice leaf in the premature stage. This article describes the development of a copy acquisition application for rice leaf disease and image processing algorithms to determine the acute intensity of the disease. The pictures were acquired using the developed system and processed using MATLAB software. By quantifying the total and infected leaf area based on pixel-counting, the algorithm calculated the percentage of the infected leaf area using a ratio-based method. The study achieved a range of 15.53% to 41.23% of the total percentage of infected leaf area through the image processing method. In comparison, the leaf area meter method yielded a lower range of 23.49% to 28.98% of the total infected area. This implies the image-processing approach can capture a wider extent of infection. However, a deviation was observed between the two methods, ranging from -9.39% to 17.74% in the results of image processing. It explores the detection of diseases in rice plants, aiming to enhance accuracy, efficiency, and timeliness in disease management. Implementing this technology significantly benefits the agricultural sector by enabling early intervention, reducing crop losses, and promoting sustainable farming practices.