A Sequential Method for Enhancing the Image Quality of Rice Leaf Diseases by Employing Various Preprocessing Techniques

Rice is a fundamental staple crop worldwide, and monitoring the health and growth of rice plants is crucial for ensuring optimal yield and quality. Automated rice leaf segmentation from images plays a vital role in plant health assessment and disease detection, enabling early intervention and management. This study presents an approach for rice leaf segmentation using image processing techniques, aiming to automate the analysis of rice plant health. The proposed methodology involves several key steps. First, pre-processing techniques such as grayscale conversion, noise reduction, and contrast enhancement are applied to prepare the input images. Subsequently, various segmentation algorithms, including thresholding, edge detection, and region-based methods, are employed to isolate the rice leaves from the background. Feature extraction is then performed to capture relevant characteristics of the leaves, facilitating a detailed analysis.