Enhancing Image Quality in Crop Disease Diagnosis: A Comparative Evaluation of Laplacian and Average Filtering with Established Techniques

Abstract

The agricultural sector is vital to ensuring global food security, yet plant diseases caused by various environmental factors lead to significant reductions in crop yields. Early detection of such diseases is critical, as crop health directly affects both yield and quality. This research introduces a novel pre-processing algorithm designed to enhance the accuracy of crop disease detection from leaf images. The proposed algorithm is compared against three widely used filtering techniques: Median filtering, Wiener filtering, and Gaussian filtering. Before implementing the proposed approach, a detailed assessment of these conventional methods is conducted.The algorithm integrates Laplacian filtering and average filtering to optimize image pre-processing. The effectiveness of this approach is evaluated using performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Mutual Information (MI). Results indicate that the proposed method achieves a 0.34% increase in PSNR, a 0.92% reduction in MSE, and a 0.48% improvement in MI compared to existing techniques. These improvements underscore the algorithm's ability to enhance image quality and outperform traditional methods.The findings suggest that incorporating Laplacian and average filtering into the pre-processing pipeline introduces a more efficient methodology for image enhancement. This approach holds potential for advancing image analysis and interpretation in agriculture and other fields, providing a foundation for further innovation in image processing technologies.