Pre Processing Of Paddy Leaf Disease Using Laplacian Filter
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Abstract
Automatic leaf disease detection in precision agriculture using the images of infected leaves using image processing, computer vision, and machine learning algorithms to determine the presence of illness. The automatic disease detection makes the farmer with accurate plant disease diagnosis that allows the completion of diagnosis procedure. It took a long time for the farmer to send in a stained leaf, where a pathologist confirmed the disease. Upon delayed response, there exist a reduction in the productivity of crop. Automating the disease detection is essential for diagnosis of agricultural diseases. In this paper, we improve the pre-processing operations of the images using Laplacian filter that removes the unwanted noises. The simulation is used to evaluate the proposed pre-processing and feature extraction with existing state-of-art methods. The simulation results suggest that the proposed method is more effective than other ways in enhancing the level of accuracy in diagnosing the entire plant.
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References
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