Plant Disease Detection using Image Processing
DOI:
https://doi.org/10.54060/a2zjournals.jase.73Keywords:
Disease Detection, Convolutional Neural Network,, Deep Learning, MLAbstract
The proposed crop disease detection method integrates color standardization, image segmentation, texture feature computation, and feature extraction through a pre-trained deep neural network. Initiating with color space normalization, it segments images for disease signs, calculates texture attributes in these areas, and harnesses a pretrained CNN to extract critical disease-related features. This cohesive process optimizes disease identification accuracy, utilizing deep learning and image analysis techniques. By automating disease recognition based on visual symptoms, this system empowers farmers with an accurate, automated tool for distinguishing crop diseases from healthy areas, facilitating timely intervention and crop management.
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