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Table 1 Contrast between traditional image processing methods and deep learning methods

From: Plant diseases and pests detection based on deep learning: a review

Technology Traditional image processing methods Deep learning methods
Essence Manual design features + classifiers (or rules) Automatic learning of features from large amounts of data
Method Image segmentation method: Threshold segmentation; Roberts, Prewitt, Sobel, Laplace and Kirsh edge detection; region segmentation
Feature extraction method: SIFT, HOG, LBP, shape, color and texture feature extraction method
Classification method: SVM, BP, Bayesian
CNN
Required conditions Relatively harsh imaging environment requirements, high contrast between lesion and non-lesion areas, less noise Adequate learning data and high-performance computing units
Applicable scenarios It is often necessary to change the threshold or redesign the algorithm when imaging environment or plant diseases and pests class changes, which has poor recognition effect in real complex natural environment It has ability to cope with certain real and complex natural environment changes