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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