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Table 3 Comparison of advantages and disadvantages of each sub-method of classification network

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

Method Advantages Disadvantages
Using network as feature extractor Obtaining effective lesion features Relying on other classifiers for final classification results
Original image classification Classic in structure, it is also the basis of other classification network sub-methods and can refer to many existing networks Lesions need to account for a certain proportion in the image, otherwise their characteristics are easily pooled out, and generally only one class of lesion is allowed in an image
Classification after locating ROI Obtaining ROI information of the lesions Additional methods are needed to obtain ROI
Multi-category classification Solving sample imbalance to some extent Secondary training is needed
Sliding window Get rough localization of lesions in images Sliding window size requires accurate selection, and can only get rough position, slow speed of traversal and sliding
Heatmap Generate more accurate lesion areas Accurate lesions location depends on network classification performance
Multi-task learning network Combining other networks to obtain exact location and category of lesions simultaneously, and reducing the number of training samples required The network structure is relatively complex, and a pixel-by-pixel label is required when adding segmentation branches