A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images
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Practical automated plant disease detection and diagnosis for wide-angle images (i.e. in-field images contain multiple leaves from fixed-position camera) is a very important application for large-scale farms management, ensuring the global food security. However, developing the automated disease diagnosis systems is often difficult because labeling a reliable disease wide-angle dataset from actual field is very laborious. In addition, the potential similarities between the training data and test data leads to a serious model overfitting problem. In this paper, we investigate changes in performance when applying disease diagnosis systems to different scenarios of wide-angle cucumber test data captured in real farms and propose a preferable diagnostic strategy. We show that the leading object recognition techniques such as SSD and Faster R-CNN achieve excellent end-to-end disease diagnostic performance on only the test dataset which is collected from the same population as the training dataset (81.5 disease cases), but it seriously deteriorates on the completely different test dataset (4.4 - 6.2 independent leaf detection and leaf diagnosis model attain a promising disease diagnostic performance with more than 6 times higher than the end-to-end systems (33.4 - 38.9 confirmed its efficiency from visual assessment, concluding that the two-stage models are suitable and a reasonable choice for the practical application.
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