Abstract
Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.
Highlights
The Deep Learning (DL) approach is a subcategory of Machine Learning (ML), introduced in 1943 [1] when threshold logic was introduced to build a computer model closely resembling the biological pathways of humans
There are some research papers previously presented to summarize the research based on agriculture by DL [43,54], but they lacked some of the recent developments in terms of visualization techniques implemented along with the DL and modified/cascaded version of famous DL models, which were used for plant disease identification
Training/validation accuracy were plotted to show the performance of the model; ResNet was considered as the best among all the plant disease, AlexNet and SqueezeNet v1.1 models were used in which AlexNet was found to be the better DL model in terms of accuracy [62]
Summary
The Deep Learning (DL) approach is a subcategory of Machine Learning (ML), introduced in 1943 [1] when threshold logic was introduced to build a computer model closely resembling the biological pathways of humans. There are some research papers previously presented to summarize the research based on agriculture (including plant disease recognition) by DL [43,54], but they lacked some of the recent developments in terms of visualization techniques implemented along with the DL and modified/cascaded version of famous DL models, which were used for plant disease identification. DL architectures along with visualization mapping/techniques used for plant disease detection; Section 3, elaborating upon the Hyperspectral Imaging with DL models; and Section 4, concluding the review and providing future recommendations for achieving more advancements in the visualization, detection, and classification of plants’ diseases
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