A Performance-Optimized Deep Learning-based Plant Disease Detection Approach for Horticultural Crops of New Zealand

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dc.citation.volume10
dc.contributor.authorSaleem MH
dc.contributor.authorPotgieter J
dc.contributor.authorArif K
dc.date.available23/08/2022
dc.date.issued23/08/2022
dc.description.abstractDeep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After finding the most suitable DL model, the data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.
dc.description.confidentialFALSE
dc.format.extent89798 - 89822
dc.identifier.citationIEEE Access, 2022, 10 pp. 89798 - 89822
dc.identifier.doi10.1109/ACCESS.2022.3201104
dc.identifier.elements-id455474
dc.identifier.harvestedMassey_Dark
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10179/17553
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.isPartOfIEEE Access
dc.relation.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9864587
dc.subjectConvolutional neural networks
dc.subjectcross-validation
dc.subjectdeep learning
dc.subjectoptimization algorithms
dc.subjectplant disease detection
dc.subject.anzsrc08 Information and Computing Sciences
dc.subject.anzsrc09 Engineering
dc.subject.anzsrc10 Technology
dc.titleA Performance-Optimized Deep Learning-based Plant Disease Detection Approach for Horticultural Crops of New Zealand
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment/Agritech
pubs.organisational-group/Massey University/College of Sciences/School of Food and Advanced Technology
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