A weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables

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dc.citation.volume13
dc.contributor.authorSaleem MH
dc.contributor.authorPotgieter J
dc.contributor.authorArif K
dc.coverage.spatialSwitzerland
dc.date.available2022
dc.date.available2022-09-22
dc.date.issued25/10/2022
dc.description.abstractDeep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.
dc.description.publication-statusPublished online
dc.format.extent1008079 - ?
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/36388538
dc.identifier.citationFront Plant Sci, 2022, 13 pp. 1008079 - ?
dc.identifier.doi10.3389/fpls.2022.1008079
dc.identifier.elements-id457396
dc.identifier.harvestedMassey_Dark
dc.identifier.issn1664-462X
dc.identifier.urihttps://hdl.handle.net/10179/17804
dc.languageeng
dc.publisherFrontiers Media
dc.relation.isPartOfFront Plant Sci
dc.subjectconvolutional neural networks
dc.subjectcross-validation
dc.subjectdeep learning
dc.subjectoptimization algorithms
dc.subjectplant disease detection
dc.subjecttransfer learning
dc.subject.anzsrc0607 Plant Biology
dc.titleA weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables
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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