Deep learning-based approaches for plant disease and weed detection : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand

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dc.confidentialEmbargo : Noen_US
dc.contributor.advisorArif, Khalid
dc.contributor.authorSaleem, Muhammad Hammad
dc.date.accessioned2022-11-10T18:01:48Z
dc.date.accessioned2023-02-03T00:18:21Z
dc.date.available2022-11-10T18:01:48Z
dc.date.available2023-02-03T00:18:21Z
dc.date.issued2022
dc.descriptionListed in 2023 Dean's List of Exceptional Thesesen
dc.description.abstractTo match the ever-growing food demand, the scientific community has been actively focusing on addressing the various challenges faced by the agricultural sector. The major challenges are soil infertility, abrupt changes in climatic conditions, scarcity of water, untrained labor, emission of greenhouse gases, and many others. Moreover, plant diseases and weeds are two of the most important agricultural problems that reduce crop yield. Therefore, accurate detection of plant diseases and weeds is one of the essential operations to apply targeted and timely control measures. As a result, this can improve crop productivity, reduce the environmental effects and financial losses resulting from the excessive application of fungicide/herbicide spray on diseased plants/weeds. Among various ways of plant disease and weed detection, image-based methods are significantly effective for the interpretation of the distinct features. In recent years, image-based deep learning (DL) techniques have been reported in literature for the recognition of weeds and plant diseases. However, the full potential of DL has not yet been explored as most of the methods rely on modifications of the DL models for well-known and readily available datasets. The current studies lack in several ways, such as addressing various complex agricultural conditions, exploring several aspects of DL, and providing a systematic DL-based approach. To address these research gaps, this thesis presents various DL-based methodologies and aims to improve the mean average precision (mAP) for the identification of diseases and weeds in several plant species. The research on plant disease recognition starts with a publicly available dataset called PlantVillage and comparative analyses are conducted on various DL feature extractors, meta-architectures, and optimization algorithms. Later, new datasets are generated from various local New Zealand horticultural farms, named NZDLPlantDisease-v1 & v2. The proposed datasets consist of healthy and diseased plant organs of 13 economically important horticultural crops of New Zealand, divided into 48 classes. A performance-optimized DL model and a transfer learning-based approach are proposed for the detection of plant diseases using curated datasets. The weed identification has been performed on an open-source dataset called DeepWeeds. A two-step weed detection pipeline is presented to show the performance improvement of the deep learning model with a significant margin. The results for both agricultural tasks achieve superior performance compared to the existing method/default settings. The research outcomes elaborate the practical aspects and extended potential of DL for selected agricultural applications. Therefore, this thesis is a benchmark step for cost-effective crop protection and site-specific weed management systems (SSWM).en_US
dc.identifier.urihttp://hdl.handle.net/10179/17991
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectPlant diseasesen
dc.subjectWeedsen
dc.subjectIdentificationen
dc.subjectData processingen
dc.subjectImage processingen
dc.subjectDeep learningen
dc.subjectDean's List of Exceptional Thesesen
dc.subject.anzsrc300409 Crop and pasture protection (incl. pests, diseases and weeds)en
dc.subject.anzsrc461103 Deep learningen
dc.titleDeep learning-based approaches for plant disease and weed detection : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealanden_US
dc.typeThesisen_US
massey.contributor.authorSaleem, Muhammad Hammaden_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorMassey Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
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