Doctoral Thesis
Deep Learning Techniques for Green on Green Weed Detection from Imagery
Doctor of Philosophy (PhD), Murdoch University
2024
Abstract
Weed is a major problem faced by the agriculture and farming sector. Advanced imaging and deep learning (DL) techniques have the potential to automate various tasks involved in weed management. However, automatic weed detection in crops from imagery is challenging because both weeds and crops are of similar colour (green on green), and their growth and texture are somewhat similar; weeds vary based on crop, season and weather. Moreover, recognising weed species is crucial for applying targeted controlling mechanisms. This thesis focuses on improving the accuracy and throughput of deep learning models for weed species recognition. This thesis has the following contributions: First, we present a comprehensive literature review highlighting the challenges in developing an automatic weed species recognition technique.
Second, we evaluate several neural networks for weed recognition in various experimental settings and dataset combinations. Moreover, we investigate transfer-learning techniques by preserving the pre-trained weights for extracting the features of crop and weed datasets.
Third, we repurpose a public dataset and construct an instance-level weed dataset. We annotate the dataset using a bounding box around each instance and label them with the appropriate species of the crop or weed. To establish a benchmark, we evaluate the dataset using several models to locate and classify weeds in crops.
Fourth, we propose a weed classification pipeline where only the discriminative image patches are used to improve the performance. We enhance the images using generative adversarial networks. The enhanced images are divided into patches, and a selected subset of these are used for training the DL models.
Finally, we investigate an approach to classify weeds into three categories based on morphology: grass, sedge and broadleaf. We train an object detection model to detect plants from images. A Siamese network, leveraging state-of-the-art deep learning models as its backbone, is used for weed classification.
Our experiments demonstrate the proposed DL techniques can be used in detecting and classifying weeds at the species level and thereby help weed mitigation.
Details
- Title
- Deep Learning Techniques for Green on Green Weed Detection from Imagery
- Authors/Creators
- Mahmudul S Hasan
- Contributors
- Ferdous Sohel (Supervisor) - Murdoch University, Centre for Crop and Food InnovationMichael G. K. Jones (Supervisor) - Murdoch University, Centre for Crop and Food InnovationDean Diepeveen (Supervisor)Hamid Laga (Supervisor) - Murdoch University, Centre for Biosecurity and One Health
- Awarding Institution
- Murdoch University; Doctor of Philosophy (PhD)
- Identifiers
- 991005652868107891
- Murdoch Affiliation
- School of Information Technology
- Resource Type
- Doctoral Thesis
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