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Published (Version of Record)CC BY-NC-ND V4.0, Open Access
Journal article
A survey of image-based computational learning techniques for frost detection in plants
Information Processing in Agriculture, Vol.10(2), pp.164-191
2023
Abstract
Frost damage is one of the major concerns for crop growers as it can impact the growth of the plants and hence, yields. Early detection of frost can help farmers mitigating its impact. In the past, frost detection was a manual or visual process. Image-based techniques are increasingly being used to understand frost development in plants and automatic assessment of damage resulting from frost. This research presents a comprehensive survey of the state-of the-art methods applied to detect and analyse frost stress in plants. We identify three broad computational learning approaches i.e., statistical, traditional machine learning and deep learning, applied to images to detect and analyse frost in plants. We propose a novel taxonomy to classify the existing studies based on several attributes. This taxonomy has been developed to classify the major characteristics of a significant body of published research. In this survey, we profile 80 relevant papers based on the proposed taxonomy. We thoroughly analyse and discuss the techniques used in the various approaches, i.e., data acquisition, data preparation, feature extraction, computational learning, and evaluation. We summarise the current challenges and discuss the opportunities for future research and development in this area including in-field advanced artificial intelligence systems for real-time frost monitoring.
Details
- Title
- A survey of image-based computational learning techniques for frost detection in plants
- Authors/Creators
- S. Shammi (Author/Creator) - Murdoch University, School of Information TechnologyF. Sohel (Author/Creator) - Murdoch UniversityD. Diepeveen (Author/Creator) - Murdoch UniversityS. Zander (Author/Creator) - Murdoch UniversityM.G.K. Jones (Author/Creator) - Murdoch University
- Publication Details
- Information Processing in Agriculture, Vol.10(2), pp.164-191
- Publisher
- Elsevier B.V. on behalf of KeAi Communications Co. Ltd
- Identifiers
- 991005544687207891
- Copyright
- © 2022 China Agricultural University.
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Information Technology; Food Futures Institute
- Language
- English
- Resource Type
- Journal article
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