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A survey of image-based computational learning techniques for frost detection in plants

Shammi, S., Sohel, F., Diepeveen, D.ORCID: 0000-0002-1535-8019, Zander, S. and Jones, M.G.K.ORCID: 0000-0001-5002-0227 (2022) A survey of image-based computational learning techniques for frost detection in plants. Information Processing in Agriculture . In Press.

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Free to read: https://doi.org/10.1016/j.inpa.2022.02.003
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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.

Item Type: Journal Article
Murdoch Affiliation(s): IT, Media and Communications
Centre for Crop and Food Innovation
Food Futures Institute
Publisher: Elsevier B.V. on behalf of KeAi Communications Co. Ltd
Copyright: © 2022 China Agricultural University.
URI: http://researchrepository.murdoch.edu.au/id/eprint/64083
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