Abstract
Mushrooms play a pivotal role in bolstering Australia’s economy, impacting key sectors like agriculture, food production, and medicinal advancements. To meet the escalating need for sustainable food options and enhance mushroom harvesting efficiency, this research: i) introduces an innovative dataset featuring three growth stages of oyster mushrooms; ii) designs a monitoring system which consists of image acquisition, cloud storage, label map and applications to achieve effective monitoring; and iii) proposes a label map method to monitor different stages within panoramic images captured from the real mushroom cultivation environment. Our preliminary studies show that the label map with state-of-art VGG-16 model emerges as the optimal choice, achieving an impressive accuracy of 82.22%. Our dataset can be obtained upon request.
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This research work is supported by Australian Research Council Early Career Industry Fellowship (IE230100119).
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Guo, L., Zhang, W.E., Chen, W., Yang, N., Nguyen, Q., Vo, T.D. (2024). Oyster Mushroom Growth Stage Identification: An Exploration of Computer Vision Technologies. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14471. Springer, Singapore. https://doi.org/10.1007/978-981-99-8388-9_6
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