Strawberry Ripeness Detection Using Deep Learning Models
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Mi, Zhiyuan
Yan, Wei Qi
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MDPI AG
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In agriculture, the timely and accurate assessment of fruit ripeness is crucial to optimizing harvest planning and reduce waste. In this article, we explore the integration of two cutting-edge deep learning models, YOLOv9 and Swin Transformer, to develop a complex model for detecting strawberry ripeness. Trained and tested on a specially curated dataset, our model achieves a mean precision (mAP) of 87.3% by using the metric intersection over union (IoU) at a threshold of 0.5. This outperforms the model using YOLOv9 alone, which achieves an mAP of 86.1%. Our model also demonstrated improved precision and recall, with precision rising to 85.3% and recall rising to 84.0%, reflecting its ability to accurately and consistently detect different stages of strawberry ripeness.
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46 Information and computing sciences
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Big Data and Cognitive Computing, ISSN: 2504-2289 (Online), MDPI AG, 8(8), 92-92. doi: 10.3390/bdcc8080092
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Except where otherwise noted, this item's license is described as © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).