Abstract
Intelligent equipment is increasingly employed in strawberry production to enhance fruit yield. To effectively monitor strawberry growth, the utilization of deep learning, specifically convolutional neural networks, has demonstrated remarkable effectiveness. This research paper delves into the study of deep learning techniques for monitoring strawberry growth and explores their applications in disease detection, fruit ripeness assessment, and fruit target identification. In addition, it provides an insightful analysis of the challenges encountered from both application and model perspectives. Furthermore, this paper proposes future trends, including the amalgamation of disease and fruit target detection, as well as the fusion of multiple algorithms.
Supported by National Natural Science Foundation of China Youth Fund(No. 61802247), Natural Science Foundation of Shanghai(No.22ZR1425300), and Other projects of Shanghai Science and technology Commission(No.21010501000).
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Tian, S., Zhang, P., Wang, X. (2024). Deep Learning in Strawberry Growth Monitoring Research: A Review. In: Li, J., Zhang, B., Ying, Y. (eds) 6GN for Future Wireless Networks. 6GN 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-031-53404-1_7
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