A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture
Ключевые слова:
Computer science, Greenhouse, Agriculture, Deep learning, Artificial intelligence, Computer vision, Geography, AgronomyАннотация
With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling data-driven decision-making. This paper provides a focused review of recent advancements in deep learning-enabled computer vision techniques tailored specifically for greenhouse environments. First, deep learning and computer vision fundamentals are briefly introduced. Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. Tables and figures showcase real-world implementations and results synthesized from current research. Key challenges are also outlined related to aspects like model adaptability, lack of sufficient labeled greenhouse data, computational constraints, the need for multi-modal sensor fusion, and other areas needing further investigation. Future trends and prospects are discussed to provide guidance for researchers exploring computer vision in the niche greenhouse domain. By condensing prior work and elucidating the state-of-the-art, this timely review aims to promote continued progress in smart greenhouse agriculture. The focused analysis, specifically on greenhouse environments, fills a gap compared to previous agricultural surveys. Overall, this paper highlights the immense potential of computer vision and deep learning in driving the emergence of data-driven, smart greenhouse farming worldwide.
Источник и лицензия. Статья открытого доступа, впервые опубликована как «A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture» в журнале IEEE Access, 2024. DOI: 10.1109/access.2024.3349418. © Jalal Uddin Md Akbar, Syafiq Fauzi Kamarulzaman, Abu Jafar Md Muzahid, Md. Arafatur Rahman, Mueen Uddin. Воспроизведена без изменений по лицензии Creative Commons Attribution 4.0.
Библиографические ссылки
He, K., Zhang, X., Ren, S. et al. (2016). Deep Residual Learning for Image Recognition. https://doi.org/10.1109/cvpr.2016.90
Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture notes in computer science. https://doi.org/10.1007/978-3-319-24574-4_28
Ren, S., He, K., Girshick, R. et al. (2016). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2016.2577031
Szegedy, C., Liu, W., Jia, Y. et al. (2015). Going deeper with convolutions. https://doi.org/10.1109/cvpr.2015.7298594
Long, J., Shelhamer, E., Darrell, T. (2015). Fully convolutional networks for semantic segmentation. https://doi.org/10.1109/cvpr.2015.7298965
Girshick, R., Donahue, J., Darrell, T. et al. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. https://doi.org/10.1109/cvpr.2014.81
Girshick, R. (2015). Fast R-CNN. https://doi.org/10.1109/iccv.2015.169
Liu, W., Anguelov, D., Erhan, D. et al. (2016). SSD: Single Shot MultiBox Detector. Lecture notes in computer science. https://doi.org/10.1007/978-3-319-46448-0_2
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Copyright (c) 2024 Jalal Uddin Md Akbar, Syafiq Fauzi Kamarulzaman, Abu Jafar Md Muzahid, Md. Arafatur Rahman, Mueen Uddin
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
Статьи публикуются под лицензией Creative Commons Attribution 4.0 International (CC BY 4.0).
