TY - JOUR
T1 - Advancing precision agriculture
T2 - The potential of deep learning for cereal plant head detection
AU - Sanaeifar, Alireza
AU - Guindo, Mahamed Lamine
AU - Bakhshipour, Adel
AU - Fazayeli, Hassan
AU - Li, Xiaoli
AU - Yang, Ce
N1 - Publisher Copyright:
© 2023
PY - 2023/6
Y1 - 2023/6
N2 - Cereal plant heads must be identified precisely and effectively in a range of agricultural applications, including yield estimation, disease detection, and breeding. Traditional methods that rely on manual feature extraction and thresholding take a lot of time and work, and they are also impacted by crop variability. Deep learning algorithms can be used to automate this procedure because they can directly extract complicated information from images and produce cutting-edge outcomes. This review provides a comprehensive overview of recent research on deep learning-based head detection in cereal plants, emphasizing object detection and image segmentation. We also discuss the major benefits and drawbacks of different deep learning architectures and training methods, as well as examples of their application in maize, rice, wheat, and sorghum. Developing robust image processing algorithms, using deep learning in other domains like unmanned aerial vehicles, and utilizing large and diverse datasets are all challenges outlined in our study as future research directions. Through the integration of advanced computer vision techniques with precision agriculture, this paper attempts to promote further research and innovation in this intriguing field. We provide a thorough analysis of current developments in deep learning-based head detection for cereal plants and emphasize how this technology can contribute significantly to precision agriculture.
AB - Cereal plant heads must be identified precisely and effectively in a range of agricultural applications, including yield estimation, disease detection, and breeding. Traditional methods that rely on manual feature extraction and thresholding take a lot of time and work, and they are also impacted by crop variability. Deep learning algorithms can be used to automate this procedure because they can directly extract complicated information from images and produce cutting-edge outcomes. This review provides a comprehensive overview of recent research on deep learning-based head detection in cereal plants, emphasizing object detection and image segmentation. We also discuss the major benefits and drawbacks of different deep learning architectures and training methods, as well as examples of their application in maize, rice, wheat, and sorghum. Developing robust image processing algorithms, using deep learning in other domains like unmanned aerial vehicles, and utilizing large and diverse datasets are all challenges outlined in our study as future research directions. Through the integration of advanced computer vision techniques with precision agriculture, this paper attempts to promote further research and innovation in this intriguing field. We provide a thorough analysis of current developments in deep learning-based head detection for cereal plants and emphasize how this technology can contribute significantly to precision agriculture.
KW - Deep learning
KW - Object detection
KW - Plant head detection
KW - Precision agriculture
KW - Segmentation
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U2 - 10.1016/j.compag.2023.107875
DO - 10.1016/j.compag.2023.107875
M3 - Review article
AN - SCOPUS:85154047777
SN - 0168-1699
VL - 209
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 107875
ER -