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Deep Learning Application in Plant Stress Imaging: A Review

Zongmei Gao, Zhongwei Luo, Wen Zhang, Zhenzhen Lv, Yanlei Xu
Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA 99350, USA
AgriEngineering 2(3), 430-446, 2020

@article{gao2020deep,

   title={Deep Learning Application in Plant Stress Imaging: A Review},

   author={Gao, Zongmei and Luo, Zhongwei and Zhang, Wen and Lv, Zhenzhen and Xu, Yanlei},

   journal={AgriEngineering},

   volume={2},

   number={3},

   pages={430–446},

   year={2020},

   publisher={Multidisciplinary Digital Publishing Institute}

}

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Plant stress is one of major issues that cause significant economic loss for growers. The labor-intensive conventional methods for identifying the stressed plants constrain their applications. To address this issue, rapid methods are in urgent needs. Developments of advanced sensing and machine learning techniques trigger revolutions for precision agriculture based on deep learning and big data. In this paper, we reviewed the latest deep learning approaches pertinent to the image analysis of crop stress diagnosis. We compiled the current sensor tools and deep learning principles involved in plant stress phenotyping. In addition, we reviewed a variety of deep learning applications/functions with plant stress imaging, including classification, object detection, and segmentation, of which are closely intertwined. Furthermore, we summarized and discussed the current challenges and future development avenues in plant phenotyping.
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