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Review

Deep Learning Application in Plant Stress Imaging: A Review

1
Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA 99350, USA
2
School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China
3
College of Information and Technology, JiLin Agricultural University, Changchun 130118, China
*
Authors to whom correspondence should be addressed.
Received: 19 May 2020 / Revised: 27 June 2020 / Accepted: 7 July 2020 / Published: 14 July 2020
(This article belongs to the Special Issue Precision Agriculture Technologies for Management of Plant Diseases)
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. View Full-Text
Keywords: deep learning; convolutional neural network; crop stress; precision phenotyping deep learning; convolutional neural network; crop stress; precision phenotyping
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MDPI and ACS Style

Gao, Z.; Luo, Z.; Zhang, W.; Lv, Z.; Xu, Y. Deep Learning Application in Plant Stress Imaging: A Review. AgriEngineering 2020, 2, 430-446. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2030029

AMA Style

Gao Z, Luo Z, Zhang W, Lv Z, Xu Y. Deep Learning Application in Plant Stress Imaging: A Review. AgriEngineering. 2020; 2(3):430-446. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2030029

Chicago/Turabian Style

Gao, Zongmei, Zhongwei Luo, Wen Zhang, Zhenzhen Lv, and Yanlei Xu. 2020. "Deep Learning Application in Plant Stress Imaging: A Review" AgriEngineering 2, no. 3: 430-446. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2030029

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