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Remote Sensing
Volume 12
Issue 16
10.3390/rs12162578
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Review

Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review

by
Daoliang Li
1,2,3,4,5,*,
Pan Zhang
1,2,3,4,5,
Tao Chen
6 and
Wei Qin
7
1
National Innovation Center for Digital Fishery, China Agricultural University, P.O. Box 121, 17 Tsinghua East Road, Beijing 100083, China
2
Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture China Agriculture University, Beijing 100083, China
3
China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing 100083, China
4
Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agriculture University, Beijing 100083, China
5
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
6
Department of Chemical and Process Engineering, University of Surrey, Guildford GU2 7XH, UK
7
College of Resources and Environmental Sciences; National Academy of Agricultural Green Development; Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2578; https://doi.org/10.3390/rs12162578
Submission received: 30 June 2020 / Revised: 4 August 2020 / Accepted: 5 August 2020 / Published: 11 August 2020
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Abstract

Recent development of non-destructive optical techniques, such as spectroscopy and machine vision technologies, have laid a good foundation for real-time monitoring and precise management of crop N status. However, their advantages and disadvantages have not been systematically summarized and evaluated. Here, we reviewed the state-of-the-art of non-destructive optical methods for monitoring the N status of crops, and summarized their advantages and disadvantages. We mainly focused on the contribution of spectral and machine vision technology to the accurate diagnosis of crop N status from three aspects: system selection, data processing, and estimation methods. Finally, we discussed the opportunities and challenges of the application of these technologies, followed by recommendations for future work to address the challenges.
Keywords: crops; nitrogen status; diagnosis; spectroscopy; vision crops; nitrogen status; diagnosis; spectroscopy; vision
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MDPI and ACS Style

Li, D.; Zhang, P.; Chen, T.; Qin, W. Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review. Remote Sens. 2020, 12, 2578. https://doi.org/10.3390/rs12162578

AMA Style

Li D, Zhang P, Chen T, Qin W. Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review. Remote Sensing. 2020; 12(16):2578. https://doi.org/10.3390/rs12162578

Chicago/Turabian Style

Li, Daoliang, Pan Zhang, Tao Chen, and Wei Qin. 2020. "Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review" Remote Sensing 12, no. 16: 2578. https://doi.org/10.3390/rs12162578

APA Style

Li, D., Zhang, P., Chen, T., & Qin, W. (2020). Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review. Remote Sensing, 12(16), 2578. https://doi.org/10.3390/rs12162578

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