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Crop Nutrition and Computer Vision Technology

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Abstract

Crop nutrition status can be reflected via leaf state and surface color. Traditional crop nutrition testing is mainly dominated by expert experience and chemical measurement, and expert experience is greatly affected by subjective factors. Although chemical measurement has high detection accuracy, its poor timeliness makes it difficult to achieve dynamic feedback control of nutrient solution, and the sampling process will bring certain damage to the crop. Computer vision technology such as hyperspectral remote sensing, visual image and 3D scanning detection technology has become a hotspot of crop nutrition detection because of its non-destructive, rapid and real-time characteristics. It is expected to develop into the main technology for real-time diagnosis of crop nutrition, provide basis for online information monitoring of crop nutrition and timely fertilization, thereby achieving automated and intelligent management of agricultural production.

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Acknowledgements

This work was supported by Fund of China Agriculture Research System (CARS-23), and the National Key Research and Development Program of China (2018YFD0201200).

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  1. College of Resources and Environmental Sciences, Southwest University, Chongqing, 400715, People’s Republic of China

    Qiu Peng & Weihong Xu

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  1. Qiu Peng
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Correspondence to Weihong Xu.

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Peng, Q., Xu, W. Crop Nutrition and Computer Vision Technology. Wireless Pers Commun 117, 887–899 (2021). https://doi.org/10.1007/s11277-020-07901-2

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