Real-time progressive hyperspectral remote sensing detection methods for crop pest and diseases

Taixia Wu; Lifu Zhang; Bo Peng; Hongming Zhang; Zhengfu Chen; Min Gao

doi:10.1117/12.2225874

19 May 2016 Real-time progressive hyperspectral remote sensing detection methods for crop pest and diseases

Taixia Wu, Lifu Zhang, Bo Peng, Hongming Zhang, Zhengfu Chen, Min Gao

Proceedings Volume 9874, Remotely Sensed Data Compression, Communications, and Processing XII; 987410 (2016) https://doi.org/10.1117/12.2225874
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

Crop pests and diseases is one of major agricultural disasters, which have caused heavy losses in agricultural production each year. Hyperspectral remote sensing technology is one of the most advanced and effective method for monitoring crop pests and diseases. However, Hyperspectral facing serial problems such as low degree of automation of data processing and poor timeliness of information extraction. It resulting we cannot respond quickly to crop pests and diseases in a critical period, and missed the best time for quantitative spraying control on a fixed point. In this study, we take the crop pests and diseases as research point and breakthrough, using a self-development line scanning VNIR field imaging spectrometer. Take the advantage of the progressive obtain image characteristics of the push-broom hyperspectral remote sensor, a synchronous real-time progressive hyperspectral algorithms and models will development. Namely, the object’s information will get row by row just after the data obtained. It will greatly improve operating time and efficiency under the same detection accuracy. This may solve the poor timeliness problem when we using hyperspectral remote sensing for crop pests and diseases detection. Furthermore, this method will provide a common way for time-sensitive industrial applications, such as environment, disaster. It may providing methods and technical reserves for the development of real-time detection satellite technology.

Citation Download Citation

Taixia Wu, Lifu Zhang, Bo Peng, Hongming Zhang, Zhengfu Chen, and Min Gao "Real-time progressive hyperspectral remote sensing detection methods for crop pest and diseases", Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 987410 (19 May 2016); https://doi.org/10.1117/12.2225874

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