Characterization of invisible symptoms caused by early phosphorus deficiency in cucumber plants using near-infrared hyperspectral imaging technology

Shi, Jiyong; Wang, Yueying; Li, Zhihua; Huang, Xiaowei; Shen, Tingting; Zhang, Xiaobo

doi:10.1016/j.saa.2021.120540

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…This technique has been used to detect different types of stress in all sorts of different crops. It has proven to be particularly useful at determining the nitrogen concentration and the concentration of other nutrients of crops, including lettuce [9], maize [10] [11], wheat [12], cucumber [13] and apple [14]. Infection by bacteria, fungi and viruses can also change the optical properties of a crop and so the technique has been used to detect infection with several of these pathogens.…”

Section: Hyperspectral Data and Thinking Machines For A Utopian Agric...mentioning

confidence: 99%

Hyperspectral Sensing and Plant Stress

Cullinan

2023

Laimburg Journal

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Light and electromagnetic radiation possess properties that allow it to interact with matter. By using hyperspectral technology that measures electromagnetic radiation and therefore its interaction with matter, we can infer things about the quality of the matter. This technique can be and is being applied in the field of agriculture. This article provides an introduction to electromagnetic radiation, measuring it with hyperspectral technology and how the technique is used in agriculture for the detection of plant stress and the consequent sustainable management thereof. Importantly, it is intended to be entertaining and easy-to-read, particularly for those who do not have extensive backgrounds in the fields of physics and remote sensing.

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Section: Hyperspectral Data and Thinking Machines For A Utopian Agric...mentioning

confidence: 99%

Hyperspectral Sensing and Plant Stress

Cullinan

2023

Laimburg Journal

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“…Therefore, the study of hyperspectral data dimension reduction technology has become the key to improving the speed of online detection. Current dimension reduction methods mainly include genetic algorithm ( 19 ), principal component analysis ( 20 , 21 ), independent component analysis ( 22 ), and some deep learning ( 23 ) methods. However, most approaches have only been applied to hyperspectral test platforms, and online detection has not yet been realized.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral dimension reduction and navel orange surface disease defect classification using independent component analysis-genetic algorithm

He²,

Liu³

et al. 2022

Front. Nutr.

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Canker is a common disease of navel oranges that is visible before harvest, and penicilliosis is a common disease occurring after harvest and storage. In this research, the typical fruit surface, canker spots, penicillium spore, and hypha of navel oranges were, respectively, identified by hyperspectral imaging. First, the light intensity on the edge of samples in hyperspectral images was improved by spherical correction. Then, independent component images and weight coefficients were obtained using independent component analysis. This approach, combined with use of a genetic algorithm, was used to select six characteristic wavelengths. The method achieved dimension reduction of hyperspectral data, and the testing time was reduced from 46.21 to 1.26 s for a self-developed online detection system. Finally, a deep learning neural network model was established, and the four kinds of surface pixels were identified accurately.

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