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Proceedings Paper

A multimodal image sensor system for identifying water stress in grapevines
Author(s): Yong Zhao; Qin Zhang; Minzan Li; Yongni Shao; Jianfeng Zhou; Hong Sun
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Paper Abstract

Water stress is one of the most common limitations of fruit growth. Water is the most limiting resource for crop growth. In grapevines, as well as in other fruit crops, fruit quality benefits from a certain level of water deficit which facilitates to balance vegetative and reproductive growth and the flow of carbohydrates to reproductive structures. A multi-modal sensor system was designed to measure the reflectance signature of grape plant surfaces and identify different water stress levels in this paper. The multi-modal sensor system was equipped with one 3CCD camera (three channels in R, G, and IR). The multi-modal sensor can capture and analyze grape canopy from its reflectance features, and identify the different water stress levels. This research aims at solving the aforementioned problems. The core technology of this multi-modal sensor system could further be used as a decision support system that combines multi-modal sensory data to improve plant stress detection and identify the causes of stress. The images were taken by multi-modal sensor which could output images in spectral bands of near-infrared, green and red channel. Based on the analysis of the acquired images, color features based on color space and reflectance features based on image process method were calculated. The results showed that these parameters had the potential as water stress indicators. More experiments and analysis are needed to validate the conclusion.

Paper Details

Date Published: 9 November 2012
PDF: 8 pages
Proc. SPIE 8527, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV, 852712 (9 November 2012); doi: 10.1117/12.977419
Show Author Affiliations
Yong Zhao, China Agricultural Univ. (China)
Qin Zhang, Washington State Univ. (United States)
Minzan Li, China Agricultural Univ. (China)
Yongni Shao, Washington State Univ. (United States)
Jianfeng Zhou, Washington State Univ. (United States)
Hong Sun, China Agricultural Univ. (China)

Published in SPIE Proceedings Vol. 8527:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV
Allen M. Larar; Hyo-Sang Chung; Makoto Suzuki; Jian-yu Wang, Editor(s)

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