Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118939
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Type: Journal article
Title: In-field automatic observation of wheat heading stage using computer vision
Author: Zhu, Y.
Cao, Z.
Lu, H.
Li, Y.
Xiao, Y.
Citation: Biosystems Engineering, 2016; 143:28-41
Publisher: Elsevier
Issue Date: 2016
ISSN: 1537-5110
Statement of
Responsibility: 
Yanjun Zhu, Zhiguo Cao, Hao Lu, Yanan Li, Yang Xiao
Abstract: Growth stage information is an important factor for precision agriculture. It provides accurate evidence for agricultural management as well as early evaluation of yield. However, the observation of critical growth stages mainly relies on manual labour at present. This has some limitations because it is time-consuming, discontinuous and non-objective. Computer vision technology can help to alleviate these difficulties when monitoring growth status. This paper describes a novel automatic observation system for wheat heading stage based on computer vision. Images compliant with statistical requirements are taken in natural conditions where illumination changes frequently. Wheat plants with low spatial resolution overlap substantially, which increases observational difficulties. To adapt to the complex environment, a two-step coarse-to-fine wheat ear detection mechanism is proposed. In the coarse-detection step, machine learning technology is used to emphasise the candidate ear regions. In the fine-detection step, non-ear areas are eliminated through higher-level features. For that purpose, scale-invariant feature transform (SIFT) is densely extracted as the low-level visual descriptor, then Fisher vector (FV) encoding is employed to generate the mid-level representation. Based on three consecutive year's data of seven image sequences, a series of experiments are conducted to demonstrate the effectiveness and robustness of our proposition. Experimental results show that the proposed method significantly outperforms other existing methods with an average value of absolute error of 1.14 days on the test dataset. The results indicate that automatic observation is quite acceptable compared to manual observations.
Keywords: Automatic observation; heading stage; computer vision; SIFT; FV
Rights: ©2015 IAgrE. Published by Elsevier Ltd. All rights reserved.
RMID: 0030099651
DOI: 10.1016/j.biosystemseng.2015.12.015
Appears in Collections:Computer Science publications

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