In-field automatic observation of wheat heading stage using computer vision. (March 2016)

• Record Type: Journal Article
• Authors: Zhu, Yanjun; Cao, Zhiguo; Lu, Hao; Li, Yanan; Xiao, Yang
• Is Part Of: Biosystems engineering. Volume 143(2016:Mar.)
• Page Start: 28
• Page End: 41
• Publication Date: 2016-03
• Subjects: Automatic observation -- Heading stage -- Computer vision -- SIFT -- FV; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2015.12.015
• Languages: English
• ISSNs: 1537-5110

Abstract

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. Graphical abstract: Highlights: Heading stage of wheat is directly observed using computer vision technology. A novel coarse-to-fine wheat ear detection mechanism is proposed. Machine learning technology is used to emphasise the candidate ear regions. Densely sampled SIFT and Fisher Vector are employed for feature representation.