Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model

Guo, Wei; Kiran, R. Uday; Ninomiya, S.

doi:10.1016/j.compag.2013.04.010

Cited by 176 publications

(133 citation statements)

References 25 publications

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“…Several approaches have been developed for vision-based vegetation detection by using RGB as well as multispectral imagery of agricultural fields (Guo et al, 2013;Hamuda et al, 2016;TorresSanchez et al, 2015). Hamuda et al (2016) present a survey about plant segmentation in field images by analyzing several threshold based as well as learning based methods.…”

Section: Related Workmentioning

confidence: 99%

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Real-Time Blob-Wise Sugar Beets vs Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

Milioto

Lottes

Stachniss

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

118

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ABSTRACT:UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.

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Section: Related Workmentioning

confidence: 99%

“…They report a vegetation detection rate of around 90 − 100 %. In contrast, Guo et al (2013) apply a learning-based approach using a decision tree classifier for vegetation detection. They exploit spectral features using different color spaces based on RGB images to perform a prediction on a per-pixel basis.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Blob-Wise Sugar Beets vs Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

Milioto

Lottes

Stachniss

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

118

View full text Add to dashboard Cite

show abstract

“…Some approaches to achieve this are based on colour spaced transformation, where colour thresholds are applied to differentiate between green and senescent leaves (Casadesús et al 2007;Casadesús and Villegas 2014). Machine learning offers another solution to differentiate vegetation from background (Guo et al 2013), where the model can be trained using plant in various illuminating conditions. For TIR imaging, different methods have been investigated to extract plant temperature from the image as reviewed in Jones and Sirault (2014).…”

Section: Data Processingmentioning

confidence: 99%

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Virlet

Sabermanesh

Sadeghi‐Tehran

et al. 2017

Functional Plant Biol.

315

210

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Abstract. Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility.

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“…The output of vegetation segmentation is the fundamental element in the subsequent process of weed and crop discrimination as well as weed control (Meyer and Camargo Neto 2008;Steward et al 2004). There are two challenging issues for robust vegetation segmentation in agricultural field conditions: (1) to overcome the strongly varying natural illumination (Jeon et al 2011;Wang et al 2012); (2) to avoid the influence of shadows under direct sunlight conditions (Guo et al 2013;Zheng et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Improved vegetation segmentation with ground shadow removal using an HDR camera

2017

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A vision-based weed control robot for agricultural field application requires robust vegetation segmentation. The output of vegetation segmentation is the fundamental element in the subsequent process of weed and crop discrimination as well as weed control. There are two challenging issues for robust vegetation segmentation under agricultural field conditions: (1) to overcome strongly varying natural illumination; (2) to avoid the influence of shadows under direct sunlight conditions. A way to resolve the issue of varying natural illumination is to use high dynamic range (HDR) camera technology. HDR cameras, however, do not resolve the shadow issue. In many cases, shadows tend to be classified during the segmentation as part of the foreground, i.e., vegetation regions. This study proposes an algorithm for ground shadow detection and removal, which is based on color space conversion and a multilevel threshold, and assesses the advantage of using this algorithm in vegetation segmentation under natural illumination conditions in an agricultural field. Applying shadow removal improved the performance of vegetation segmentation with an average improvement of 20, 4.4, and 13.5% in precision, specificity and modified accuracy, respectively. The average processing time for vegetation segmentation with shadow removal was 0.46 s, which is acceptable for real-time application (\1 s required). The proposed ground shadow detection and removal method enhances the performance of vegetation segmentation under natural illumination conditions in the field and is feasible for real-time field applications.

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Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model

Cited by 176 publications

References 25 publications

Real-Time Blob-Wise Sugar Beets vs Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

Real-Time Blob-Wise Sugar Beets vs Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Improved vegetation segmentation with ground shadow removal using an HDR camera

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