In-field automatic observation of wheat heading stage using computer vision

Zhu, Yanjun; Chen, Zhiguo; Lü, Hao; Li, Yanan; Xiao, Yang

doi:10.1016/j.biosystemseng.2015.12.015

Cited by 80 publications

(68 citation statements)

References 28 publications

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“…In [8], an algorithm to detect tomatoes in greenhouse scenes was proposed, in which the possible tomato objects were firstly identified by extracting the Haar-like features of grey scale image and classifying by the AdaBoost classifier, and then the false classification were eliminated by color analysis approach. In [24], a two-step coarse-to-fine wheat ear detection mechanism was proposed. To improve the detection accuracy, the machine learning technology was used to identify candidate targets in the coarse-detection step.…”

Section: Introductionmentioning

confidence: 99%

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components

Luo

Tang

Zou

et al. 2016

Sensors

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The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination.

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Section: Introductionmentioning

confidence: 99%

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components

Luo

Tang

Zou

et al. 2016

Sensors

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“…The results of field testing showed that the proposed detection algorithm can alleviate the influence of environment variations. Zhu, Cao, Lu, Li, and Xiao (2016) provided a two-step coarse-to-fine wheat ear detection mechanism. In the coarse-detection step, machine learning technology was used to increase the correct detection rate.…”

Section: Introductionmentioning

confidence: 99%

Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis

Zhao

Gong

Zhou

et al. 2016

Biosystems Engineering

128

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“…CNN-based deep learning algorithms are also used for rice panicle detection in [9], and for sorghum panicle detection in [10] and [11]. Optical images were also used in [12] for wheat ear detection during the wheat heading stage, and in [13] for studying the flowering dynamics of rice plants. Both [12] and [13] use support vector machine (SVM) for detection.…”

Section: Introductionmentioning

confidence: 99%

“…Optical images were also used in [12] for wheat ear detection during the wheat heading stage, and in [13] for studying the flowering dynamics of rice plants. Both [12] and [13] use support vector machine (SVM) for detection. Algorithms mentioned above require a significant amount of labeled training data.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Bayesian learning for rice panicle segmentation with UAV images

Hayat

Cao

2020

Plant Methods

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Background: In this paper, an unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV) over paddy fields. Unlike existing supervised learning methods that require a large amount of labeled training data, the unsupervised learning approach detects panicle pixels in UAV images by analyzing statistical properties of pixels in an image without a training phase. Under the Bayesian framework, the distributions of pixel intensities are assumed to follow a multivariate Gaussian mixture model (GMM), with different components in the GMM corresponding to different categories, such as panicle, leaves, or background. The prevalence of each category is characterized by the weights associated with each component in the GMM. The model parameters are iteratively learned by using the Markov chain Monte Carlo (MCMC) method with Gibbs sampling, without the need of labeled training data. Results:Applying the unsupervised Bayesian learning algorithm on diverse UAV images achieves an average recall, precision and F 1 score of 96.49%, 72.31%, and 82.10%, respectively. These numbers outperform existing supervised learning approaches. Conclusions:Experimental results demonstrate that the proposed method can accurately identify panicle pixels in UAV images taken under diverse conditions.

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In-field automatic observation of wheat heading stage using computer vision

Cited by 80 publications

References 28 publications

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components

Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis

Unsupervised Bayesian learning for rice panicle segmentation with UAV images

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