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

Zhao, Yiru; Gong, Liang; Zhou, Bin; Huang, Yongfeng; Liu, Chengliang

doi:10.1016/j.biosystemseng.2016.05.001

Cited by 128 publications

(65 citation statements)

References 16 publications

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“…From the three primary colors, different kinds of color spaces can be calculated by using either linear or nonlinear transformations [21], such as HSI, YCbCr and L*a*b* etc. To find the more effective color components for the summer black grape, in the preliminary works of this study, the grape images captured in the vineyard conditions were observed by decomposing them to various color spaces [8]. The detailed steps are as follows: firstly, because a substantial amount of noise that caused by varying illumination in the vineyard usually existed in the captured images, the mean filter was implemented to minimize the disruption to target detection.…”

Section: Methodsmentioning

confidence: 99%

“…The detection methods based on the advanced vision sensors and fusion of multiple image data can achieve higher correct detection rates. However, the high-cost of the sensors is the key shortcoming for commercial application [8]. …”

Section: Introductionmentioning

confidence: 99%

“…Chamelat et al [23] developed a grape detection approach based on support vector machine (SVM) by selecting Zernike moments and color information as features. 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.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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 processor working on 640 by 480 pixel images, faces are detected at 32 frames per second. Among the face detection algorithms, the AdaBoost [1][2][3][4] based method is generating the strong learner by iteratively adding weak learners. In this paper Simple AdaBoost learning algorithm is used.…”

Section: The Proposed Methods Video Based Face Recognizationmentioning

confidence: 99%

“…Several approaches are used to resolve the problems such as K-Nearest Neighbor (K-NN), Artificial Neural Network (NN), Adaptive boost (Adaboost) [14], Support Vector Machine (SVM). They had used a variant of the AdaBoost algorithm [1,2] which attains rapid and robust face recognition in images. This paper provides the innovative solution for automatic real time face recognition from the video by the following algorithms like Adaboost, cascade classifier, and local binary pattern Histogram (LBPH).…”

Section: Introductionmentioning

confidence: 99%

Boosting Based Implementationof Biometric Authentication in IoT

Thilagavathi

Suthendran

2018

JCSM

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In security and control application the biometric authentication played a specific and important role to identify the person. Analysis of face recognization is the prerequisite process for the entire authentication. This paper focuses an automatic real-time implementation of face recognization system by highlevel description language such as python. Comparing the biometrics where still images are used, video based biometric holds ample information than a single image. It provides the innovative solution for automatic real time face recognition from the video by the following algorithms like Adaboost, Haar cascade classifier and local binary pattern Histogram (LBPH). From the video stream, the input images are trained by adaboost algorithm which is implemented in cascade classifier. AdaBoost is called as adaptive boosting algorithm. It is a learning algorithm which has been combined with a weak classifier in order to form a strong classifier. Here, the real time testing image is compared with the 627 frames of trained images which are obtained from 47.7 MB (56088648 bytes) video. The hardware implementation of face recognization is obtained and the result can be stored and viewed in http://169.254.108.24. Thus by the above said procedures are authenticated by the combinational performance of Adaboost and Cascade classifier.

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Detecting Apples in Orchards Using YOLOv3

Kuznetsova

Maleva

Soloviev

2020

Computational Science and Its Applications – ICCSA 2020

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A machine vision system for detecting apples in orchards was developed. The system designed for use in harvesting robots is based on a YOLOv3 algorithm modification with pre- and postprocessing. As a result, apples that are blocked by leaves and branches, green apples on a green background, darkened apples are detected. Apple detection time averaged 19 ms with 90.8% Recall (fruit detection rate), and 7.8% FPR.

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

Cited by 128 publications

References 16 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

Boosting Based Implementationof Biometric Authentication in IoT

Detecting Apples in Orchards Using YOLOv3

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