Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer

Berenstein, Ron; Ben-Shahar, Ohad; Shapiro, Amir; Edan, Yael

doi:10.1007/s11370-010-0078-z

Cited by 130 publications

(84 citation statements)

References 15 publications

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“…In the aspect of fruit detection in images, Berenstein et al [9] utilized the difference in edge distribution between the grape clusters and the foliage to detect the grape clusters for an automatic selective vineyard sprayer. In [10], the number of grape berries were counted by detecting specular spherical reflection peaks in RGB images captured at night under artificial illumination.…”

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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“…Other research in this field [10], [11] have results near to 98% in the first case, and 91% in the second. But in the first case the results came from the detection of individual berries, reason why, are not comparable with our result, because we estimate bunch's area.…”

Section: Discussionmentioning

confidence: 70%

Feature extraction on vineyard by Gustafson Kessel FCM and K-means

Correa

Valero

Elorza

et al. 2012

2012 16th IEEE Mediterranean Electrotechnical Conference

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Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color images segmentation, but Fuzzy CMeans has been widely used, because it has a good performance in a wide class of images. However, it is not adequate for noisy images and it takes longer runtimes, as compared to other method like K-means. For this reason, several methods have been proposed to improve these weaknesses. Methods like Fuzzy C-Means with Gustafson-Kessel algorithm (FCM-GK), which improve its performance against the noise, but increase significantly the runtime. In this paper we propose to use the centroids generated by GK-FCM algorithms as seeding for K-means algorithm in order to accelerate the runtime and improve the performance of K-means with random seeding. These segmentation techniques were applied to feature extraction on vineyard images. Segmented images were evaluated using several quality parameters such as the rate of correctly classified area and runtime.

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“…Currently, the mainstream direction for robotics in agriculture is full automation, with the best existing algorithms and machinery being about 85-90% effective 20 . However, in actual practice, this level of effectiveness might not be acceptable.…”

Section: Resultsmentioning

confidence: 99%

User interface considerations for telerobotics: the case of an agricultural robot sprayer

et al. 2014

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Agricultural robots can tackle harsh working conditions and hardness of work, as well as the shortage of laborers that is a bottleneck to agricultural production. Such robots exist, but they are not yet widespread. We believe that the limited usage of robotics in agriculture could be related to the fact that the mainstream direction for robotics in agriculture is full automation. The teleoperation of an agricultural robotic system can enable improved performance overcoming the complexity that current autonomous robots face due to the dynamic and unstructured agricultural environment. A field study was conducted to evaluate eight different user interfaces aiming to determine the factors that should be taken into consideration by designers while developing user interfaces for robot teleoperation in agriculture. Thirty participants, including farmers and agricultural engineers, were asked to use different teleoperation interaction modes in order to navigate the robot along vineyard rows and spray grape clusters. Based on our findings, additional views for target identification and peripheral vision improved both robot navigation (fewer collisions) and target identification (sprayed grape clusters). In this paper, we discuss aspects of user interface design related to remote operation of an agricultural robot.

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Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer

Cited by 130 publications

References 15 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

Feature extraction on vineyard by Gustafson Kessel FCM and K-means

User interface considerations for telerobotics: the case of an agricultural robot sprayer

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