Improving Fruit Detection for Robotic Fruit Harvesting

Bulanon, D M; Burks, Thomas F.; Alchanatis, V.

doi:10.17660/actahortic.2009.824.39

Cited by 14 publications

(7 citation statements)

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“…The occlusion of branches and leaves or stems in the natural environment also affects the recognition of fruits and vegetables. Some researchers have reduced the effects of branches and leaves and separated the overlapping fruits for high recognition accuracy (Bulanon, Burks, Alchanatis, Albrigo, et al, 2009; Changhui et al, 2017; Feng et al, 2011; Jiao et al, 2020; Jing et al, 2021; D. Li et al, 2019; Lv et al, 2016; H. B. Song et al, 2013; Y. Xu et al, 2015; Zeng et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Xu³

et al. 2022

Journal of Field Robotics

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Fruit images collected by picking robots in natural environments have problems such as uneven lighting, complex backgrounds, occlusion of branches and leaves, and overlapping fruits and vegetables, which greatly increases the difficulty for picking robots to accurately identify target fruits and vegetables. Meanwhile, most fruits and vegetables have similar‐color backgrounds. Compared with the ones with different colors in the background, fruits and vegetables in similar‐color backgrounds are similar to the colors of their leaves and surrounding weeds, which increases the difficulty of identification. Therefore, realizing the accurate identification of fruits and vegetables in similar‐color backgrounds is vital to realize the dynamic monitoring of the growth of fruits and vegetables and intelligent picking, which has important value and application prospects for optimizing plantation management and automated harvesting operations. The work summarized the specific characteristics of fruits and vegetables with the close‐color background and the different processing procedures in the identification process. The following methods have been summarized, for example, image acquisition, image preprocessing, feature extraction, image segmentation, and fruit and vegetable detection in the process, and some methods were compared. Finally, the current research was discussed, and a solution was proposed for the different processing steps of fruit and vegetable recognition in similar‐color backgrounds. Deep learning methods were combined with some traditional methods to identify fruits and vegetables with close‐color background, which provides references for further research in various aspects.

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Section: Discussionmentioning

confidence: 99%

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Xu³

et al. 2022

Journal of Field Robotics

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“…Bulanon et al [10] enhanced the portion occupied by fruit in images using a red chromaticity coefficient and adopted a circle detection method for classifying individual fruits. To improve fruit visibility, they acquired multiple views from different viewing angles for a portion of a tree canopy.…”

Section: Fruit and Vegetable Recognition And Classificationmentioning

confidence: 99%

Application of Image Processing in Fruit and Vegetable Analysis: A Review

Dubey

Jalal

2015

Journal of Intelligent Systems

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Images are an important source of data and information in the agricultural sciences. The use of image-processing techniques has outstanding implications for the analysis of agricultural operations. Fruit and vegetable classification is one of the major applications that can be utilized in supermarkets to automatically detect the kinds of fruits or vegetables purchased by customers and to determine the appropriate price for the produce. Training on-site is the underlying prerequisite for this type of arrangement, which is generally caused by the users having little or no expert knowledge. We explored various methods used in addressing fruit and vegetable classification and in recognizing fruit disease problems. We surveyed imageprocessing approaches used for fruit disease detection, segmentation and classification. We also compared the performance of state-of-the-art methods under two scenarios, i.e., fruit and vegetable classification and fruit disease classification. The methods surveyed in this paper are able to distinguish among different kinds of fruits and their diseases that are very alike in color and texture.

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“…However, in this case, the orchard was heavily pruned to maximize the size and quality of the individual fruit and thus their fresh market value. As a consequence, less than 80% of the individual paraguayo pieces overlap in the images acquired and this overlapping tends to disappear when the camera changes the relative point of view of the trees analyzed [ 35 ]. In the future, new efforts will be focused on differentiating between overlapping paraguayo pieces in a less specialized orchard but the assumption in this work is that the fruit do not overlap in the images analyzed.…”

Section: Approachmentioning

confidence: 99%

Definition of Linear Color Models in the RGB Vector Color Space to Detect Red Peaches in Orchard Images Taken under Natural Illumination

Teixidó

Font

Pallejà

et al. 2012

Sensors

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This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates.

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Improving Fruit Detection for Robotic Fruit Harvesting

Cited by 14 publications

References 0 publications

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Application of Image Processing in Fruit and Vegetable Analysis: A Review

Definition of Linear Color Models in the RGB Vector Color Space to Detect Red Peaches in Orchard Images Taken under Natural Illumination

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