The use of dynamic sensing strategies to improve detection for a pepper harvesting robot

Kurtser, Polina; Edan, Yael

doi:10.1109/iros.2018.8593746

Cited by 15 publications

(21 citation statements)

References 19 publications

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“…For successful robotic harvesting, the robot must detect the fruit, reach the fruit, determine if the fruit is mature, detach the mature fruit from the plant, and transfer it to a container [ 2 ]. Most agricultural robotics research and development projects [ 3 , 4 , 5 ] focused on detecting [ 6 , 7 , 8 ], reaching [ 4 , 9 , 10 ], and detaching the fruit [ 4 , 9 ], with only a few studies focusing on maturity level determination [ 11 , 12 , 13 ]. Since different fruits can be in different maturity stages within the field and even on the same plant/branch, maturity classification is essential to enable selective harvesting [ 3 ] and an important element of an intelligent fruit-picking robot.…”

Section: Introductionmentioning

confidence: 99%

“…Detectability varies significantly between different viewpoints, with up to 50% differences [ 43 ]. Therefore, choosing the best viewpoint and the best number of viewpoints is essential for detection [ 6 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

“…Since the whole pepper must be examined in order to estimate its color percentage, the precise determination of sweet pepper fruit In the fruit grading process, multiple viewpoints or multiple cameras are used for maturity assessment [41]. However, equipping a harvester robot with multiple cameras can be expensive, and acquiring multiple viewpoints before harvesting a single fruit could be time consuming, leading to increased cycle times [6]. Therefore, research on the best viewpoints to estimate fruit maturity while using the minimal number of viewpoints is essential for the development and the cost effectiveness of harvesting robots.…”

Section: Introductionmentioning

confidence: 99%

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Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

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The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Viewpoint Analysis for Maturity Classification of Sweet Peppers

Harel

Essen

Parmet

et al. 2020

Sensors

Self Cite

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“…Those studies are included as they also discussed the attributes of an ideal viewpoint. There were 3 studies out of 49 where the view was of action performed by a robot but the view was not external (all these studies selected the best viewpoints for grasping where the camera was mounted on the arm) [45,46,47]. There were 2 studies out of 49 where the subject of the external view was action but it was not performed by a robot but rather a computer player [48] or human [49].…”

Section: Studies That Discussed Attributes Of Ideal Viewpointmentioning

confidence: 99%

“…Field of View [76,77,78,70,31,71,32,33,34,37,48,35,36,20,42,49,72,73,46,61] Visibility/Occlusions [70,31,71,32,33,34,37,48,35,36,20,42,74,46,44] Depth of Field [76,77,78,70,31,71,32,33,34,37,35,36,20,59,60] Resolution/Zoom [76,77,…”

Section: Studies Camera Configuration Attributesmentioning

confidence: 99%

Best Viewpoints for External Robots or Sensors Assisting Other Robots

Dufek

Xiao

Murphy

2021

IEEE Trans. Human-Mach. Syst.

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This dissertation creates a model of the value of different external viewpoints of a robot performing tasks. The current state of the practice is to use a teleoperated assistant robot to provide a view of a task being performed by a primary robot. However, there is no existing model of the value of different external viewpoints and the choice of viewpoints is ad hoc not always leading to improved performance. This research develops the model using a psychomotor approach based REFERENCES .

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NDT‐6D for color registration in agri‐robotic applications

Gupta,

Lilienthal,

Andreasson

et al. 2023

Journal of Field Robotics

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Registration of point cloud data containing both depth and color information is critical for a variety of applications, including in-field robotic plant manipulation, crop growth modeling, and autonomous navigation. However, current state-ofthe-art registration methods often fail in challenging agricultural field conditions due to factors such as occlusions, plant density, and variable illumination.To address these issues, we propose the NDT-6D registration method, which is a color-based variation of the Normal Distribution Transform (NDT) registration approach for point clouds. Our method computes correspondences between pointclouds using both geometric and color information and minimizes the distance between these correspondences using only the three-dimensional (3D) geometric dimensions. We evaluate the method using the GRAPES3D data set collected with a commercial-grade RGB-D sensor mounted on a mobile platform in a vineyard. Results show that registration methods that only rely on depth information fail to provide quality registration for the tested data set. The proposed color-based variation outperforms state-of-the-art methods with a root mean square error (RMSE) of 1.1-1.6 cm for NDT-6D compared with 1.1-2.3 cm for other color-information-based methods and 1.2-13.7 cm for noncolor-information-based methods. The proposed method is shown to be robust against noises using the TUM RGBD data set by artificially adding noise present in an outdoor scenario. The relative pose error (RPE) increased ~14% for our method compared to an increase of ~75% for the best-performing registration method. The obtained average accuracy suggests that the NDT-6D registration methods can be used for in-field precision agriculture applications, for example, crop detection, size-based maturity estimation, and growth modeling.

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The use of dynamic sensing strategies to improve detection for a pepper harvesting robot

Cited by 15 publications

References 19 publications

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Viewpoint Analysis for Maturity Classification of Sweet Peppers

Best Viewpoints for External Robots or Sensors Assisting Other Robots

NDT‐6D for color registration in agri‐robotic applications

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