Automatic Adjustable Spraying Device for Site-Specific Agricultural Application

Berenstein, Ron; Edan, Yael

doi:10.1109/tase.2017.2656143

Cited by 72 publications

(36 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The core of the ASD contains three elements: a spraying nozzle, a step motor interconnected to the nozzle, and a camera. Based on the size of the targets detected on the image captured from the camera, the step motor changes the nozzle diameter and performs a single spray using an electric valve . The ASD was mounted to a pan‐tilt unit (PTU) that directs the nozzle toward the target.…”

Section: The Robotmentioning

confidence: 99%

“…). The performance measure was TP rate. Spraying material estimation : an estimation of the quantity of spraying material used (representing pesticides) was conducted based on the ASD development results . In the estimation, the quantity of liquid that was used in each of the spraying experiments above is compared to a simulated experiment of the robot continuously driving along the targets’ base with three open nozzles (similar to the traditional spraying method; Fig.…”

Section: Integrative Site‐specific Sprayer Experimentsmentioning

confidence: 99%

“…6) was developed using a single nozzle (AYHSS 16) to enable accurate spraying of the agricultural target. 33 The developed device was designed to implement the one target-one shot (OTOS) spraying method, which was designed to cover the entire target by adjusting the spray diameter to the minimum possible closing circle diameter with a single spray. 37 The core of the ASD contains three elements: a spraying nozzle, a step motor interconnected to the nozzle, and a camera.…”

Section: The Robotmentioning

confidence: 99%

“…It includes several levels of collaboration based on Sheridan's 10 levels of collaboration . The human‐robot collaborative sprayer presented in this paper integrates all modules necessary for site‐specific spraying: target detection algorithms, target marking techniques, a remote interface for human‐robot collaboration, collaboration levels between the human and the robot, and an adjustable diameter spraying device . The proposed human‐robot collaborative framework focuses on the main task of detecting and spraying targets, with the aim to increase TP rate and reduce false positives (FPs) .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Human‐robot collaborative site‐specific sprayer

Berenstein

Edan

2017

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

Spraying pesticides is a key element of agriculture worldwide, since 30% to 35% of crop losses can be prevented when harmful insects and diseases are eliminated by applying pesticides. Site‐specific spraying can help reduce pesticide application; however, target detection is limited due to the complex agricultural environment. This paper presents a human‐robot collaborative sprayer designed for site‐specific targeted spraying. The robotic sprayer platform, the framework, and tools for the robotic sprayer to collaborate with a remote human operator for the target detection and spraying tasks are detailed. An experiment to evaluate the elements of the collaborative human‐robot framework working in sync was designed, implemented, and evaluated. The collaborative spraying system shows a 50% reduction of sprayed material. The experiment also proves the feasibility of human‐robot collaboration for the complex task of spraying specific targets considering both the True Positive (TP) and False Positive (FP) rates.

show abstract

Section: The Robotmentioning

confidence: 99%

Section: Integrative Site‐specific Sprayer Experimentsmentioning

confidence: 99%

Section: The Robotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Human‐robot collaborative site‐specific sprayer

Berenstein

Edan

2017

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Target application is one of the research studies focusing on fine pesticides, and many scholars have conducted meaningful studies on it [5][6][7][8][9][10][11][12][13][14]. Aiming at the poor mechanization operation conditions within orchards in China and the ineffective spraying of fruit tree gaps during continuous spraying with traditional orchard sprayers, an automatic target-spraying control system is designed by Xu et al, which has a good practical value for the accurate targeting control as well as diseases and insect pests prevention in sparse orchards [5].…”

Section: Introductionmentioning

confidence: 99%

A Screen Location Method for Treating American Hyphantria cunea Larvae Using Convolutional Neural Network

Gao

Zhao

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Chemical control is the major approach to handle the American Hyphantria cunea issue; however, it often causes chemical pollution and resource waste. How to precisely apply pesticide to reduce pollution and waste has been a difficult problem. e premise of accurate spraying of chemicals is to accurately determine the location of the spray target. In this paper, an algorithm based on a convolutional neural network (CNN) is proposed to locate the screen of American Hyphantria cunea. Specifically, comparing the effect of multicolor space-grouping convolution with that of the same color space-grouping convolution, the better effect of different color space-grouping convolution is first proved. en, RGB and YIQ are employed to identify American Hyphantria cunea screen. Moreover, a noncoincident sliding window method is proposed to divide the image into multiple candidate boxes to reduce the number of convolutions. at is, the probability of American Hyphantria cunea is determined by grouping convolution in each candidate box, and two thresholds (E and Q) are set. When the probability is higher than E, the candidate box is regarded as excellent; when the probability is lower than Q, the candidate box is regarded as unqualified; when the probability is in between, the candidate box is regarded as qualified. e unqualified candidate box is eliminated, and the qualified candidate box cannot exit the above steps until the number of extractions of the candidate box reaches the set value or there is no qualified candidate box. Finally, all the excellent candidate boxes are fused to obtain the final recognition result. Experiments show that the recognition rate of this method is higher than 96%, and the processing time of a single picture is less than 150 ms.

show abstract

Development of a sweet pepper harvesting robot

Arad

Balendonck

Barth

et al. 2020

Journal of Field Robotics

Self Cite

268

132

View full text Add to dashboard Cite

This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end effector, RGB-D camera, high-end computer with graphics processing unit, programmable logic controllers, other electronic equipment, and a small container to store harvested fruit. All is mounted on a cart that autonomously drives on pipe rails and concrete floor in the end-user environment. The overall operation of the harvesting robot is described along with details of the algorithms for fruit detection and localization, grasp pose estimation, and motion control. The main contributions of this paper are the integrated system design and its validation and extensive field testing in a commercial greenhouse for different varieties and growing conditions. A total of 262 fruits were involved in a 4-week long testing period. The average cycle time to harvest a fruit was 24 s. Logistics took approximately 50% of this time (7.8 s for discharge of fruit and 4.7 s for platform movements). Laboratory experiments have proven that the cycle time can be reduced to 15 s by running the robot manipulator at a higher speed. The harvest success rates were 61% for the best fit crop conditions and 18% in current crop conditions. This reveals the importance of finding the best fit crop conditions and crop varieties for successful robotic harvesting. The SWEEPER robot is the first sweet pepper harvesting robot to demonstrate this kind of performance in a commercial greenhouse.

show abstract

Automatic Adjustable Spraying Device for Site-Specific Agricultural Application

Cited by 72 publications

References 33 publications

Human‐robot collaborative site‐specific sprayer

Human‐robot collaborative site‐specific sprayer

A Screen Location Method for Treating American Hyphantria cunea Larvae Using Convolutional Neural Network

Development of a sweet pepper harvesting robot

Contact Info

Product

Resources

About