Development of an automatic visual grading system for grafting seedlings

Tian, Si; Wang, Zifan; Yang, Jifeng; Huang, Zichen; Wang, Ruili; Wang, Liping; Dong, Jianghui

doi:10.1177/1687814016686265

Cited by 10 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the data augmentation, such as the flip, brightness and saturation change, we enlarged the training data and created samples in different status, and this classification method we constructed still had a high accuracy. It showed strong robustness in classification ability against the traditional image processing method [1][2][3][4][5][6]. Because the different brightness or saturation of image samples may affect the segmentation result or any other algorithm effect in traditional image processing, so as to reduce the accuracy.…”

Section: Plug Seedlings Classification Test Rusultmentioning

confidence: 99%

“…Qingchun et al used structured light and industrial camera methods to identify seedlings by detecting leaf area and stem height, and the recognition accuracy was over 90% [4]. Tian et al extracted the measurements of scion and rootstock stem diameters to grade scion and rootstock seedlings [5]. Wang et al compared the pixel value of seedling with threshold value to determine whether the cell was short of seedlings according to the binary images of the plug tray.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification Method of Plug Seedlings Based on Transfer Learning

Zhang

Liu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

The classification of plug seedlings is important work in the replanting process. This paper proposed a classification method for plug seedlings based on transfer learning. Firstly, by extracting and graying the interest region of the original image acquired, a regional grayscale cumulative distribution curve is obtained. Calculating the number of peak points of the curve to identify the plug tray specification is then done. Secondly, the transfer learning method based on convolutional neural network is used to construct the classification model of plug seedlings. According to the growth characteristics of the seedlings, 2286 seedlings samples were collected to train the model at the two-leaf and one-heart stages. Finally, the image of the interest region is divided into cell images according to the specification of the plug tray, and the cell images are put into the classification model, thereby classifying the qualified seedling, the unqualified seedling and the lack of seedling. After testing, the identification method of the tray specification has an average accuracy of 100% for the three specifications (50 cells, 72 cells, 105 cells) of the 20-day and 25-day pepper seedlings. Seedling classification models based on the transfer learning method of four different convolutional neural networks (Alexnet, Inception-v3, Resnet-18, VGG16) are constructed and tested. The classification accuracy of the VGG16-based classification model is the best, which is 95.50%, the Alexnet-based classification model has the shortest training time, which is 6 min and 8 s. This research has certain theoretical reference significance for intelligent replanting classification work.

show abstract

Section: Plug Seedlings Classification Test Rusultmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification Method of Plug Seedlings Based on Transfer Learning

Zhang

Liu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Its productivity was determined by the number of modules applied, and this robot is suitable for small-to medium-sized farms [21] . Research on the key technologies of grafting robots were also reported, such as outward-feature properties measurement of seedlings [31] , grafting quality or healing state detection [32,33] , and seedlings classification [34,35] .…”

Section: Introductionmentioning

confidence: 99%

Development of a high-productivity grafting robot for Solanaceae

Xie

Chu

et al. 2020

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

Grafting is a green, environmentally friendly, and sustainable way to prevent soil-borne diseases. Although artificial grafting is the main grafting approach used for grafting production, it has some problems which are low productivity, unstable operating quality and labor-intensive. Hence, some countries have been engaged in the development of grafting robots for the past two decades; however, the productivity of these grafting robots has no advantage when compared to artificial grafting. This study aims to develop a high-productivity grafting robot (HPR) for Solanaceae. To improve grafting productivity, this paper adopted plug trays to feed crown-removed rootstocks automatically and carried out multi-plant simultaneous grafting to improve grafting productivity and extensibility. Manipulators were employed to take out rootstocks, increase the distance between them, and transfer them to transfer cups for the simultaneous multi-plant grafting. At the same time, negative pressure mechanisms were designed for speeding up the auxiliary feeding of root-removed scions. Although the HPR was designed in a two-operator mode, a one-operator mode can also be implemented by adjusting the control program. Tests were conducted by varying the artificial feeding speed to analyze the performance of the grafting robot. The results showed that the productivity of the robot in the two-operator mode was 2250 plants/h, and 1542 plants/h in one-operator mode; comparing the artificial feeding productivity with auto grafting productivity, it was found that the capacity of the grafting robot was higher than the feeding speed of the one-operator mode but lower than that of the two-operator mode.

show abstract

“…The use of machine vision has been employed too in grading grafting seedlings in a study carried out by Tian et al (2017). A success rate of 98% was achieved exhibiting that their developed vision system was helpful in improving the grading accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Single-line automated sorter using mechatronics and machine vision system for Philippine table eggs

Quilloy¹,

Suministrado²,

Bato³

2018

Afr. J. Agric. Res.

View full text Add to dashboard Cite

An automated single-line table egg sorting machine that integrates machine vision and mechatronics principles was developed in this study. The machine was fabricated using low cost and locally available materials. The developed machine was composed of the feeding unit, computing unit and the sorting unit. The conveyor was powered by a 12V DC geared motor, and the sorting arm was actuated by a DC servo motor which positions the arm. A machine vision software, EGGSoTiC, was used in the sorting of the table eggs -moving through a conveyor at 13 cm-s -1. Test for similarity of readings revealed that the developed machine is capable of yielding consistent results with low values of coefficient of variation ranging from 0.38 to 0.85 mm 2 and 0.42 to 0.94 g for the projected area and estimated weight, respectively. Results of dynamic test for 100 table egg samples indicated that the machine could sort table eggs with an accuracy of 91% at 2.52 seconds per sample, yielding a projected capacity of 1,426 eggs per hour. Results also indicated that there were no large errors in the estimation of weights yielding a root mean square error of 1.90 g which is not significantly higher than the bias of 0.93 g.

show abstract

Development of an automatic visual grading system for grafting seedlings

Cited by 10 publications

References 23 publications

Classification Method of Plug Seedlings Based on Transfer Learning

Classification Method of Plug Seedlings Based on Transfer Learning

Development of a high-productivity grafting robot for Solanaceae

Single-line automated sorter using mechatronics and machine vision system for Philippine table eggs

Contact Info

Product

Resources

About