Skewness correction and quality evaluation of plug seedling images based on Canny operator and Hough transform

Tong, Junhua; Shi, Hufeng; Wu, Chuanyu; Jiang, Huanyu; Yang, Taiwei

doi:10.1016/j.compag.2018.10.035

Cited by 42 publications

(22 citation statements)

References 8 publications

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“…true area se > threshold sed and threshold sud < area su < threshold suu f alse else (7) where area se is the area of seedling region; area su is the area of the substrate region; threshold sed is the seedling area lower threshold; threshold sud and threshold suu are the substrate region lower threshold and upper threshold. The defect of substrate damage needs to be identified by area shape analysis.…”

Section: Seedling Defects Defects' Characterizationmentioning

confidence: 99%

“…In recent years, the research on defect seedling identification technology included seedling information acquisition technology [1][2][3], seedling and background segmentation [4,5], and seedling defect identification technology [6][7][8]. Advanced technologies such as hyperspectral image sensors, near-infrared image sensors, machine learning techniques, and artificial neural network technologies have played an important role.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Cabbage Seedling Defects in a Fast Automatic Transplanter Based on the maxIOU Algorithm

et al. 2020

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The automatic identification of seedling defects is an important technology of an intelligent automatic transplanting machine, which effectively improves the quality of the transplanting machine’s operation. The accurate segmentation of seedling substrate and seedling region is the key to the success of the seedling defect recognition algorithm. This paper proposes the maxIOU algorithm to calculate the image segmentation threshold: The image G channel and excess green color space were selected as the color space for the segmentation of the substrate region and seedling region by analyzing the color histogram. Several images were randomly selected from the dataset to generate a training set and were labeled manually as the ground truth. The training set images were segmented using a threshold of zero to 255, and the intersection over union (IOU) were calculated using the algorithm segmented result and the ground truth. The threshold corresponding to the average IOU maximum was used as the segmentation threshold. After image segmentation, three features (area of the substrate, area of the seedling, and filling ratio of the lower part of the substrate) were obtained by the algorithm, and the image was identified for whether there was an empty conveyor belt, seedling deficiency, multiple seedlings, skew, and damaged substrate. The algorithm was tested on the automatic transplanter test platform. The experiment results were as follows: Firstly, the image segmentation threshold was calculated by the maxIOU method. The color component interval corresponding to the segmented substrate region was [0, 24] in the G channel, and the color component interval corresponding to the segmented seedling region was [21, 255] in the excess green channel. The average IOU of the substrate area was 0.854, and the average IOU of the seedling area was 0.820 in the verification experiment. Secondly, a dataset including 431 normal seedling images and 69 defective seedling images (empty conveyor belt, seedling deficiency, multiple seedlings, skew, and damaged substrate) was identified for defects. The accuracy, precision, and recall were 97.6%, 97.4%, and 99.8%. The processing time was 71.4 ms. The conclusion of the experiment was as follows: the maxIOU algorithm had high accuracy in the segmentation of the substrate and seedling region. The defect identification algorithm had high accuracy for defect identification of cabbage seedlings, and the algorithm had good real-time performance, which can be applied to high speed field transplanters.

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Section: Seedling Defects Defects' Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Cabbage Seedling Defects in a Fast Automatic Transplanter Based on the maxIOU Algorithm

et al. 2020

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“…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%

“…Jiang et al used tomato seedlings as test samples and applied a morphological-based watershed algorithm to complete leaf edge segmentation, extracting each leaf area, and leaf circumference of the seedlings in a plug tray was used to identify the seedlings; recognition accuracy reached 98% [1]. Tong et al aimed at the leaf overlap of the plug seedlings, proposed a decision-making method combining the central information of the leaflet area and improved the watershed method to segment the seedlings, and then calculated the seedling leaf area to realize the seedling identification judgment; the recognition accuracy rate was 95% [2,3]. 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].…”

Section: Introductionmentioning

confidence: 99%

Classification Method of Plug Seedlings Based on Transfer Learning

Zhang

Liu

et al. 2019

Applied Sciences

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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.

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“…The characteristics of Hough transform algorithm lead to its high time and space complexity, and in the process of detection, it can only determine the direction of the line and lose the length information of the line segment, so it is not suitable for lane line detection directly. 17,18 The least square method is only suitable for the data with less noise, but in many cases, some points which deviate from the curve obviously need to be discarded, so it is not suitable for lane line fitting. RANSAC curve fitting method is an improvement of the least square method.…”

Section: Introductionmentioning

confidence: 99%

Lane line recognition based on improved 2D-gamma function and variable threshold Canny algorithm under complex environment

Zhang

Liang

Jiang

et al. 2020

Measurement and Control

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The visual guidance of AGV (automated guided vehicle) has gradually become one of the most important perception methods. Aiming at the problem that it is difficult to extract lane line accurately when AGV is running in complex working environment (such as uneven illumination, overexposure, lane line is not obvious, etc.), a scheme of lane line recognition under complex environment is proposed. Firstly, the variable scale image correction is carried out for the uneven illumination area in ROI (region of interest), and the threshold of Canny algorithm is adjusted adaptively according to the luminance of ROI region by Fuzzy-Canny algorithm; Secondly, the edge points matching the lane width feature are extracted by the way of aerial view. Finally, the curve fitting method based on RANSAC (Random Sample Consensus) is used to fit a curve with the lowest error rate and then get the lane center curve. The experimental results show that the processing algorithm used in this paper is feasible and effective, has strong robustness and fast computing performance, and can meet the requirements of intelligent AGV in various complex environments.

show abstract

Skewness correction and quality evaluation of plug seedling images based on Canny operator and Hough transform

Cited by 42 publications

References 8 publications

Identification of Cabbage Seedling Defects in a Fast Automatic Transplanter Based on the maxIOU Algorithm

Identification of Cabbage Seedling Defects in a Fast Automatic Transplanter Based on the maxIOU Algorithm

Classification Method of Plug Seedlings Based on Transfer Learning

Lane line recognition based on improved 2D-gamma function and variable threshold Canny algorithm under complex environment

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