Tea Bud Leaf Identification by Using Machine Learning and Image Processing Techniques

Karunasena, Gayani; Priyankara, H.D.N.S.

doi:10.14299/ijser.2020.08.02

Cited by 15 publications

(8 citation statements)

References 4 publications

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“…To ensure that the accuracy did not drop too severely (<20%), the number of pruning layers was selected as 16 in the study, and the index number of the pruned shortcut layer at this time was [68, 74, 65, 71, 61, 55, 52, 49, 40, 43, 58, 46, 18, 15, 30, 27], where the shortcut layer with the first index number was pruned first because of its low contribution to the model. The remaining shortcut layers ( [21,24,33,36,8,11,4]) and the corresponding CBL layers were retained.…”

Section: Evaluation Of Model Pruning 321 Results Of Sparse Trainingmentioning

confidence: 99%

“…Zhang et al [7] used an image process algorithm combined with Bayesian discrimination to achieve the identification of fresh tea leaves and harvest status, thereby providing a basis for the automated management of tea gardens. Karunasena et al [8] presented a new method for tea shoot detection using a cascade classifier, which carried out the detection of tea buds by combining histogram of oriented gradient features and support vector machine classification. Zhang et al [9] proposed a method based on an improved watershed algorithm for the identification and segmentation of tea sprouts and used piecewise linear transformation to enhance the differentiation degree of old tea sprouts and the segmentation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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High-efficiency tea shoot detection method via a compressed deep learning model

Jia

et al. 2022

International Journal of Agricultural and Biological Engineering

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Achieving high-efficiency and accurate detection of tea shoots in fields is essential for tea robotic plucking. A real-time tea shoot detection method using the channel and layer pruned YOLOv3-SPP deep learning algorithm was proposed in this study. First, tea shoot images were collected and data augmentation was performed to increase sample diversity, and then a spatial pyramid pooling module was added to the YOLOv3 model to detect tea shoots. To simplify the tea shoot detection model and improve the detection speed, the channel pruning algorithm and the layer pruning algorithm were used to compress the model. Finally, the model was fine-tuned to restore its accuracy, and achieve the fast and accurate detection of tea shoots. The test results demonstrated that the number of parameters, model size, and inference time of the tea shoot detection model after compression reduced by 96.82%, 96.81%, and 59.62%, respectively, whereas the mean average precision of the model was only 0.40% lower than that of the original model. In the field test, the compressed model was deployed on a Jetson Xavier NX to conduct the detection of tea shoots. The experimental results demonstrated that the detection speed of the compressed model was 15.9 fps, which was 3.18 times that of the original model. All the results indicate that the proposed method could be deployed on tea harvesting robots with low computing power to achieve high efficiency and accurate detection.

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Section: Evaluation Of Model Pruning 321 Results Of Sparse Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-efficiency tea shoot detection method via a compressed deep learning model

Jia

et al. 2022

International Journal of Agricultural and Biological Engineering

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“…Grayscale includes only information about luminance (brightness) and no information about colours [13][14]. That's why the highest luminance is white, and the minimum luminance is black; the shade of gray is everywhere in between.…”

Section: Grayscale Conversionmentioning

confidence: 99%

Binocular Vision-Based Intelligent 3-D Perception for Robotics Application

Karunachandra¹,

Herath²

2021

Preprint

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Vision-based robotics has been the subject of several research contributions in the area of vision and control. Vision technology is becoming a pioneer in the most common applications such as localization, automated map creation, autonomous navigation, mapping analysis, or risk pattern prediction. The Stereo applications or programs use pairs of 2-D images as inputs and generate reconstructed 3-D imagery by locating the matching points. This paper introduces a method to the development of an algorithm of intelligent 3-D view reconstruction using binocular vision for robotic applications. The proposed system consists of two identical colour cameras and cameras were mounted as one stereo camera. 3-D reconstruction and visualization were performed according to the pair of 2-D images. Calibration, multi-view image acquisition, the stereo rectification process, and the disparity process were discussed in Sect. 2. Real-time captured images and stereo images from Middlebury Stereo Datasets were used to test the system and verify results in Sect. 3.

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“…Grayscale is the set or range of monochrome (gray) shades that range from pure white on the lightest end to pure black on the other end. Grayscale includes only information about luminance (brightness) and no information about colours [13][14]. That's why the highest luminance is white, and the minimum luminance is black; the shade of gray is everywhere in between.…”

Section: Grayscale Conversionmentioning

confidence: 99%

Binocular Vision-Based Intelligent 3-D Perception for Robotics Application

Karunachandra¹,

Herath²

2020

IJSRP

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Vision-based robotics has been the subject of several research contributions in the area of vision and control. Vision technology is becoming a pioneer in the most common applications such as localization, automated map creation, autonomous navigation, mapping analysis, or risk pattern prediction. The Stereo applications or programs use pairs of 2-D images as inputs and generate reconstructed 3-D imagery by locating the matching points. This paper introduces a method to the development of an algorithm of intelligent 3-D view reconstruction using the binocular vision for the robotic applications. The proposed system consists of two identical colour cameras and cameras were mounted as one stereo camera. 3-D reconstruction and visualization were performed according to the pair of 2-D images. Calibration, multi-view image acquisition, the stereo rectification process, and the disparity process were discussed in Section II. Real-time captured images and stereo image from Middlebury Stereo Datasets were used to test the system and verify results in Section III.

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Tea Bud Leaf Identification by Using Machine Learning and Image Processing Techniques

Cited by 15 publications

References 4 publications

High-efficiency tea shoot detection method via a compressed deep learning model

High-efficiency tea shoot detection method via a compressed deep learning model

Binocular Vision-Based Intelligent 3-D Perception for Robotics Application

Binocular Vision-Based Intelligent 3-D Perception for Robotics Application

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