A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

Fan, Pan; Lang, Guodong; Yan, Bin; Lei, Xiaoyan; Guo, Pengju; Liu, Zhijie; Yang, Fuzeng

doi:10.3390/rs13061211

Cited by 31 publications

(25 citation statements)

References 51 publications

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“…Many techniques [ 50 , 51 , 52 ] in the recent literature do not analyze the chromaticity information to threshold color images, since they incorporate RGB-channel-based thresholding. While some [ 10 , 53 , 54 ] do apply HSV or L*A*B* color space analysis to thresholding problems, they do not automatically determine the threshold limits based on the unique characteristics of the image.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Automatic Color Thresholding Technique

Shamna

Ellam²,

Grübl

et al. 2023

Sensors

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Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one’s focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing background suppression in PCA boards facilitates the inspection of digital images with small objects of interest, such as text or microcontrollers on a PCA board. The segmentation of skin cancer lesions will help doctors to automate skin cancer detection. The results showed a clear and robust background–foreground separation across various sample images under different camera or lighting conditions, which the naked implementation of existing state-of-the-art thresholding methods could not achieve.

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Section: Related Workmentioning

confidence: 99%

A Two-Stage Automatic Color Thresholding Technique

Shamna

Ellam²,

Grübl

et al. 2023

Sensors

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“…9 Based on the type of sensor used, the RGB cameras capture images with different resolutions. Some of them are PowerShot G16, 19 Industrial camera 20 and AFT-0814MP. 21 Kinect v2 camera operates with image 1920 3 1080 and depth resolution of 512 3 424.…”

Section: Vision Systems For Fruit Harvesting Robotmentioning

confidence: 99%

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Kumar

Mohan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Progressive application of multidisciplinary research and development pushes the evolution of automation in many subsectors of agriculture to increase productivity, economic growth and environmental preservation with the help of robotics and artificial intelligence. Fruit harvesting robots have been developed mainly to provide support in the field for limited labour resources, to enable selective harvesting, to improve the efficiency and to preserve the quality of fruits. Even a small delay in harvesting can cause a maximum impact to the quality of the fruit. Selective fruit harvesting is an integration of different subcomponents. This paper provides a brief analysis of the techniques in selective fruit harvesting for the past 6 years starting from 2017 to 2022, associated principles, limitations and directions for future challenges. The first subcomponent is the vision system, it captures the information about the fruit in a tree canopy to perform efficient 2D and 3D localization. Hence getting accurate information from the vision system is more essential even in a complex agricultural environment. The detection of fruit from the vision system is normally done with two major methods such as traditional image processing and deep learning approaches. The result shows that the traditional methods provide high efficiency but the colour similarity, complex backgrounds and lightning conditions often makes failure in detection. Shortage of standard dataset and high-powered processing devices hinders the development of deep learning algorithms. Also, the usage of large data sets reduces the training speed even in pre-trained networks. For fruit grasping and detachment, the detection of plucking point is more essential to preserve the quality and for further storage. The elaborate description about the framework, limitations in current sensing, fruit and picking point detection algorithms provides guidelines to the researchers in building a fully automated robotic system to increase the processing speed and production rate.

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“…Many novel plant vision systems in the context of machine vision technology have been developed to estimate the morphological features of plants, with the aim of determining their growth and health status [ 9 , 10 , 11 ]. Partial image features were combined with color information to segment apple images using a pixel block segmentation approach in the gray-centered RGB color space [ 12 ]. To recognize ripe and unripe pomelo fruit on the trees, Liu et al [ 13 ] used a machine vision algorithm based on an ellipse boundary model to convert images from RGB space to YC b C r space.…”

Section: Related Workmentioning

confidence: 99%

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Liu

Wang

et al. 2022

Sensors

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Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.

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A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

Cited by 31 publications

References 51 publications

A Two-Stage Automatic Color Thresholding Technique

A Two-Stage Automatic Color Thresholding Technique

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

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