Segmentation using fuzzy cluster‐based thresholding method for apple fruit sorting

Henila, Manickam; Chithra, Palaniappan

doi:10.1049/iet-ipr.2020.0705

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…All images adopt this threshold to realize the image segmentation of fruits and vegetables. The threshold setting is based on color features (Blok et al, 2016; Cheng et al, 2015; Edan et al, 2000; Henila & Chithra, 2020; Linker, 2017; Stajnko et al, 2004; H. Q. Wang, Ji, et al, 2013; Zhou et al, 2012), texture features (Cheng et al, 2015) and spectral features (Okamoto & Lee, 2009; Tejada et al, 2017; T. Yuan et al, 2009). The adaptive threshold segmentation automatically selects the segmentation threshold by the distribution difference between fruits and the background according to the selected features.…”

Section: Recognition Of Fruits and Vegetables With Similar‐color Back...mentioning

confidence: 99%

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confidence: 99%

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Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Xu³

et al. 2022

Journal of Field Robotics

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Fruit images collected by picking robots in natural environments have problems such as uneven lighting, complex backgrounds, occlusion of branches and leaves, and overlapping fruits and vegetables, which greatly increases the difficulty for picking robots to accurately identify target fruits and vegetables. Meanwhile, most fruits and vegetables have similar‐color backgrounds. Compared with the ones with different colors in the background, fruits and vegetables in similar‐color backgrounds are similar to the colors of their leaves and surrounding weeds, which increases the difficulty of identification. Therefore, realizing the accurate identification of fruits and vegetables in similar‐color backgrounds is vital to realize the dynamic monitoring of the growth of fruits and vegetables and intelligent picking, which has important value and application prospects for optimizing plantation management and automated harvesting operations. The work summarized the specific characteristics of fruits and vegetables with the close‐color background and the different processing procedures in the identification process. The following methods have been summarized, for example, image acquisition, image preprocessing, feature extraction, image segmentation, and fruit and vegetable detection in the process, and some methods were compared. Finally, the current research was discussed, and a solution was proposed for the different processing steps of fruit and vegetable recognition in similar‐color backgrounds. Deep learning methods were combined with some traditional methods to identify fruits and vegetables with close‐color background, which provides references for further research in various aspects.

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Section: Recognition Of Fruits and Vegetables With Similar‐color Back...mentioning

confidence: 99%

mentioning

confidence: 99%

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Xu³

et al. 2022

Journal of Field Robotics

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“…Besides, the darker area is not clearly segmented. Henila et al (2020) constructed a fuzzy clustering-based threshold segmentation method for segmenting regions of interest in apple images with the largest cluster of pixels to calculate the threshold value, which has substantially improved the segmentation accuracy and efficiency compared with the grayscale thresholding method. Jia et al (2020a) proposed an apple image recognition method based on PCNN and GA–Elman fusion.…”

Section: Introductionmentioning

confidence: 99%

YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit

et al. 2022

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Accurate detection and segmentation of the object fruit is the key part of orchard production measurement and automated picking. Affected by light, weather, and operating angle, it brings new challenges to the efficient and accurate detection and segmentation of the green object fruit under complex orchard backgrounds. For the green fruit segmentation, an efficient YOLOF-snake segmentation model is proposed. First, the ResNet101 structure is adopted as the backbone network to achieve feature extraction of the green object fruit. Then, the C5 feature maps are expanded with receptive fields and the decoder is used for classification and regression. Besides, the center point in the regression box is employed to get a diamond-shaped structure and fed into an additional Deep-snake network, which is adjusted to the contours of the target fruit to achieve fast and accurate segmentation of green fruit. The experimental results show that YOLOF-snake is sensitive to the green fruit, and the segmentation accuracy and efficiency are significantly improved. The proposed model can effectively extend the application of agricultural equipment and provide theoretical references for other fruits and vegetable segmentation.

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“…The method can obtain the defect information better and has a high defect detection rate (Che et al, 2020;Cheng et al, 2018;Luo et al, 2019). Some studies applied threshold segmentation methods for image segmentation of larger defects, which are good for obtaining larger defect information (Liu et al, 2017;Diao et al, 2018;Su et al, 2019;Henila et al, 2020). Edge detection algorithms can be used to obtain edge information and pinpoint edge locations, which also have better edge processing capabilities (Yang et al, 2021;Sangeetha et al, 2016;Wang et al, 2017;Han et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Effect on Mechanical Damage on Castor Germination and Damage Detection Method

Hou

Yao

Zhu

et al. 2022

INMATEH

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To study the types of mechanical damage for castor seeds and their effects on germination, the image processing method was applied to detect the damage affecting germination. Two typical varieties of castor were selected for test. The type of mechanical damage of castor seeds was taken as the factor, the germination rate and germination vigor index were selected as indicators for one-way analysis of variance. The effects of mechanical damage on the germination of castor seeds were analyzed. Different algorithms were applied to extract the features of cracks and seed shell missing, and the corresponding defect parameters were calculated. The results showed that the effects of mechanical damage on the germination rate, germination potential, germination index, and vigor index of castor seeds were significant. The endosperm damage seriously affected the activity of castor seeds and seriously hindered seed germination. According to the analysis of the shell, some castor seeds cracked or there was incomplete shell damage at the same time, the internal endosperm being also damaged. The actual crack length was compared with the length measured by the ultra-depth of field microscope, which found that the margin of error was about 25% and the better error was 10%. Through the morphological processing, it could completely extract the characteristics of castor seed image without seed shells. The error between the extracted feature area and the measured object area function of the super depth of field microscope is about 10%.

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Segmentation using fuzzy cluster‐based thresholding method for apple fruit sorting

Cited by 11 publications

References 24 publications

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit

Effect on Mechanical Damage on Castor Germination and Damage Detection Method

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