Global thresholding based on improved histogram for chalk area segmentation in rice quality evaluation

Itharat, Peerapat; Wattuya, Pakaket; Sreewongchai, Tanee; Watcharopas, Chakrit

doi:10.1117/12.2572963

Cited by 2 publications

(2 citation statements)

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“…[3] applied indices based histogram to leaf segmentation, the segmentation accuracy can reach 92.06%. Itharat et al [4] applied the global threshold based histogram approach to chalk area segmentation in rice quality evaluation. The method has a small amount of computation and is insensitive to the size and position of the target, so it can effectively deal with the problem of low contrast.…”

Section: Introductionmentioning

confidence: 99%

AXanthoceras sorbifoliumcrack segmentation method based on an improved level set

Zhang

Chen

et al. 2023

IET Image Processing

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The dehiscence of the Xanthoceras sorbifolium (X. sorbifolium) may lead to seeds jump out and economic loss. The shape and the degree of the crack will provide the relevant elements for the study of the X. sorbifolium dehiscence and duly picked. Therefore, an improved level set method is proposed for X. sorbifolium crack segmentation. The problems of intensity inhomogeneity and so on, which pose challenges for accurate crack segmentation. The local Gaussian distribution fitting method has a good segmentation effect, but it is sensitive to the initial contour and does not use gradient information, which affects the accurate location of the edge. Aiming at the above problems and the scene of crack segmentation, this paper firstly adopts histogram threshold method to obtain the initial contour automatically. Secondly, the energy function is constructed by combining local and edge information. Finally, the double‐well potential function is used to reduce the oscillation and distortion of the method. In this paper, the experiment results show that the average boundary precision is 88.25% and average segmentation time of each image is 12.7s of the proposed method. After comprehensive analysis of IoU and boundary recall, the method in this paper achieves better results.

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Section: Introductionmentioning

confidence: 99%

AXanthoceras sorbifoliumcrack segmentation method based on an improved level set

Zhang

Chen

et al. 2023

IET Image Processing

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“…Te OMT's and FC accuracy rates are 89.83% and 95.53%. Itharatet al [28] used Khao Dawk MAli 105 dataset and applied global thresholding to segment the chalk area on images of rice. RiceNet-based segmentation has improved the quality of adhesive rice grain.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification

Fatima

Khan

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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Image processing is an important domain for identifying various crop varieties. Due to the large amount of rice and its varieties, manually detecting its qualities is a very tedious and time-consuming task. In this work, we propose a two-stage deep learning framework for detecting and classifying multiclass rice grain varieties. A series of steps is included in the proposed framework. The first step is to perform preprocessing on the selected dataset. The second step involves selecting and fine-tuning pretrained deep models from Darknet19 and SqueezeNet. Transfer learning is used to train the fine-tuned models on the selected dataset. The 50% sample images are employed for the training and rest 50% are used for the testing. Features are extracted and fused using a maximum correlation-based approach. This approach improved the classification performance; however, redundant information has also been included. An improved butterfly optimization algorithm (BOA) is proposed, in the next step, for the selection of the best features that are finally classified using several machine learning classifiers. The experimental process was conducted on selected rice datasets that include five types of rice varieties and achieves a maximum accuracy of 100% that was improved than the recent method. The average accuracy of the proposed method is obtained at 99.2%, through confidence interval-based analysis that shows the significance of this work.

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Global thresholding based on improved histogram for chalk area segmentation in rice quality evaluation

Cited by 2 publications

References 0 publications

AXanthoceras sorbifoliumcrack segmentation method based on an improved level set

AXanthoceras sorbifoliumcrack segmentation method based on an improved level set

Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification

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