Deep CNN-based damage classification of milled rice grains using a high-magnification image dataset

Bhupendra, -; Moses, Kriz; Miglani, Ankur; Kankar, Pavan Kumar

doi:10.1016/j.compag.2022.106811

Cited by 56 publications

(13 citation statements)

References 32 publications

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“…Until now, not much research has been conducted to explore the methods for testing the quality of grain-based food products based on the MCV technology. Bhupendra et al (2022) acquired some images of various rice grains using a camera with 4.5× optical magnification lens and established a model for grading rice grain impairments [ 31 ]. In this study, a camera with 2× optical magnification lens was used for image acquisition.…”

Section: Discussionmentioning

confidence: 99%

Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model

Sun

Zhang

Tong

et al. 2022

Foods

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This study aims to develop a high-speed and nondestructive mildewed rice grain detection method. First, a set of microscopic images of rice grains contaminated by Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea are acquired to serve as samples, and the mildewed regions are marked. Then, three YOLO-v5 models for identifying regions of rice grain with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in microscopic images are established. Finally, the relationship between the proportion of mildewed regions and the total number of colonies is analyzed. The results show that the proposed YOLO-v5 models achieve accuracy levels of 89.26%, 91.15%, and 90.19% when detecting mildewed regions with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in the microscopic images of the verification set. The proportion of the mildewed region area of rice grain with contamination of Aspergillus niger/Penicillium citrinum/Aspergillus cinerea is logarithmically correlated with the logarithm of the total number of colonies (TVC). The corresponding determination coefficients are 0.7466, 0.7587, and 0.8148, respectively. This study provides a reference for future research on high-speed mildewed rice grain detection methods based on MCV technology.

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Section: Discussionmentioning

confidence: 99%

Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model

Sun

Zhang

Tong

et al. 2022

Foods

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“…There were B0-B7 EfficientNet versions. Mobile Inverted Bottleneck Convolution (MBConv) was the core structure of the network ( Zhang et al, 2020 ; Liu et al, 2021 ; Bhupendra et al, 2022 ). This module introduces the Squeeze-and-Excitation Network (SE)’s core concept to optimize the Network’s structure, as shown in Figure 7 .…”

Section: Methodsmentioning

confidence: 99%

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Jin

et al. 2022

Front. Plant Sci.

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The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model’s ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88–20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.

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“…Since this is a multi-class classi cation problem, macro-averaged accuracy, precision, recall, and F1-score are the metrics used to compare the performance of various models as displayed in Table I. The macroaverage metrics are the sum of all individual metrics computed across all the N classes [3,9].…”

Section: B Performance Evaluation For Multiclassi Cation Modelmentioning

confidence: 99%

“…Particularly, in agriculture elds, the classi cation of the rice grains variety is an important factor in determining the quality of grains, acceptance of the market value rate, stability of the storage, and nutritious content. Based on these factors, rice varieties are priced on the market [1,2,3].…”

Section: Introductionmentioning

confidence: 99%

Design of Accurate Multi-class Optimized Lightweight Convolution Neural Network for Rice Varieties Classification

Selvaraj

Venkatesan

2023

Preprint

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Worldwide, more than 40k rice varieties are existing, each with different nutritional content and quality. Identifying these has to be consistent, automated, and accurate. Considering the feature extraction process, convolution Neural Networks (CNN) are preferred over machine learning (ML) for this classification. Transfer learning approaches help to optimize the CNN model; therefore, it fits in an FPGA. Seven different CNN models were proposed to classify five rice varieties, each model differs based on the: kernel depth; the number of convolution layers (CL); the number of fully connected layers (FCL); and the number of neurons per FCL. These were analyzed considering 70% and 30% for training and testing respectively. A dataset of 15,000 images/variety with each image of resolution. This results in an Optimized Lightweight Convolutional Neural Network (OpLW-CNN) model, having a CL, followed by two FCLs. This model is further analyzed using a random set of images: 500, 5000, and 75000 to fit the model optimally. This model achieves 99%, 98.13%, and 98.14% of specificity, F1-score, and accuracy for a set of 5000 images. These metrics are approximately 1% to 2% lesser than the performance of the benchmark model, and 81.5% fewer computations. Also, this model requires less than a second to classify an image.

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Deep CNN-based damage classification of milled rice grains using a high-magnification image dataset

Cited by 56 publications

References 32 publications

Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model

Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Design of Accurate Multi-class Optimized Lightweight Convolution Neural Network for Rice Varieties Classification

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