An Improved EfficientNet for Rice Germ Integrity Classification and Recognition

Li, Bing; Liu, Bin; Li, Shuofeng; Liu, Haiming

doi:10.3390/agriculture12060863

Cited by 14 publications

(7 citation statements)

References 39 publications

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“…The variation characteristics of the gray levels of R, G, and B components in the defect area of Huanghuali were analyzed, and finally the maximum combined set of defect pixels and all defect areas were found. Li et al (2022) put forward a method for identifying germ and endosperm with saturation S as a characteristic parameter by analyzing the color characteristics of germ rice and color images, in order to realize the automatic computer vision of rice germ retention rate detection. Experiments are carried out with the established identification indicators and methods, and the results show that the coincidence rate between the identification results of the computer vision system and the manual detection is over 88%.…”

Section: Resnextmentioning

confidence: 99%

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AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

Nawaz

Bhatti

et al. 2022

Front. Plant Sci.

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Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed.

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Section: Resnextmentioning

confidence: 99%

“… Li et al. (2022) put forward a method for identifying germ and endosperm with saturation S as a characteristic parameter by analyzing the color characteristics of germ rice and color images, in order to realize the automatic computer vision of rice germ retention rate detection.…”

Section: Object Detection and Recognition Applications In Agriculture...mentioning

confidence: 99%

AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

Nawaz

Bhatti

et al. 2022

Front. Plant Sci.

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“…EfficientNet (Tan & Le, 2019) is a novel convolutional neural network architecture for analyzing images and is preferred as a prediction model in various disease diagnostic research for humans (Venugopal, Joseph, Das, & Nath, 2022) (Nayak, Padhy, Mallick, Zymbler, & Kumar, 2022) (Wang, Liu, Xie, Yang, & Zhou, 2021) (Ravi, Acharya, & Alazab, 2022) (Zhu et al, 2022) (Marques, Ferreras, & de la Torre-Diez, 2022) and plants (Hanh, Van Manh, & Nguyen, 2022) (Farman et al, 2022) (Atila, Uçar, Akyol, & Uçar, 2021) (Li, Liu, Li, & Liu, 2022). EfficientNet's popularity among deep learning researchers is due to its high classification and prediction performances.…”

Section: Literature Reviewmentioning

confidence: 99%

“…EfficientNet's popularity among deep learning researchers is due to its high classification and prediction performances. EfficientNet's performance is compared with other deep learning models in various studies, and, EfficientNet is found to perform better than others (Nayak et al, 2022) (Zhu et al, 2022) (Marques et al, 2022) (Atila et al, 2021) (Li et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosing Diseases From Fingernail Images

CAN

Işık

2022

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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This paper investigates how people's finger and nail appearance helps diagnose various diseases, such as Darier's disease, Muehrcke's lines, alopecia areata, beau's lines, bluish nails, and clubbing, by image processing and deep learning techniques. We used a public dataset consisting of 17 different classes with 655 samples. We divided the dataset into three folds based on a widely used rule, the 0.7:0.2:0.1, for training, validation, and testing purposes. We tested the EfficientNet-B2 model for performance evaluation purposes by using Noisy-Student weights by setting the batch size and epochs as 32 and 1000. The model achieves a 72% accuracy score and 91% AUC score for test samples to detect fingernail diseases. The empirical findings in this study provide a new understanding that the EfficientNet-B2 model can categorize nail disease types through numerous classes.

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“…Li et al [ 24 ] refined the Inception-v3 model to detect the integrity of the germ with the addition of mutual channel loss and mlpconv. Li et al [ 25 ] identified rice germ integrity based on the EfficientNet-B3 model with the introduction of the double attention network (DAN).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model

Chen

Wang

2022

Foods

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Traditional machine learning-based methods for the detection of rice degree of milling (DOM) that are not comprehensive in feature extraction and have low recognition rates fail to meet the demand for fast, non-destructive, and accurate detection. This paper presents a digital image processing technology combined with deep learning to implement the classification of DOM of rice. An improved multi-scale information fusion model of the InceptionResNet–Bayesian optimization algorithm (IRBOA) was constructed based on the Inception-v3 structure and residual network (ResNet) model. It enables to automatically extract more comprehensive features of rice and determine the DOM of rice. Additionally, the important hyperparameters in the model were tuned by the BOA to optimize the recognition rate of rice DOM. The results show the hyperparameters optimized using the BOA are those that would not be chosen in manual tuning. The classification precision of the IRBOA model reached 99.22%, 94.92%, and 96.55% for well-milled, reasonably well-milled, and substandard rice, respectively, with an average accuracy of no less than 96.90%. This model improved 7.41% over the traditional machine learning model and at least 1.35% over the fashionable CNN model with strong generalization performance. This method effectively completes rapid, non-destructive, and accurate intelligent detection of rice DOM, which can supply a reliable and accurate technical mean for rice processing enterprises to guide the rice processing process.

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An Improved EfficientNet for Rice Germ Integrity Classification and Recognition

Cited by 14 publications

References 39 publications

AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

Diagnosing Diseases From Fingernail Images

Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model

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