MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks

Sun, Henan; Xu, Hao; Liu, Bin; He, Dongjian; He, Jinrong; Zhang, Haitao; Geng, Nan

doi:10.1016/j.compag.2021.106379

Cited by 105 publications

(42 citation statements)

References 16 publications

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“…In the scenarios of real-world audio identification, it is necessary to consider the limited computational capabilities and constraints in resources, particularly low-powered devices [43]. Some previous studies tended to reduce the computational cost by reducing model size and complexity [22,55], tuning the number of parameters [56], or replacing more efficient arithmetic operations [57]. For instance, Anvarjon et al [58] applied a lightweight CNN model with fewer layers for speech emotion recognition.…”

Section: Discussionmentioning

confidence: 99%

Automated identification of chicken distress vocalizations using deep learning models

Mao

Giraudet

Liu

et al. 2022

J. R. Soc. Interface.

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The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an ‘iceberg indicator’ of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.

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Section: Discussionmentioning

confidence: 99%

Automated identification of chicken distress vocalizations using deep learning models

Mao

Giraudet

Liu

et al. 2022

J. R. Soc. Interface.

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“…In the scenarios of real-world audio identification, it is necessary to consider the limited computational capabilities and constraints in resources, particularly low powered devices [42]. Some previous studies tended to reduce the computational cost by reducing model size and complexity [22,54], tuning the number of parameters [55], or replacing more efficient arithmetic operations [56]. Anvarjon et al .…”

Section: Discussionmentioning

confidence: 99%

Automated identification of chicken distress vocalisations using deep learning models

Mao

Giraudet

Liu

et al. 2021

Preprint

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The annual global production of chickens exceeds 25 billion birds, and they are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an “iceberg indicator” of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotations, which is very labour-intensive and time-consuming. Thus, a novel light-VGG11 was developed to automatically identify chicken distress calls using recordings (3,363 distress calls and 1,973 natural barn sounds) collected on intensive chicken farms. The light-VGG11 was modified from VGG11 with a significantly smaller size in parameters (9.3 million vs 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e., precision (94.58%), recall (94.89%), F1-score (94.73%), and accuracy (95.07%), therefore more useful for model deployment in practice. To further improve the light-VGG11’s performance, we investigated the impacts of different data augmentation techniques (i.e., time masking, frequency masking, mixed spectrograms of the same class, and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. In terms of precision livestock farming, our research opens new opportunities for developing technologies used to monitor the output of distress calls in large, commercial chicken flocks.

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“…Based on the Python language, the DBN constructed by the Theano library ( Budhi et al, 2021 ) is used, and Spyder ( Baydogan and Aatas, 2021 ) is used as the programming environment. The identification and classification of folk musical instruments were tested on the Windows 7 64-bit operating system of the workstation Dell Precision Tower7910 ( Sun et al, 2021 ). The main workstation parameters are as follows: Intel (R) Xeno (R) CPU E5-2620 v3 @ 2.40 GHz (8 cores) processor, 64 GB memory, NVIDIA GeForce GTX TITAN X graphics card, 1 T disk capacity.…”

Section: Methodsmentioning

confidence: 99%

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

2022

Front. Psychol.

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In order to study the application of the deep learning (DL) method in music genre recognition, this study introduces the music feature extraction method and the deep belief network (DBN) in DL and proposes the parameter extraction feature and the recognition classification method of an ethnic music genre based on the DBN with five kinds of ethnic musical instruments as the experimental objects. A national musical instrument recognition and classification network structure based on the DBN is proposed. On this basis, a music library classification retrieval learning platform has been established and tested. The results show that, when the DBN only contains one hidden layer and the number of neural nodes in the hidden layer is 117, the basic convergence accuracy is approximately 98%. The first hidden layer has the greatest impact on the prediction results. When the input sample feature size is one-third of the number of nodes in the first hidden layer, the network performance is basically convergent. The DBN is the best way for softmax to identify and classify national musical instruments, and the accuracy rate is 99.2%. Therefore, the proposed DL algorithm performs better in identifying music genres.

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MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks

Cited by 105 publications

References 16 publications

Automated identification of chicken distress vocalizations using deep learning models

Automated identification of chicken distress vocalizations using deep learning models

Automated identification of chicken distress vocalisations using deep learning models

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

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