Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks
The livestock of Pakistan includes different animal breeds utilized for milk farming and exporting worldwide. Buffalo have a high milk production rate, and Pakistan is the third-largest milk-producing country, and its production is increasing over time. Hence, it is essential to recognize the best B...
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MDPI AG
2022-09-01
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| Series: | Agriculture |
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| author | Yuanzhi Pan Hua Jin Jiechao Gao Hafiz Tayyab Rauf |
| author_facet | Yuanzhi Pan Hua Jin Jiechao Gao Hafiz Tayyab Rauf |
| author_sort | Yuanzhi Pan |
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| description | The livestock of Pakistan includes different animal breeds utilized for milk farming and exporting worldwide. Buffalo have a high milk production rate, and Pakistan is the third-largest milk-producing country, and its production is increasing over time. Hence, it is essential to recognize the best Buffalo breed for a high milk- and meat yield to meet the world’s demands and breed production. Pakistan has the second-largest number of buffalos among countries worldwide, where the Neli-Ravi breed is the most common. The extensive demand for Neli and Ravi breeds resulted in the new cross-breed “Neli-Ravi” in the 1960s. Identifying and segregating the Neli-Ravi breed from other buffalo breeds is the most crucial concern for Pakistan’s dairy-production centers. Therefore, the automatic detection and classification of buffalo breeds are required. In this research, a computer-vision-based recognition framework is proposed to identify and classify the Neli-Ravi breed from other buffalo breeds. The proposed framework employs self-activated-based improved convolutional neural networks (CNN) combined with self-transfer learning. Moreover, feature maps extracted from CNN are further transferred to obtain rich feature vectors. Different machine learning (Ml) classifiers are adopted to classify the feature vectors. The proposed framework is evaluated on two buffalo breeds, namely, Neli-Ravi and Khundi, and one additional target class contains different buffalo breeds collectively called Mix. The proposed research achieves a maximum of 93% accuracy using SVM and more than 85% accuracy employing recent variants. |
| format | Article |
| id | doaj-art-e83fc67485fa4631a052900f761f9725 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-e83fc67485fa4631a052900f761f97252025-01-23T14:03:51ZengMDPI AGAgriculture2077-04722022-09-01129138610.3390/agriculture12091386Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural NetworksYuanzhi Pan0Hua Jin1Jiechao Gao2Hafiz Tayyab Rauf3Faculty of Business and Economics, The University of Hong Kong, Hong Kong 999077, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaDepartment of Computer Science, University of Virginia, Charlottesville, VA 22903, USAIndependent Researcher, Bradford BD8 0HS, UKThe livestock of Pakistan includes different animal breeds utilized for milk farming and exporting worldwide. Buffalo have a high milk production rate, and Pakistan is the third-largest milk-producing country, and its production is increasing over time. Hence, it is essential to recognize the best Buffalo breed for a high milk- and meat yield to meet the world’s demands and breed production. Pakistan has the second-largest number of buffalos among countries worldwide, where the Neli-Ravi breed is the most common. The extensive demand for Neli and Ravi breeds resulted in the new cross-breed “Neli-Ravi” in the 1960s. Identifying and segregating the Neli-Ravi breed from other buffalo breeds is the most crucial concern for Pakistan’s dairy-production centers. Therefore, the automatic detection and classification of buffalo breeds are required. In this research, a computer-vision-based recognition framework is proposed to identify and classify the Neli-Ravi breed from other buffalo breeds. The proposed framework employs self-activated-based improved convolutional neural networks (CNN) combined with self-transfer learning. Moreover, feature maps extracted from CNN are further transferred to obtain rich feature vectors. Different machine learning (Ml) classifiers are adopted to classify the feature vectors. The proposed framework is evaluated on two buffalo breeds, namely, Neli-Ravi and Khundi, and one additional target class contains different buffalo breeds collectively called Mix. The proposed research achieves a maximum of 93% accuracy using SVM and more than 85% accuracy employing recent variants.https://www.mdpi.com/2077-0472/12/9/1386buffalo breedsNeural NetworksSelf Activated CNNdeep learning |
| spellingShingle | Yuanzhi Pan Hua Jin Jiechao Gao Hafiz Tayyab Rauf Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks Agriculture buffalo breeds Neural Networks Self Activated CNN deep learning |
| title | Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks |
| title_full | Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks |
| title_fullStr | Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks |
| title_full_unstemmed | Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks |
| title_short | Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks |
| title_sort | identification of buffalo breeds using self activated based improved convolutional neural networks |
| topic | buffalo breeds Neural Networks Self Activated CNN deep learning |
| url | https://www.mdpi.com/2077-0472/12/9/1386 |
| work_keys_str_mv | AT yuanzhipan identificationofbuffalobreedsusingselfactivatedbasedimprovedconvolutionalneuralnetworks AT huajin identificationofbuffalobreedsusingselfactivatedbasedimprovedconvolutionalneuralnetworks AT jiechaogao identificationofbuffalobreedsusingselfactivatedbasedimprovedconvolutionalneuralnetworks AT hafiztayyabrauf identificationofbuffalobreedsusingselfactivatedbasedimprovedconvolutionalneuralnetworks |