Author
Listed:
- Yuanzhi Pan
(Faculty of Business and Economics, The University of Hong Kong, Hong Kong 999077, China
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
Artificial Intelligence Lab, Zhenjiang Hongxiang Automation Technology Co., Ltd., Zhenjiang 212050, China)
- Hua Jin
(School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China)
- Jiechao Gao
(Department of Computer Science, University of Virginia, Charlottesville, VA 22903, USA
Department of Electrical Engineering, Columbia University, New York City, NY 10027, USA)
- Hafiz Tayyab Rauf
(Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK)
Abstract
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.
Suggested Citation
Yuanzhi Pan & Hua Jin & Jiechao Gao & Hafiz Tayyab Rauf, 2022.
"Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks,"
Agriculture, MDPI, vol. 12(9), pages 1-19, September.
Handle:
RePEc:gam:jagris:v:12:y:2022:i:9:p:1386-:d:906149
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1386-:d:906149. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.