Issue |
BIO Web Conf.
Volume 82, 2024
International Scientific and Practical Conference “Methods for Synthesis of New Biologically Active Substances and Their Application in Various Industries of the World Economy – 2023” (MSNBAS2023)
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Article Number | 05020 | |
Number of page(s) | 11 | |
Section | Economic Aspects of the Production and Use of Biologically Active Substances | |
DOI | https://doi.org/10.1051/bioconf/20248205020 | |
Published online | 03 January 2024 |
An Integrated Approach to Dairy Farming: AI and IoT-Enabled Monitoring of Cows and Crops via a Mobile Application
Faculty of CS & IT, Kalinga University, Naya Raipur, Chhattisgarh, India
* Corresponding author: ku.devraspandey@kalingauniversity.ac.in
The globalized and fiercely competitive nature of the international market has expanded the range of demands across all sectors of the agri-food business. The dairy business needs to adjust to the prevailing market conditions by enhancing resource efficiency, adopting environmentally sustainable practices, promoting transparency, and ensuring security. The Internet of Things (IoT), Edge Computing (EC), and deep learning play pivotal roles in facilitating these advancements as they enable the digitization of various components within the value chain. Solutions that depend on human observation via visual inspections are susceptible to delayed detection and potential human mistakes and need more scalability. The growing herd numbers raise a significant worry due to the potential negative impact on cow health and welfare, particularly about extended or undiscovered lameness. This condition has severe consequences for cows, eventually leading to a decline in milk output on the farm. To address this issue, an Integrated Approach to Dairy Farming (IA-DF) has been developed, which utilizes sophisticated Artificial Intelligence (AI) and data analytics methodologies using mobile applications to continuously monitor livestock and promptly detect instances of lameness in cattle. Initially, the VGG16 model, pre-trained on the ImageNet dataset, was used as the underlying architecture to extract the sequence of feature vectors associated with each video. This approach was adopted to circumvent the limitations of conventional feature engineering methods, which tend to be both time-consuming and labor-intensive with deep learning-based classification algorithms. IA-DF can extract semantic details from historical data in both forward and backward directions, hence enabling precise identification of fundamental behaviors shown by dairy cows.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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