Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63376

Automatic Cattle Feeding Bot

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63376

Volume:

10 |

5 |

May 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63376

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Share this article:

Authors

SHREEVATHSA

SHREEVATHSA

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU

Download Author Certificate
RAJESHWARI B B

RAJESHWARI B B

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU

Download Co-Author Certificate
T PRAVEEN KUMAR

T PRAVEEN KUMAR

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU

Download Co-Author Certificate
S G SHILPA

S G SHILPA

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU

Download Co-Author Certificate

Mentors

Dr.SREERANGAPPA.M

Dr.SREERANGAPPA.M

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU

Download Mentor Certificate

Author Services & Downloads

Full ArticlesAcceptance Certificate

Abstract

Modern smart agriculture techniques have been much improved by the quick development of artificial intelligence, visual computing, and integrated automation technologies. This articledescribes the design and implementation of an artificial intelligence-driven automatic cattle feeding robot meant for effective cattle feeding operations. Through fast cattle identification and automated feed delivery systems, the proposed method tries to reduce human labor, improve feeding homogeneity, and enable self- operating farm management. A Raspberry Pi 5, the TensorFlow Lite AI framework, OpenCV for picture analysis, a USB webcam, an L298N motor driver, DC motors, and a servo motor-based feed dispensing unit make up this system. The USB webcam constantly records real-time video feed from the agricultural environment, and the captured photos are evaluated with OpenCV and TensorFlow Lite for cattle identification reasons. The robot stops and starts the servo system to distribute predetermined amounts of feed when cattle are precisely located with sufficient certainty. After the feed distribution, the robot returns to its autonomous navigation and maintains surveillance of its surroundings. Developed system efficiently demonstrated real-time cattle identification, self- guided robot navigation, accurate feed dispensing, and reduced human involvement throughout reviews. The suggested Automatic Cattle Feeding Bot is an affordable, intelligent, and portable answer for smart farming that fits right into the current livestock management and agricultural automation industries.

KEYWORDS

Index Terms - Artificial Intelligence (AI) Raspberry Pi 5 TensorFlow Lite OpenCV Computer Vision Smart Farming Livestock Automation Autonomous Robot Cattle Detection Feed Dispensing System Embedded Systems Agricultural Automation.

Cite This Article

SHREEVATHSA, RAJESHWARI B B, T PRAVEEN KUMAR, S G SHILPA, Dr.SREERANGAPPA.M, "Automatic Cattle Feeding Bot," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-7, May 2026. https://doi.org/10.55041/IJSREM63376

Full Article/Conclusion

This work presented the development of an intelligent Automatic Cattle Feeding Bot designed for modern smart farming applications. The proposed system combined Artificial Intelligence, image processing, embedded control, and robotic automation to perform autonomous cattle feeding operations with reduced human involvement.   The implemented system utilized Raspberry Pi 5, TensorFlow Lite, OpenCV, USB web camera, L298N motor driver, DC motors, and servo-based feed dispensing mechanism for real- time cattle detection and automated feeding. Experimental testing confirmed that the robot was capable of detecting cattle accurately, navigating autonomously inside the farm environment, and dispensing feed in controlled quantity whenever livestock presence was identified successfully.   The automated feeding mechanism reduced manual labor requirements and improved feeding consistency compared to conventional livestock feeding methods. The use of AI-based cattle detection also minimized unnecessary feed wastage by ensuring that feed was dispensed only when cattle were present near the robot.   Overall, the developed Automatic Cattle Feeding Bot demonstrated reliable performance and effective integration of Artificial Intelligence and robotic automation technologies for livestock management applications. The proposed system provides a practical, cost-effective, and scalable solution for smart dairy farming and agricultural automation environments.

References

[1]       N. Umapathi, R. Jegadeesan, G. Karthick, N. Venkateswaran, and R. Ramana, “IoT based smart feeding system for livestock,” in Proc. IEEE Conf. Emerging Smart Computing and IoT Applications, 2025, pp. 1–6.   [2]    C. Giannone, “Computer vision techniques for real- time dairy cattle monitoring,” Smart Agricultural Systems, 2025. [3]      V. C. Souza, M. H. Ferreira, and A. L. Silva, “Production responses of dairy cows to precision feeding based on historical performance during short-term changes in supplementation,” Journal of Animal Science, vol. 103, Jan. 2025.   [4]  J. Tinoco, F. Tinoco, P. Cuyubamba, and J. Huaytalla, “A multi-sensory cattle monitoring system with wireless and solar charging capabilities,” in Proc. IEEE 12th Int. Conf. Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, Aug. 2024, pp. 195–199.   [5]    J. O. Chelotti and G. G. Santos, “Livestock feeding behaviour: A review on automated sensing, signal processing, and monitoring approaches,” Biosystems Engineering, vol. 236, pp. 80–98, 2024. [6]   G. D. Siraskar, S. Pawade, R. Biradar, V. K. Javanjal, H. Tiwari, and L. R. Raut, “Development and implementation of an automated cattle feeding system using cost-effective automation techniques,” Journal of Electrical Systems, vol. 20, no. 3, pp. 1–9, 2024. [7]   Google Developers, “TensorFlow Lite for embedded and edge AI applications.”

Editorial & Review Transparency

Publication Date

5/17/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

0

Total views since publication

PDF Downloads

2

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...