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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63376
Volume:
10 |
5 |
May 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
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.

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY,TUMAKURU
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
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.
[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.”
Publication Date
5/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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