Enhanced Livestock Monitoring with Modified Kalman Filter and Decision Tree Algorithm for Noise-Reduction in Sensor Data

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 5

Year of Publication : 2025

Authors : Arun. C.A, Sudhan. M. B, Thripthi P Balakrishnan, S Gopinath, N. Srividhya, V. Saravanan, S. Navaneethan

10.14445/23488379/IJEEE-V12I5P116

How to Cite?

Arun. C.A, Sudhan. M. B, Thripthi P Balakrishnan, S Gopinath, N. Srividhya, V. Saravanan, S. Navaneethan, "Enhanced Livestock Monitoring with Modified Kalman Filter and Decision Tree Algorithm for Noise-Reduction in Sensor Data," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 5, pp. 176-189, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I5P116

Abstract:

Kalman filtering, a robust statistical estimation method, has emerged as a pivotal tool across disciplines, excelling in noise reduction and state estimation with minimal computational demands. Its adaptability has fostered diverse implementations, notably in complex sensory and robotic systems. This technique significantly enhances system reliability and efficiency by effectively filtering signals from noise, thereby catalyzing technological progress across numerous sectors. The method is precious in modern systems that rely on high-sensitivity sensors like accelerometers and gyroscopes, which are crucial for improved performance but vulnerable to noise. By addressing the challenge of noisy data readings, which can significantly impact system performance. By strategically deploying sensors on cattle, real-time data on animal movements are collected, and with this, we can predict valuable insights into their daily activities are possible. This innovative research on animal welfare management has been adopted to optimize farm operations. The suggested revised algorithm of the standard Kalman filter can effectively minimize noise in the livestock management system. Different combinations of values for the Q and R variables are tested and tabulated at Q = 0.01 and R = 100, also Q = 0.01, and R = 1000 we got maximum results. Also, we get better results on the proposed modified Kalman filter with the decision tree algorithm, which effectively predicts the actual data with an accuracy of 88.67%, precision of 87.53%, recall of 87.5%, and F1 score of 87.47%, indicating its strong ability to capture the underlying patterns in the dataset. In contrast, the Linear Regression model may be underfitting due to its inability to model such non-linearity effectively. When comparing the results, the decision tree regression method outperformed linear regression and polynomial regression methods. Also, it is well-suited for capturing sudden shifts and plateaus in the cattle's behavior. This innovative project adopted an advanced system that enhances animal welfare management and optimizes farm operations.

Keywords:

Kalman filter, Regression algorithms, Livestock management system, MPU6050 and ESP12 Microcontroller.

References:

[1] Rudolph Emil Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Journal of Fluids Engineering, vol. 82, no. 1, pp. 35-45, 1960.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Claudio Urrea, and Rayko Agramonte, “Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation,” Journal of Sensors, vol. 2021, no. 1, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] S.A. Quadri, and Othman Sidek, “Error and Noise Analysis in an IMU using Kalman Filter,” International Journal of Hybrid Information Technology, vol. 7, no. 3, pp. 39-48, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Xianliang Chen et al., “Kalman Filter and Neural Network Fusion for Fault Detection and Recovery in Satellite Attitude Estimation,” Acta Astronautica, vol. 217, pp. 48-61, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Kunal Kunal et al., “Accelerometer Implementation as Feedback on 5 Degree of Freedom Arm Robot,” Journal of Robotics and Control (JRC), vol. 1, no. 1, pp. 31-34, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Xie Pengfei, Chu Shiwen, and Yan Zhang, “Design of Pose Measurement and Display System Based on STM32 and MPU6050,” IEEE International Conference on Intelligent Computing, Automation and Systems, Chongqing, China, pp. 71-74, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] K. Kalaiarasi et al., “Advanced Deep Learning Approaches for Enhanced Satellite Image Analysis and Comprehensive Land Use Classification in Diverse Environmental Contexts,” IEEE International Conference on Cybernation and Computation (CYBERCOM), Dehradun, India, pp. 603-608, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] G.R. Kanagachidambaresan et al., “Harnessing Machine Learning Based Sequence Prediction for Industry 4.0 Applications,” IEEE 15th International Conference on Computing Communication and Networking Technologies, Kamand, India, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] C. Aquilani et al., “Review: Precision Livestock Farming Technologies in Pasture-Based Livestock Systems,” Animal, vol. 16, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] E.S. Fogarty et al., “Can Accelerometer Ear Tags Identify Behavioural Changes in Sheep Associated with Parturition?,” Animal Reproduction Science, vol. 216, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] S. Maheswari, and E. Malarvizhi, “IoT-Powered Real-Time Soldier Retrieval and Surveillance System for Intervention in Critical Combat Environments,” Communications on Applied Nonlinear Analysis, vol. 32, no. 9s, 2025.
[CrossRef] [Publisher Link]
[12] Kristina Dineva, and Tatiana Atanasova, “Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud,” Animals, vol. 13, no. 20, pp. 1-28, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] A. Suganya et al., “Innovative System for Environmental Monitoring with Connected Sensors and Data Analysis,” IEEE International Conference on Intelligent Systems and Advanced Applications, Pune, India, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Reza Arablouei et al., “In-Situ Classification of Cattle Behavior using Accelerometry Data,” Computers and Electronics in Agriculture, vol. 183, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] ESP-12F Datasheet, Ai-Thinker, 2018. [Online]. Available: https://docs.ai-thinker.com/_media/esp8266/docs/esp-12f_product_specification_en.pdf
[16] Yan Pei et al., “An Elementary Introduction to Kalman Filtering,” Communications of the ACM, vol. 62, no. 11, pp. 122-133, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Alfian Ma'arif et al., “Kalman Filter for Noise Reducer on Sensor Readings,” Signal and Image Processing Letters, vol. 1, no. 2, pp. 50-61, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Anggun Winursito, Ibnu Masngut, and Gilang N.P. Pratama, “Development and Implementation of Kalman Filter for IoT Sensors: Towards a Better Precision Agriculture,” IEEE 3rd International Seminar on Research of Information Technology and Intelligent Systems, Yogyakarta, Indonesia, pp. 360-364, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Rio Ikhsan Alfian, Alfian Ma'arif, and Sunardi Sunardi, “Noise Reduction in the Accelerometer and Gyroscope Sensor with the Kalman Filter Algorithm,” Journal of Robotics and Control, vol. 2, no. 3, pp. 180-189, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Qian-Qian Qian, and Yu-Qing Bao, “Application of Kalman-Filtering with Disturbance Estimation in Power System Frequency Control,” IEEE/IAS Industrial and Commercial Power System Asia, Chengdu, China, pp. 782-787, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Dah-Jing Jwo, and Amita Biswal, “Implementation and Performance Analysis of Kalman Filters with Consistency Validation,” Mathematics, vol. 11, no. 3, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Arvind Dagur et al., Artificial Intelligence, Blockchain, Computing and Security, 1st eds, CRC Press, London, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Mustapha Dakkak et al., “Indoor Localization Method Based on RTT and AOA Using Coordinates Clustering,” Computer Networks, vol. 55, no. 8, pp. 1794-1803, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Suliman Abdulmalek et al., “IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review,” Healthcare, vol. 10, no. 10, pp. 1-32, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] V. Sharmila et al., Challenges in Information, Communication and Computing Technology, Proceedings of the 2nd International Conference on Challenges in Information, Communication, and Computing Technology (ICCICCT 2024), Namakkal, Tamil Nadu, India, 1st eds, CRC Press, London, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Shuang Wang et al., “Machine-Learning-Based Human Motion Recognition via Wearable Plastic-Fiber Sensing System,” IEEE Internet of Things Journal, vol. 10, no. 20, pp. 17893-17904, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Hyun Myung et al., “Mobile Robot Localization Using a Gyroscope and Constrained Kalman Filter,” IEEE SICE-ICASE International Joint Conference, Busan, Korea (South), pp. 2098-2103, 2006.
[CrossRef] [Google Scholar] [Publisher Link]