Animals’ cultivation faces challenges such as animal abuse by ranchers, problems of growth, financial uncertainty, instability of input use, consumer retailer knowledge, and poor animal healthcare, among others. The review aims at specifically focusing on the abuse of animal cultivation by ranchers. This specific issue is linked to the concept of plant cultivation. Production line cultivation is the primary cause of animal ill treatment and abuse. These quiet victims have been transformed into equipment that produce meat, milk, and eggs for human consumption. These animals are conscious beings with the innate desire to live, but they are cruelly treated by the ranchers entrusted with the responsibility of managing them. In this paper, a model for shrewd monitoring of livestock using fuzzy logic was created. The survey included the waterfall model as a methodology, and the execution of the model was done using the python programming language. From the experiment, the results showed that the model performed better than other existing models with precision and area under bend of 77% and 0.81, individually. In Conclusion, the survey would be instrumental to the agricultural sector and other experts for effective monitoring and treatment of their livestock.
In this review, we proposed a fuzzy logic-based model for shrewd livestock monitoring as well as the advances that controlled the precision of the presentation of the created framework. The study also proposed an AI model for mechanized domesticated animals observing and the executives. Also, an amazing ongoing representation observing framework for improving trust and security in domesticated animals checking and the board. Lastly, an enhanced system for precisely observing the wellbeing status of a suspected creature.
References
[1]Ayman, S., Osamah, K., & Ghaida, A. (2021). An adaptive intelligent alarm system for wireless sensor networks. Indonesian Journal of Electrical Engineering & Computer Science, 15(1), 142–147.
[2]Bradley, A. W., & Murphy, G. C. (2021). Supporting software history exploration. In Proceedings of the 8th Working Conference on Mining Software Repositories (pp. 193–202). ACM Press.
[3]Banjol, S. D. (2023). Clot-Net: A scalable cognitive IoT-based smart city network and architecture. Human-Centric Computing and Information Sciences. https://doi.org/10.1186/5/3673-019-0190-9
[4]Chen, H. (2020). Applications of fuzzy logic in data mining processes. In Z. H. Bai & Y. Bai (Eds.), Advanced fuzzy logic technologies in industrial applications. Springer.
[5]Chibroma, A. (2021). Structured generative models using the IoT. In Proceedings of the 31st International Conference on Machine Learning (Vol. 32, pp. 1–14). JMLR Workshop and Conference Proceedings.
[6]Danilo, R., Shakir, M., Ivo, D., Karol, G., & Daan, W. (2021). One-shot generalization in generative models. In Proceedings of the International Conference on Machine Learning (Vol. 48, pp. 1–9). JMLR Workshop and Conference Proceedings.
[7]David, A., & Graham, P. (2022). Design science in decision support systems research: Assessment using the Hevner, March, Park, and Ram guidelines. Journal of the Association for Information Systems, 13(11), 923–949.
[8]Dyer, R., Nguyen, H. A., Rajan, H., & Nguyen, T. N. (2022). Boa: Ultra-large-scale repository and source code mining. ACM Transactions on Software Engineering and Methodology, 25(1).
[9]Eissa, A. (2020). Smart sustainable agriculture (SSA) solution underpinned by Internet of Things (IoT) and artificial intelligence (AI). International Journal of Advanced Computer Science and Applications, 10(5), 93–102.
[10]Ekong, V. (2023). A fuzzy inference system for predicting depression risk levels. African Journal of Mathematics and Computer Science Research, 6(10), 197–204.
[11]Lamik, I., Anthony, B., Mihaela, S., & Charlotte, D. (2020). Deep generative models for 3D linker design. Journal of Chemical Information and Modeling, 60, 1983–1995.
Hannah, M., & Julie, S. (2023). A review of age-friendly virtual assistive technologies and their effect on daily living for carers and dependent adults. Healthcare, 7, 49. https://doi.org/10.3390/healthcare7010049
How to Cite This Paper
Promise Enyindah, Umejuru Daniel (2026). A Fuzzy Logic Based Model for Shrewd Livestock Monitoring. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
