Abstract :

This Project Presents An Intelligent AI And Machine Learning–driven Pet Feeding System That Integrates Advanced Digital Image Processing With Convolutional Neural Networks (CNNs) To Automate Pet Species Identification And Generate Personalized Dietary Recommendations. Unlike Earlier Wildlife-monitoring Models That Relied On PCA And Template-matching Techniques, The Proposed Mechanism Utilizes Deep Learning To Extract Hierarchical Image Features Such As Fur Texture, Facial Geometry, And Body Structure Under Varying Environmental Conditions. The System Follows A Structured Pipeline Including Dataset Acquisition, Pre-processing (resizing, Normalization, Augmentation), Supervised CNN Training, Performance Evaluation, And Real-time Deployment Through A User-friendly Graphical Interface. The Trained CNN Model Classifies Pet Species With High Accuracy And Dynamically Retrieves Predefined Nutritional Guidelines Tailored To The Identified Species, Including Food Type, Portion Control, And Dietary Restrictions. This Fully Software-based Approach Eliminates Hardware Dependency And Improves Scalability By Enabling Continuous Dataset Expansion And Retraining. Performance Validation Using Accuracy, Precision, Recall, And F1-score Ensures Reliable Classification. The Proposed Framework Enhances Feeding Consistency, Minimizes Overfeeding Or Underfeeding Risks, And Promotes Data-driven Pet Nutrition Management, Offering A Scalable And Intelligent Solution For Modern Smart Pet Care Systems. Keywords- Artificial Intelligence (AI), Machine Learning (ML), Convolutional Neural Networks (CNN), Deep Learning, Digital Image Processing (DIP), Image Classification, Computer Vision, Intelligent Feeding System, Automated Pet Care, Nutritional Recommendation System, Pattern Recognition, Smart Pet Monitoring.

Published:

20-2-2026

Issue:

Vol. 26 No. 2 (2026)

Page Nos:

52-57

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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