Deep Convolutional Neuronal Networks for Robotic Food Classification and Caloric Estimation

B. BHAVYA
Assistant Professor, Dept Of CSE CMR Technical Campus Hyderabad, Telangana, India
bhavya.cse@cmrtc.ac.in
M. HARINATH
UG Student, Dept Of CSE CMR Technical Campus Hyderabad, Telangana, India
237r1a05a3@cmrtc.ac.in
R. DANIYAL
UG Student, Dept Of CSE CMR Technical Campus Hyderabad, Telangana, India
237r1a05b2@cmrtc.ac.in

BALAJI CHANDRA
UG Student, Dept Of CSE CMR Technical Campus Hyderabad, Telangana, India
227r1a05n3@cmrtc.ac.in
P. BHANU SWAROOP
UG Student, Dept Of CSE CMR Technical Campus Hyderabad, Telangana, India
237r1a05a8@cmrtc.ac.in

Abstract—In recent years, the rapid increase in lifestyle-related diseases such as obesity, diabetes, cardiovascular disorders, and hypertension has raised serious concerns regarding dietary habits and nutritional balance. Accurate monitoring of food intake and caloric consumption is essential for maintaining a healthy lifestyle. Traditional dietary tracking methods rely heavily on manual input and subjective estimation, which often results in inaccuracies and inconsistencies.To address these limitations, this research proposes an intel- ligent robotic food classification and caloric estimation system based on Deep Convolutional Neural Networks (CNNs). The proposed systemintegrates computer vision, deep learning, and nutritional analysis to  automatically recognize food items from captured images and estimate their caloric values in real time. The CNN architecture is designed to extract hierarchical visual features and perform robust classification across diverse food categories. Furthermore, the system incorporates nutritional databases to calculate daily calorie intake and provide meaningful dietary feedback. Experimental evaluations demonstrate that CNN-based ap- proaches significantly outperform traditional machine learning models in terms of classification accuracy, scalability, and com- putationalefficiency. The integration of such intelligent systems in robotic  platforms can revolutionize healthcare monitoring, dietary management, and smart nutrition tracking applications.
Index Terms—Food Recognition, Deep Learning, CNN, Calorie Estimation, Robotics, Computer Vision, Dietary Monitoring