Convolutional neural networks for sensitive identification of tea species using electrochemical sensors

A fast and precise convolutional neural network (CNN) framework was built for classifying tea species using low-cost electrochemical profiles of samples. The model achieved an outstanding 96.3% accuracy in categorizing 9 major tea taxa from sensor input data alone. The approach combines simplicity with reliability gains over conventional analytical chemistry, thereby enabling field verification of labels along supply chains. Precision of 96% and recall exceeding 94% per class substantiate robust deployability. Charting of loss values and accuracy over training epochs validates learning efficiency. Comparative evaluation proves deep learning superiority over other models with up to 11% higher prediction rates. Normalization of input signatures was also found vital for inter-sample variability reduction and amplification of subtle inter-species differences that aid discrimination. Practical validation can entrench viability for scalable commercial adoption targeting small producers and vendors globally. In conclusion, an AI-powered rapid, low-cost sensor prototype allows tea sector modernization with data-driven quality assessments.