Hybrid Blended Deep Learning Approach for Milk Quality Analysis

Abstract

There has been an increase in the implementation of Artificial Intelligence (AI) in the dairy industry for Milk Quality Analysis (MQA). However, traditional Machine Learning (ML) algorithms may not be effective due to non-linearity in milk spectral data and the requirement of pre-processing. Important features from the spectral data may be lost during the pre-processing stage, which is a severe problem. Deep Learning (DL) can help by eliminating the need for pre-processing, thereby avoiding the loss of information. Although traditional DL methods have been used in dairy farming applications, fewer studies indicate the use of DL for MQA. Therefore, there is a need to develop novel DL models for MQA to improve the classification accuracy for milk quality monitoring. This study proposes a Hybrid Blended Deep Learning (HyBDL) approach for better classification accuracy and lower prediction errors. The proposed model outperformed classical DL and Blended DL models in terms of overall accuracy, loss, and class-wise accuracy used in this study. The model achieved 98.03% accuracy and lower Mean Squared Error (MSE) scores for each iteration, and its power consumption, energy consumption, and training time were evaluated. To support our work, we calculated the reproducibility score for all the models, representing how consistent the results are when repeated multiple times. Time complexity analysis of the models is performed to compare the resource consumption and training times for the base learners and HyBDL model. To further validate the performance of our model, we have trained it on different resource-intensive edge devices, such as the NVIDIA Jetson Nano and a low-end device. Edge devices can be used in dairy processing plants to provide real-time milk quality predictions making it essential to this field of research. Our proposed HyBDL model outperformed all the other models by having a low deviation score of 0.0037 for ten iterations and 0.0077 for 100 iterations showing high reproducibility.