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.

Background: Milk quality analysis (MQA) is critical for ensuring high‐quality dairy production and enhancing the productivity of dairy processing plants. The integration of Internet of Things (IoT) and artificial intelligence (AI) can revolutionise MQA by enabling automated, real‐time detection and monitoring of key parameters (i.e. composition, adulterants, indicators of microbial quality), reducing human error and improving decision‐making in the dairy supply chain.Aim(s): This systematic review aimed to evaluate the application of IoT and AI in MQA over the past decade, focussing on three critical areas: composition monitoring, adulteration detection and indicators of microbial quality.Methods: A systematic analysis of 43 studies was conducted, comparing sensing technologies (e.g. spectroscopy and gas sensors), data communication protocols (i.e. WifI, Bluetooth), data processing strategies (edge computing, cloud platforms) and AI models (Machine Learning, Deep Learning). The methodologies implemented, along with model performance evaluation metrics and the scalability of the proposed technologies, will be assessed.Major Findings: Spectroscopy emerged as a leading method for compositional and adulteration analysis (62%), while gas sensors were preferred for microbial spoilage detection. Edge computing was the leading data processing strategy (64%), enabling real‐time analysis, though hybrid edge–cloud platforms demonstrated good scalability potential. Machine Learning models (e.g. Support Vector Machines, Random Forest) achieved high predictive accuracy, while Deep Learning excelled in classification tasks, particularly for adulteration detection.Scientific Implications: Despite the significant advancements, challenges remain in developing generalised models applicable across diverse milk samples (geographic regions and milk species). Current systems face limitations in data communication from remote farms with poor connectivity and computational constraints when processing complex spectral data on edge devices. Future advancements in 5G networks could address latency issues in large‐scale deployments, while smart sensors with embedded preprocessing capabilities may reduce computational constraints. Hybrid edge‐cloud platforms enable real‐time analysis while supporting model updates, enhancing smart dairy monitoring.

The dairy industry is experiencing a surge in data from Edge devices, using spectroscopic techniques for milk quality assessment. Milk spectral data can help understand the species of milk producer and detect inter-species adulteration. Transmitting raw milk spectral data to the cloud for processing faces challenges due to limited network resources such as bandwidth, computational memory, and energy availability. Edge processing offers a solution by training data closer to the source, enhancing efficiency and real-time analysis by providing reduced latency, improved accuracy, resource-aware computation, and real-time customization. However, traditional Deep Learning (DL) methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) struggle on resource-constrained Edge devices due to complexity. To address this, we propose an Edge-Centric Application-Adaptive Light-Weight DL approach (AppAdapt-LWDL) for milk species identification and adulteration detection. Our method optimizes DL models via double model optimization, involving low-magnitude pruning and post-training quantization. Our novel application-adaptive algorithm balances speed and accuracy by determining the pruning ratio automatically for the specific application. The chosen model is then quantized for smaller databases, ideal for embedded devices. The AppAdapt-LWDL framework significantly accelerates training, speeds up inferencing, enhances energy efficiency, and maintains accuracy based on application needs.

Ireland has a wide variety of farmlands that includes arable fields, grassland, hedgerows, streams, lakes, rivers, and native woodlands. Traditional methods of habitat identification rely on field surveys, which are resource intensive, therefore there is a strong need for digital methods to improve the speed and efficiency of identification and differentiation of farmland habitats. This is challenging because of the large number of subcategories having nearly indistinguishable features within the habitat classes. Heterogeneity among sites within the same habitat class is another problem. Therefore, this research work presents a preliminary technique for accurate farmland classification using stacked ensemble deep convolutional neural networks (DNNs). The proposed approach has been validated on a high-resolution dataset collected using drones. The image samples were manually labelled by the experts in the area before providing them to the DNNs for training purposes. Three pre-trained DNNs customized using the transfer learning approach are used as the base learners. The predicted features derived from the base learners were then used to train a DNN based meta-learner to achieve high classification rates. We analyse the obtained results in terms of convergence rate, confusion matrices, and ROC curves. This is a preliminary work and further research is needed to establish a standard technique.

Recent advancements in technology have led to the emergence of Cyber-Physical Systems (CPS), which seamlessly integrate the cyber and physical domains in various sectors such as agriculture, autonomous systems, and healthcare. This integration presents opportunities for enhanced efficiency and automation through the utilization of artificial intelligence (AI) and machine learning (ML). However, the complexity of CPS brings forth challenges related to transparency, bias, and trust in AI-enabled decision-making processes. This research explores the significance of AI and ML in enabling CPS in these domains and addresses the challenges associated with interpreting and trusting AI systems within CPS. Specifically, the role of explainable AI (XAI) in enhancing trustworthiness and reliability in AI-enabled decision-making processes is discussed. Key challenges such as transparency, security, and privacy are identified, along with the necessity of building trust through transparency, accountability, and ethical considerations.