Tiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, process, and provide results without transferring data to external entities. The technology aims to democratize AI by making it available to more sectors and contribute to the digital revolution of intelligent devices. In this work, a classification of the most common optimization techniques for Neural Network compression is conducted. Additionally, a review of the development boards and TinyML software is presented. Furthermore, the work provides educational resources, a classification of the technology applications, and future directions and concludes with the challenges and considerations.

Food safety is a fundamental right in modern societies. One of the most pressing problems nowadays is the provenance of food and food-related products that citizens consume, mainly due to several food scares and the globalization of food markets, which has resulted in food supply chains that extend beyond nations or even continent boundaries. Food supply networks are characterized by high complexity and a lack of openness. There is a critical requirement for applying novel techniques to verify and authenticate the origin, quality parameters, and transfer/storage details associated with food. This study portrays an end-to-end approach to enhance the security of the food supply chain and thus increase the trustfulness of the food industry. The system aims at increasing the transparency of food supply chain monitoring systems through securing all components that those consist of. A universal information monitoring scheme based on blockchain technology ensures the integrity of collected data, a self-sovereign identity approach for all supply chain actors ensures the minimization of single points of failure, and finally, a security mechanism, that is based on the use of TinyML’s nascent technology, is embedded in monitoring devices to mitigate a significant portion of malicious behavior from actors in the supply chain.

Internet of Things devices are frequently used as consumer devices to provide digital solutions, such as smart lighting and digital voice-activated assistants, but they are also employed to alert residents in the instance of an emergency. Given the increasingly costly nature of present neural network systems, it is necessary to transport information to the cloud for intelligent machine analysis. TinyML is a potential technology that has been presented by the research world for building fully independent and safe devices that can gather, analyze, and produce data, without transferring it to distant organizations. This paper describes a gas leakage detection system based on TinyML. The proposed solution can be programmed to identify anomalies and warn occupants via the utilization of the BLE technology, in addition to an incorporated LCD screen. Experiments have been employed to show and assess two distinct test situations. For the first occasion, the smoke detection test case, the system earned an F1-Score of 0.77, whereas the F1-Score for the ammonia test case was 0.70.

Background: Epilepsy is one of the most common and devastating neurological disorders, manifesting with seizures and affecting approximately 1–2% of the world’s population. The criticality of seizure occurrence and associated risks, combined with the overwhelming need for more precise and innovative treatment methods, has led to the development of invasive neurostimulation devices programmed to detect and apply electrical stimulation therapy to suppress seizures and reduce the seizure burden. Tiny Machine Learning (TinyML) is a rapidly growing branch of machine learning. One of its key characteristics is the ability to run machine learning algorithms without the need for high computational complexity and powerful hardware resources. The featured work utilizes TinyML technology to implement an algorithm that can be integrated into the microprocessor of an implantable closed-loop brain neurostimulation system to accurately detect seizures in real-time by analyzing intracranial EEG (iEEG) signals. Methods: A dataset containing iEEG signal values from both non-epileptic and epileptic individuals was utilized for the implementation of the proposed algorithm. Appropriate data preprocessing was performed, and two training datasets with 1000 records of non-epileptic and epileptic iEEG signals were created. A test dataset with an independent dataset of 500 records was also created. The web-based platform Edge Impulse was used for model generation and visualization, and different model architectures were explored and tested. Finally, metrics of accuracy, confusion matrices, and ROC curves were used to evaluate the performance of the model. Results: Our model demonstrated high performance, achieving 98% and 99% accuracy on the validation and test EEG datasets, respectively. Our results support the use of TinyML technology in closed-loop neurostimulation devices for epilepsy, as it contributes significantly to the speed and accuracy of seizure detection. Conclusions: The proposed TinyML model demonstrated reliable seizure detection in real-time by analyzing EEG signals and distinguishing epileptic activity from normal brain electrical activity. These findings highlight the potential of TinyML in closed-loop neurostimulation systems for epilepsy, enhancing both speed and accuracy in seizure detection.

In viticulture, downy mildew is one of the most common diseases that, if not adequately treated, can diminish production yield. However, the uncontrolled use of pesticides to alleviate its occurrence can pose significant risks for farmers, consumers, and the environment. This paper presents a new framework for the early detection and estimation of the mildew’s appearance in viticulture fields. The framework utilizes a protocol for the real-time acquisition of drones’ high-resolution RGB images and a cloud-docker-based video or image inference process using object detection CNN models. The authors implemented their framework proposition using open-source tools and experimented with their proposed implementation on the debina grape variety in Zitsa, Greece, during downy mildew outbursts. The authors present evaluation results of deep learning Faster R-CNN object detection models trained on their downy mildew annotated dataset, using the different object classifiers of VGG16, ViTDet, MobileNetV3, EfficientNet, SqueezeNet, and ResNet. The authors compare Faster R-CNN and YOLO object detectors in terms of accuracy and speed. From their experimentation, the embedded device model ViTDet showed the worst accuracy results compared to the fast inferences of YOLOv8, while MobileNetV3 significantly outperformed YOLOv8 in terms of both accuracy and speed. Regarding cloud inferences, large ResNet models performed well in terms of accuracy, while YOLOv5 faster inferences presented significant object classification losses.

Agriculture is a high-priority sector since it creates economic opportunities and generates the majority of the world’s food. In 2050, agricultural products will be in exceptionally high demand due to a 30% increase in the world’s population. Human resources for agriculture development are disappearing as young people migrate to major cities, while agricultural land is being abused for rapid expansion. To satisfy the food demand, the major portion of agricultural tasks must be automated. Agricultural research has revealed that the Internet of Things (IoT) technology might be the future of modern and automated agriculture research. The aforementioned technology faces a number of common obstacles, including internet requirements, data transfer, and privacy concerns. TinyML is an emerging technology that delivers low-cost and highly efficient devices capable of locally running complex machine learning models and neural networks, while overcoming the majority of the aforementioned IoT issues. As a comprehensive solution for autonomously watering plants, a TinyML-based system capable of monitoring ambient conditions and plants’ soil moisture is presented in this work.