Machine learning-based detection of freezing events using infrared thermography

Abstract

Frost can cause irreversible damage to plant tissue and can significantly reduce yields and quality. A thorough understanding of the freezing dynamics is crucial to developing strategies for frost protection and the prevention of freezing damage. This study investigated artificial intelligence machine learning (ML) models to capture the thermodynamic patterns of freezing based on infrared thermography (IRT) imagery, which would help to automate the image analysis process in real-time or post frost events. A small-scale dataset of IRT images was collected to capture the freezing process from sample droplets containing ice-nucleating bacterium. We performed several ML experiments on the data to detect the transitions in temperatures from the images. We evaluated five popular ML models, namely support vector machines, random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron, and convolutional neural networks. We analysed the dataset to classify adjacent temperature transitions. The results show that ML models can consistently capture the thermodynamics of frost events, i.e., ice-nucleation and freezing points on typical freezing curves. Amongst the ML models, RF and XGBoost achieved the best results, both with an average accuracy of 87–88% in classifying the temperatures. With 0.25 °C temperature transitions, RF model identified the ice nucleation and freezing points at around −2.25 °C to −2.75 °C and −4.25 °C to −4.75 °C, respectively. RF and XGBoost took about 7.3 ms and 5.5 ms time per image respectively, which indicates that these models can be used in real-time applications. Our study shows that ML models using IRT imagery can be used as an automatic real-time tool to accurately detect the critical temperatures for frost formation.