Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.

Identifying blueberry characteristics such as the wax bloom is an important task that not only helps in phenotyping (for novel variety development) but also in classifying berries better suited for commercialization. Deep learning techniques for image analysis have long demonstrated their capability for solving image classification problems. However, they usually rely on large architectures that could be difficult to implement in the field due to high computational needs. This paper presents a small (only 1502 parameters) Bayesian–CNN ensemble architecture that can be implemented in any small electronic device and is able to classify wax bloom content in images. The Bayesian model was implemented using Keras image libraries and consists of only two convolutional layers (eight and four filters, respectively) and a dense layer. It includes a statistical module with two metrics that combines the results of the Bayesian ensemble to detect potential misclassifications. The first metric is based on the Euclidean distance (${\mathcal{L}}_2$) between Gaussian mixture models while the second metric is based on a quantile analysis of the binary class predictions. Both metrics attempt to establish whether the model was able to find a good prediction or not. Three experiments were performed: first, the Bayesian–CNN ensemble model was compared with state-of-the-art small architectures. In experiment 2, the metrics for detecting potential misclassifications were evaluated and compared with similar techniques derived from the literature. Experiment 3 reports results while using cross validation and compares performance considering the trade-off between accuracy and the number of samples considered as potentially misclassified (not classified). Both metrics show a competitive performance compared to the state of the art and are able to improve the accuracy of a Bayesian–CNN ensemble model from $96.98\%$ to $98.72\pm 0.54\%$ and $98.38\pm 0.34\%$ for the ${\mathcal{L}}_2$ and $r^2$ metrics, respectively.