Good air quality is a critical determinant of public health, influencing life expectancy, respiratory health, work productivity, and the prevention of chronic diseases. This study presents a novel approach to classifying the Air Quality Index (AQI) using deep learning techniques, specifically convolutional neural networks (CNNs). We collected and curated a dataset comprising 11,000 digital images from three distinct regions in Indonesia—Jakarta, Malang, and Semarang—ensuring uniformity through standardized acquisition settings. The images were categorized into four air quality classes: good, moderate, unhealthy for sensitive groups, and unhealthy. We designed and implemented a CNN architecture optimized for AQI classification. The model achieved an impressive accuracy of 99.81% using K-fold cross-validation. In addition, the model’s interpretative capabilities were examined using techniques such as Grad-CAM, providing valuable insights into how the CNN identifies and classifies air quality conditions based on image features. These findings underscore the effectiveness of CNNs for AQI classification and highlight the potential for future work to incorporate a more diverse set of digital images captured from various perspectives to enhance dataset complexity and model robustness. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.15727522.

A new statistical method for estimating the orientation distribution of fibres in a fibre process is suggested where the process is observed in the form of a degraded digital greyscale image. The method is based on line transect sampling of the image in a few fixed directions. A well-known method based on stereology is available if the intersections between the transects and fibres can be counted. We extend this to the case where, instead of the intersection points, only scaled variograms of grey levels along the transects are observed. The nonlinear estimation equations for a parametric orientation distribution as well as a numerical algorithm are given. The method is illustrated by a real-world example and simulated examples where the elliptic orientation distribution is applied. In its simplicity, the new approach is intended for industrial on-line estimation of fibre orientation in disordered fibrous materials.

A method to assist the reading of calcified structures with digital images is proposed. An a priori growth pattern is introduced in the image processing to take into account both the variability of the ring size and the non-linearity of their growth. The introduction of biological knowledge is performed within a demodulation processing using the reciprocal growth function. The ring number is estimated by spectral analysis. The constraints introduced by the growth model are weak since they are restricted to the definition of L0, in the case of an exponential model, or of L∞ in the case of a Von Bertalanffy model. When applied to larval Dover sole otoliths (Solea solea) results close to the expert's are obtained. In the case of saithe otoliths (Pollachius virens) the mean discrepancy with the expert's, i.e. 1.5 years, can be assigned to the lack of processing of marginal rings and to the inadequacy of the morphogenesis model on which the processing algorithm is based. This approach which is only founded on the dynamic features of the "biological signal", will have to be completed in future work with the introduction of structural information.