This review aims to explore the literature to assess the potential of artificial intelligence (AI) in environmental monitoring for predicting microbiome dynamics. Recognizing the significance of comprehending microorganism diversity, composition, and ecologically sustainable impact, the review emphasizes the importance of studying how microbiomes respond to environmental changes to better grasp ecosystem dynamics. This bibliographic search examines how AI (Machine Learning and Deep Learning) approaches are employed to predict changes in microbial diversity and community composition in response to environmental and climate variables, as well as how shifts in the microbiome can, in turn, influence the environment. Our research identified a final sample of 50 papers that highlighted a prevailing concern for aquatic and terrestrial environments, particularly regarding soil health, productivity, and water contamination, and the use of specific microbial markers for detection rather than shotgun metagenomics. The integration of AI in environmental microbiome monitoring directly supports key sustainability goals through optimized resource management, enhanced bioremediation approaches, and early detection of ecosystem disturbances. This study investigates the challenges associated with interpreting the outputs of these algorithms and emphasizes the need for a deeper understanding of microbial physiology and ecological contexts. The study highlights the advantages and disadvantages of different AI methods for predicting environmental microbiomes through a critical review of relevant research publications. Furthermore, it outlines future directions, including exploring uncharted territories and enhancing model interpretability.

Vaccinium corymbosum is one of the main sources of commercialized blueberries across the world. This species has a large number of distinct cultivars, leading to significantly different berries characteristics such as size, sweetness, production rate, and growing season. In this context, accurate cultivar discrimination is of significant relevance, and currently, it is mostly performed through berries examination. In this work, we developed a method to discriminate 19 cultivars from the V. corymbosum species through their leaves’ near-infrared spectra. Spectra were acquired from fresh blueberry leaves collected from two geographic regions and across three seasons. Machine-learning-based models, selected from a pool of 10 classifiers based on their discrimination power under a twofold stratified cross-validation process, were trained/tested with 1 to 20 components obtained by the application of data dimensionality reduction (DDR) techniques (dictionary learning, factor analysis, fast individual component analysis, and principal component analysis) to different near-infrared (NIR) spectra regions’ data, to either analyze a single spectral region and season or combine spectral regions and/or seasons for each side of the blueberry leaf. The percentage of correct cultivar discrimination ranged from 52.27 to 100%, with the best spectral results obtained with the adaxial side of the leaves in the fall (100% $Accuracy$) and the abaxial side of the leaves in the winter (100% $Accuracy$); the fast ICA DDR technique was present in 83.33% of the best analyses (five out of six); and the LinearSVC was present in 66.67% (four out six best analyses). The results obtained in this work denote that near-infrared spectroscopy is a suitable and accurate technique for V. corymbosum cultivar discrimination.

Advances in Industry 4.0 manufacturing have accelerated the adoption of machine learning (ML) for automated classification. Polyester (PES), a widely used synthetic fiber, competes with natural fibers like cotton and other synthetics, highlighting the need for continuous research and improvement. In the textile sector, distinguishing recycled polyester (rPES) from virgin polyester (vPES) remains challenging due to overlapping chemical signatures and material variability. A combination of Fourier transform infrared (FTIR) spectroscopy and ML has not been explored for this purpose. In this study, we evaluated ML models to discriminate three PES fiber types (45 vPES, 65 rPES, and 55 mixed PES) using 165 FTIR spectra across four spectral regions, R1, R2, R3, and R4, as well as their combined representation. Six ML approaches were tested on data reduced with fast independent component analysis (FastICA) (1–30 components) using an 80/20 train–test dataset split. The Decision Tree classifier achieved the highest Accuracy in four of the five spectral evaluations, with classification accuracies ranging from 66.67% to 77.78% for region R4, which also had a balanced classification profile with an area-under-the-curve (AUC) value of 0.81. Notably, despite the moderate overall Accuracy, the model achieved 100% discrimination of rPES when distinguishing it from both mixed and vPES. Mixed fibers remained the most difficult to classify, highlighting the need for improved feature representation.