Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of target functions and data distributions over time (concept drifts). Most existing work relies solely on labeled data to adapt to concept drifts in classification problems. However, labeling all instances in a potentially life-long data stream is frequently prohibitively expensive, hindering such approaches. Therefore, we propose a novel algorithm to exploit unlabeled instances, which are typically plentiful and easily obtained. The algorithm is an online semisupervised radial basis function neural network (OSNN) with manifold-based training to exploit unlabeled data while tackling concept drifts in classification problems. OSNN employs a novel semisupervised learning vector quantization (SLVQ) to train network centers and learn meaningful data representations that change over time. It uses manifold learning on dynamic graphs to adjust the network weights. Our experiments confirm that OSNN can effectively use unlabeled data to elucidate underlying structures of data streams while its dynamic topology learning provides robustness to concept drifts.

This study presents a bibliometric review aimed at mapping and analyzing the scientific literature related to the ethical implications of artificial intelligence (AI) in livestock farming, which is a rapidly emerging yet still underexplored field in international research. Based on the Scopus database, 151 documents published between 2015 and 2025 were identified and analyzed using the VOSviewer version 1.6.20 and Biblioshiny for Bibliometrix (RStudio version 2023.12.1) tools. The results show a significant increase in publications from 2021 onwards, reflecting the growing maturity of discussions around the integration of digital technologies in the agricultural sector. Keyword co-occurrence and bibliographic coupling analyses revealed the formation of four main thematic clusters, covering technical applications in precision livestock farming as well as reflections on governance, animal welfare, and algorithmic justice. The most influential authors, high-impact journals, and leading countries in the field were also identified. As a key contribution, this study highlights the lack of robust ethical guidelines and proposes future research directions for the development of regulatory frameworks, codes of conduct, and interdisciplinary approaches. The findings underscore the importance of aligning technological innovation with ethical responsibility and social inclusion in the transition to digital livestock farming.

Precision agriculture requires accurate methods for classifying crops and soil cover in agricultural production areas. The study aims to evaluate three machine learning-based classifiers to identify intercropped forage cactus cultivation in irrigated areas using Unmanned Aerial Vehicles (UAV). It conducted a comparative analysis between multispectral and visible Red-Green-Blue (RGB) sampling, followed by the efficiency analysis of Gaussian Mixture Model (GMM), K-Nearest Neighbors (KNN), and Random Forest (RF) algorithms. The classification targets included exposed soil, mulching soil cover, developed and undeveloped forage cactus, moringa, and gliricidia in the Brazilian semiarid. The results indicated that the KNN and RF algorithms outperformed other methods, showing no significant differences according to the kappa index for both Multispectral and RGB sample spaces. In contrast, the GMM showed lower performance, with kappa index values of 0.82 and 0.78, compared to RF 0.86 and 0.82, and KNN 0.86 and 0.82. The KNN and RF algorithms performed well, with individual accuracy rates above 85% for both sample spaces. Overall, the KNN algorithm demonstrated superiority for the RGB sample space, whereas the RF algorithm excelled for the multispectral sample space. Even with the better performance of multispectral images, machine learning algorithms applied to RGB samples produced promising results for crop classification.

Semisupervised classification (SSC) consists of using both labeled and unlabeled data to classify unseen instances. Due to the large number of unlabeled data typically available, SSC algorithms must be able to handle large-scale data sets. Recently, various ensemble algorithms have been introduced with improved generalization performance when compared to single classifiers. However, existing ensemble methods are not able to handle typical large-scale data sets. We propose efficient cluster-based boosting (ECB), a multiclass SSC algorithm with cluster-based regularization that avoids generating decision boundaries in high-density regions. A semisupervised selection procedure reduces time and space complexities by selecting only the most informative unlabeled instances for the training of each base learner. We provide evidences to demonstrate that ECB is able to achieve good performance with small amounts of selected data and a relatively small number of base learners. Our experiments confirmed that ECB scales to large data sets while delivering comparable generalization to state-of-the-art methods.

Mastitis is a disease that is considered an obstacle in dairy farming. Some methods of diagnosing mastitis have been used effectively over the years, but with an associated relative cost that reduces the producer’s profit. In this context, this sector needs tools that offer an early, safe, and non-invasive diagnosis and that direct the producer to apply resources to confirm the clinical picture, minimizing the cost of monitoring the herd. The objective of this study was to develop a predictive methodology based on sequential knowledge transfer for the automatic detection of bovine subclinical mastitis using computer vision. The image bank used in this research consisted of 165 images, each with a resolution of 360 × 360 pixels, sourced from a database of 55 animals diagnosed with subclinical mastitis, all of which were not exhibiting clinical symptoms at the time of imaging. The images utilized in the sequential learning transfer were those of MammoTherm, which is used for the detection of breast cancer in women. The optimized model demonstrated the most optimal network performance, achieving 92.1% accuracy, in comparison to the model with manual search (86.1%). The proposed predictive methodologies, based on knowledge transfer, were effective in accurately classifying the images. This significantly enhanced the automatic detection of both healthy animals and those diagnosed with subclinical mastitis using thermal images of the udders of dairy cows.