Supervised learning requires the accurate labeling of instances, usually provided by an expert. Crowdsourcing platforms offer a practical and cost-effective alternative for large datasets when individual annotation is impractical. In addition, these platforms gather labels from multiple labelers. Still, traditional multiple-annotator methods must account for the varying levels of expertise and the noise introduced by unreliable outputs, resulting in decreased performance. In addition, they assume a homogeneous behavior of the labelers across the input feature space, and independence constraints are imposed on outputs. We propose a Generalized Cross-Entropy-based framework using Chained Deep Learning (GCECDL) to code each annotator’s non-stationary patterns regarding the input space while preserving the inter-dependencies among experts through a chained deep learning approach. Experimental results devoted to multiple-annotator classification tasks on several well-known datasets demonstrate that our GCECDL can achieve robust predictive properties, outperforming state-of-the-art algorithms by combining the power of deep learning with a noise-robust loss function to deal with noisy labels. Moreover, network self-regularization is achieved by estimating each labeler’s reliability within the chained approach. Lastly, visual inspection and relevance analysis experiments are conducted to reveal the non-stationary coding of our method. In a nutshell, GCEDL weights reliable labelers as a function of each input sample and achieves suitable discrimination performance with preserved interpretability regarding each annotator’s trustworthiness estimation.

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets.

Addressing unemployment is essential for formulating effective public policies. In particular, socioeconomic and monetary variables serve as essential indicators for anticipating labor market trends, given their strong influence on employment dynamics and economic stability. However, effective unemployment rate prediction requires addressing the non-stationary and non-linear characteristics of labor data. Equally important is the preservation of interpretability in both samples and features to ensure that forecasts can meaningfully inform public decision-making. Here, we provide an explainable framework integrating unsupervised and supervised machine learning to enhance unemployment rate prediction and interpretability. Our approach is threefold: (i) we gather a dataset for Colombian unemployment rate prediction including monetary and socioeconomic variables. (ii) Then, we used a Local Biplot technique from the widely recognized Uniform Manifold Approximation and Projection (UMAP) method along with local affine transformations as an unsupervised representation of non-stationary and non-linear data patterns in a simplified and comprehensible manner. (iii) A Gaussian Processes regressor with kernel-based feature relevance analysis is coupled as a supervised counterpart for both unemployment rate prediction and input feature importance analysis. We demonstrated the effectiveness of our proposed approach through a series of experiments conducted on our customized database focused on unemployment indicators in Colombia. Furthermore, we carried out a comparative analysis between traditional statistical techniques and modern machine learning methods. The results revealed that our framework significantly enhances both clustering and predictive performance, while also emphasizing the importance of input samples and feature selection in driving accurate outcomes.

For precision agriculture (PA) applications that use aerial platforms, researchers are likely to be interested in extracting, study and understanding biophysical and structural properties in a spatio-temporal manner by using remotely sensed imagery to infer variations of vegetation biomass and/or plant vigor, irrigation strategies, nutrient use efficiency, stress, disease identification, among others. This requires measuring spectral responses of the crop at specific wavelengths by using, for instance, Vegetation Indices (VI). However, for the analysis of this spectral response and its heterogeneity and spatial variability, a large amount of aerial imagery (data) must be collected and processed using a photogrammetry software. Data extraction is often performed in a Geographic Information System (GIS) software and then analyzed using (in general) statistical software. On the one hand, a GIS is used for the collection of resources to manipulate, analyze, and display all forms of geographically referenced information. In this regard, Quantum GIS (QGIS) is one of the most well-known open-source software used which provides an integration of geoprocessing tools from a variety of different software libraries. QGIS is widely used to obtain VI computations through the raster calculator, although, this computation is performed with band rasters manually provided by the user; one by one, which is time-consuming. On the other hand, QGIS provides a Python interface to efficiently exploit the capabilities of a GIS to create similar plugins, but this can be a non-trivial task. In this work, we developed a specific and QGIS independent semi-automatic tool called ViCTool (Vegetation index Computation Tool) as a free open-source software (FOSS) for large amount of data extraction to derive VIs from aerial raster images in a certain region of interest. This tool has the option of extracting several multispectral and RGB VIs employing Blue, Green, Red, NIR, LWIR, or Red edge bands. The user must provide the input folder path containing one or more raster band folders, the shapefile with the regions of Interests, an output path to store the output VI rasters, and the file containing the VI computations. ViCTool was developed using Python PyQT for designing the User Interface (UI) and Python GDAL for raster processing to simplify and speed up the process of calculating a large amount of data intuitively.