The use of recurrent neural networks has proven effective in time series prediction tasks such as weather. However, their use in resource-limited systems such as MCUs presents difficulties in terms of both size and stability with longer prediction windows. In this context, we propose a variant of the LSTM model, which we call SE-LSTM (Single Embedding LSTM), which uses embedding techniques to vectorially represent seasonality and latent patterns through variables such as temperature and humidity. The proposal is systematically compared in two parts: The first compares it against other reference architectures such as CNN-LSTM, TCN, LMU, and TPA-LSTM. The second stage, which includes implementation, compares it against the CNN-LSTM, LSTM, and TCN networks. Metrics such as the MAE and MSE are used along with the network weight, a crucial aspect for MCUs such as an ESP32 or Raspberry Pi Pico. An analysis of the memory usage, energy consumption, and generalization across different regions is also included. The results show that the use of embedding optimizes the network space without sacrificing the performance, which is crucial for edge computing. This research is part of a larger project, which focuses on improving agricultural monitoring systems.

This paper presents a methodology for detecting the pest Aleurothrixus floccosus in citrus crops in Pedregal de Arequipa, Peru. The study employs simple random sampling during image collection to minimize bias, alternating and extracting leaves from different citrus trees. Image processing techniques, including noise reduction, edge smoothing, and segmentation, are applied for pest detection. Machine learning algorithms are used to classify the images, culminating in a robust detection methodology. A dataset of 1200 images was analyzed during the study.

This study aims to detect pathologies in constructions made of sillar, a volcanic material of great historical and cultural value, commonly used in residential and heritage buildings, in the city of Arequipa, Peru. Due to the uniqueness of sillar and the particular characteristics of its pathologies, such as moisture and surface wear, a non-invasive methodology using digital images is proposed, oriented toward the analysis of heritage constructions, with the objective of developing a method that does not alter or modify the heritage or damage the structure, considering that in invasive studies, sample collection may affect the integrity of the material. The proposed strategy combines computer vision techniques, including clustering methods for preliminary segmentation, with the use of deep neural networks for anomaly and deterioration detection. Furthermore, a validation scheme is introduced that integrates standard segmentation metrics with intersection analysis relative to pathology maps, allowing computational analysis to align more closely with the criteria employed in architectural conservation. The results demonstrate good performance in moisture detection, although with lower accuracy in identifying other types of deterioration, highlighting both the feasibility and the challenges of applying deep learning to sillar diagnostics and laying the groundwork for the development of digital tools that support the documentation and preservation of architectural heritage.

This study proposes a methodology based on Discrete Cosine Transform (DCT) coefficients and deep learning to identify steganographic algorithms (UERD, J-UNIWARD, and JMiPOD) and to classify distortion levels in images from the ALASKA2 dataset. For each image, 258,048 AC coefficients were extracted, and 9999 were selected using Mutual Information (MI), allocated equally across three frequency bands. Two representations were compared under identical architectures: the MI-selected coefficients and full spatial maps of the DCT coefficients. Four architectures (CNN-1D, CNN 2D, CNN 2D + CBAM, and Extended Deep CNN) were trained with three random seeds. Spatial maps outperformed the MI-selected coefficients for every architecture, by 11.7 percentage points in algorithm identification (CNN 2D) and by 1.0 to 2.1 points in distortion-level classification. The best results were obtained by 2D convolutional networks on spatial maps: 59.2 ± 0.2% accuracy for algorithm identification (CNN 2D + CBAM, 0.8 points above the same network without attention) and 66.5 ± 0.5% for distortion levels (CNN 2D). A network with about eleven times more parameters did not improve the results. Distinguishing among the considered algorithms and distortion levels remains challenging.

The Historic Centre of Arequipa, a UNESCO World Heritage Site, contains buildings constructed mainly of sillar, a white volcanic ignimbrite, whose ornamented façades are time-consuming to document. This study evaluates whether image-to-image translation models can convert façade photographs into two-dimensional façade representations. A dataset of 578 heritage façades was collected through field photography and Google Street View, preprocessed through obstacle removal, homography-based rectification, and stitching, and paired with reference representations produced with generative tools and corrected by architects. Three models (Pix2Pix, Pix2PixHD, and Contrastive Unpaired Translation, CUT) were trained and evaluated on an independent test set of 66 façades using the mean absolute error (MAE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). Pix2Pix obtained the best scores (MAE = 7.24, SSIM = 0.768, PSNR = 22.90 dB), followed closely by Pix2PixHD, whereas CUT performed worse (MAE = 20.80, SSIM = 0.418, PSNR = 16.04 dB). An exploratory assessment of three buildings showed that 50–58% of the architectural components were successfully generated. The models reproduced the overall façade layout but not complex ornamentation. However, the limited availability of heritage-building data restricts the evaluation of model generalization.

The purpose of this article is to study a proposal for image processing and machine learning techniques to detect the pest Aleurothrixus floccosus in citrus in the area of Pedregal de Arequipa, Peru. Thus, in the collection of images a type of simple random sampling is adopted for the decrease of the bias in different citrus trees alternating and extracting leaves from the plantation also applying image processing techniques for noise reduction and edge smoothing, segmentation for the detection of the target and using machine learning algorithms that aims to perform the classification of the images, a methodology for the detection of the white fly or Aleurothrixus floccosus is achieved. It is necessary to understand that in the development of the article, it was possible to identify three stages of the pest in the plant, these being the presence of yellow areas, leaf rolling, and secretion of melase, likewise in the process it was possible to obtain a sample of 1200 images.