Constant monitoring is necessary for powdery mildew prevention in field crops because, as a fungal disease, it modifies the green pigments of the leaves and is responsible for production losses. Therefore, there is a need for solutions that assure early disease detection to realize proactive control and management of the disease. The methodology currently used for the identification of powdery mildew disease uses RGB leaf images to detect damage levels. In the early stage of the disease, no symptoms are visible, but this is a point at which the disease can be controlled before the symptoms appear. This study proposes the implementation of a support vector machine to identify powdery mildew on cucurbit plants using RGB images and color transformations. First, we use an image dataset that provides photos covering five growing seasons in different locations and under natural light conditions. Twenty-two texture descriptors using the gray-level co-occurrence matrix result are calculated as the main features. The proposed damage levels are ’healthy leaves’, ’leaves in the fungal germination phase’, ’leaves with first symptoms’, and ’diseased leaves’. The implementation reveals that the accuracy in the L * a * b color space is higher than that when using the combined components, with an accuracy value of 94% and kappa Cohen of 0.7638.

Currently, there are significant challenges in the classification, recognition, and detection of fruits and vegetables. An important step to solve this problem is to obtain an accurate segmentation of the object of interest. However, the background and object separation in a gray image shows high errors for some thresholding techniques due to uneven or poorly conditioned lighting. An accepted strategy to decrease the error segmentation is to select the channel of a RGB image with high contrast. This paper presents the results of an experimental procedure based on a binary segmentation enhancement by using the Otsu method. The procedure was carried out with images of real agricultural products with and without additional noise to corroborate the robustness of the proposed strategy. The experimental tests were performed by using our database of RGB images of agricultural products under uncontrolled illumination. The results exhibit that the best segmentation is based on the selection of the Blue channel of the RGB test images due to its better contrast. Here, the quantitative results are measured by applying the Jaccard and Dice metrics based on the ground-truth images as optimal reference. Most of the results show an average percentage improvement difference greater than 45.5% in two experimental tests.

Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.

In this work, the electrochemical nucleation and growth mechanism of cobalt onto a polycrystalline silver electrode was investigated to analyze its initial stages of deposition in aqueous media. Electrodeposition was carried out using an aqueous solution containing 0.01 M CoSO₄ and 1 M Na₂SO₄ at pH 7.0 and 25 °C. Cyclic voltammetry study revealed that cobalt deposition is an irreversible process controlled by Co²⁺ mass transport. Current density–time transients obtained from potentiostatic steps were analyzed using the Scharifker–Hills and Hermann–Tarallo models. The results showed that electrodeposition follows a three-dimensional progressive nucleation mechanism with diffusion-controlled growth, in which active sites are continuously created during the process. Kinetic parameters, such as the nucleation rate, density of active sites, and saturation density, increased markedly at more negative cathodic potentials. From the transient analysis, the Co²⁺ diffusion coefficient was calculated as 3.3 × 10⁻⁶ cm² s⁻¹. Furthermore, analysis based on atomistic nucleation theory yielded a critical nucleus size of zero and a Gibbs free-energy barrier of 1.54 × 10⁻²⁰ J nucleus⁻¹, suggesting favorable thermodynamic conditions for cobalt nucleation on polycrystalline silver.

This work consists of the design and implementation of a TENS electrostimulator (Transcutaneous Electrical Nerve Stimulation), which generates its signal with an oscillator created from a NE555 Timer, in which, its shape and signal amplitude can be varied from potentiometers, in order to also modify the frequency at which it will work and which is connected to a power stage with a tip31C transistor, in addition to having its own power source, which connects directly to the alternating current at home, since it has its respective transformers and diode bridge to rectify the signal, also integrating voltage regulators to avoid noise and distortions of the signal, and thus be able to generate electrical impulses that can be used in the human body for therapeutic use. Obtaining an electrostimulator that generates an electrical impulse signal for the use of electrotherapy with a preliminary established frequency of 100Hz.

This data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo), a native species from the arid and semi-arid regions of Southern Zacatecas, Mexico. In particular, Stenocereus spp. are important cacti in the region due to its nutritional properties, role as an economic resource, and cultural significance.It is worth emphasising that these cacti traditionally grow wild (i.e., without deliberate cultivation); accordingly, controlled cultivation is uncommon and remains understudied. With the aim of producing a formal, comprehensive analysis and compendium, the data were collected across multiple phenological stages to provide a complete representation of the plant development cycle, from vegetative growth through to fruiting. To achieve this, the collection process combined high-resolution multispectral imaging with field spectrometry in the 400–700 nm range. Standardized acquisition protocols were applied in field conditions to capture consistent reflectance data, and environmental variables such as illumination, temperature, and geographic coordinates were recorded for each session to ensure reproducibility.