A computer vision system for the automatic classification of five varieties of tree leaf images

Producción CientíficaA computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid ANN–BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1–Cydonia oblonga (quince) 0.991 (ANN–ABC), 95.89% (ANN–ABC), 95.91% (ANN–ABC); 2–Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100% (LDA), 100% (LDA); 3–Malus pumila (apple) 0.996 (LDA), 96.63% (LDA), 94.99% (LDA); 4–Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71% (LDA), 82.57% (LDA); and 5–Prunus armeniaca (apricot) 0.994 (LDA), 88.67% (LDA), 94.65% (LDA), respectively.Unión Europea (project 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP

Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

Producción CientíficaNondestructive estimation of fruit properties during their ripening stages ensures the best value for producers and vendors. Among common quality measurement methods, spectroscopy is popular and enables physicochemical properties to be nondestructively estimated. The current study aims to nondestructively predict tissue firmness (kgf/cm), acidity (pH level) and starch content index (%) in apples (Malus M. pumila) samples (Fuji var.) at various ripening stages using visible/near infrared (Vis-NIR) spectral data in 400–1000 nm wavelength range. Results show that non-linear regression done by an artificial neural network-cultural algorithm (ANN-CA) was able to properly estimate the investigated fruit properties. Moreover, the performance of the proposed method was evaluated for Vis-NIR data based on optimal NIR wavelength values selected by a genetic optimization tool.Agencia Estatal de Investigación - Fondo Europeo de Desarrollo Regional (project RTI2018-098958-B-I00

Supervised contrastive learning over prototype-label embeddings for network intrusion detection

Producción CientíficaContrastive learning makes it possible to establish similarities between samples by comparing their distances in an intermediate representation space (embedding space) and using loss functions designed to attract/repel similar/dissimilar samples. The distance comparison is based exclusively on the sample features. We propose a novel contrastive learning scheme by including the labels in the same embedding space as the features and performing the distance comparison between features and labels in this shared embedding space. Following this idea, the sample features should be close to its ground-truth (positive) label and away from the other labels (negative labels). This scheme allows to implement a supervised classification based on contrastive learning. Each embedded label will assume the role of a class prototype in embedding space, with sample features that share the label gathering around it. The aim is to separate the label prototypes while minimizing the distance between each prototype and its same-class samples. A novel set of loss functions is proposed with this objective. Loss minimization will drive the allocation of sample features and labels in embedding space. Loss functions and their associated training and prediction architectures are analyzed in detail, along with different strategies for label separation. The proposed scheme drastically reduces the number of pair-wise comparisons, thus improving model performance. In order to further reduce the number of pair-wise comparisons, this initial scheme is extended by replacing the set of negative labels by its best single representative: either the negative label nearest to the sample features or the centroid of the cluster of negative labels. This idea creates a new subset of models which are analyzed in detail. The outputs of the proposed models are the distances (in embedding space) between each sample and the label prototypes. These distances can be used to perform classification (minimum distance label), features dimensionality reduction (using the distances and the embeddings instead of the original features) and data visualization (with 2 or 3D embeddings). Although the proposed models are generic, their application and performance evaluation is done here for network intrusion detection, characterized by noisy and unbalanced labels and a challenging classification of the various types of attacks. Empirical results of the model applied to intrusion detection are presented in detail for two well-known intrusion detection datasets, and a thorough set of classification and clustering performance evaluation metrics are included.Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación - Fondo Europeo de Desarrollo Regional (grant RTI2018-098958-B-I00

A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties

Producción CientíficaSince different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.Unión Europea (project 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP

Identification of internal defects in potato using spectroscopy and computational intelligence based on majority voting techniques

Producción CientíficaPotatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.Ministerio de Ciencia, Innovación y Universidades; Ministerio de Ciencia e Innovación; Agencia Estatal de Investigación y Fondo Europeo de Desarrollo Regional (FEDER) - (grant RTI2018-098156-B-C53

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Producción CientíficaSite-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively

Caracterización conjunta del basamento en Hontomín (España) empleando datos sísmicos y microgravimétricos

9ª Asamblea Hispano Portuguesa de Geodesia y Geofísica: Madrid 28-30 de junio 2016 / Organizado por la Comisión Española de Geodesia y Geofísica ; Secçao Portuguesa das Unios Internacionais Astronomica e Geodésica ; Universidad Complutense de MadridInstitut de Ciéncies de la Terra Jaume Almera, EspañaDepartment of Geology and Petroleum Geology, University of Aberdeen, Reino UnidoDepartamento de Geología, Universidad de Salamanca, EspañaInstituto Geológico y Minero de España, EspañaPeer reviewe

Characterization of the Antitumor Potential of Extracts of Cannabis sativa Strains with High CBD Content in Human Neuroblastoma

Cannabis has been used for decades as a palliative therapy in the treatment of cancer. This is because of its beneficial effects on the pain and nausea that patients can experience as a result of chemo/radiotherapy. Tetrahydrocannabinol and cannabidiol are the main compounds present in Cannabis sativa, and both exert their actions through a receptor-mediated mechanism and through a non-receptor-mediated mechanism, which modulates the formation of reactive oxygen species. These oxidative stress conditions might trigger lipidic changes, which would compromise cell membrane stability and viability. In this sense, numerous pieces of evidence describe a potential antitumor effect of cannabinoid compounds in different types of cancer, although controversial results limit their implementation. In order to further investigate the possible mechanism involved in the antitumoral effects of cannabinoids, three extracts isolated from Cannabis sativa strains with high cannabidiol content were analyzed. Cell mortality, cytochrome c oxidase activity and the lipid composition of SH-SY5Y cells were determined in the absence and presence of specific cannabinoid ligands, with and without antioxidant pre-treatment. The cell mortality induced by the extracts in this study appeared to be related to the inhibition of the cytochrome c oxidase activity and to the THC concentration. This effect on cell viability was similar to that observed with the cannabinoid agonist WIN55,212-2. The effect was partially blocked by the selective CB1 antagonist AM281, and the antioxidant α-tocopherol. Moreover, certain membrane lipids were affected by the extracts, which demonstrated the importance of oxidative stress in the potential antitumoral effects of cannabinoids.This work has been partially supported by a grant from the Ministry of Economy and Competitiveness (DIN2019-010902 and DIN2020-011349) and the Basque Government Department of Economic Development, Sustainability and Environment (Bikaintek program: 005-B2/2021)

Risk Factors in Third and Fourth Degree Perineal Tears in Women in a Tertiary Centre: An Observational Ambispective Cohort Study

Objectives: To analyze the main risk factors associated with third and fourth degree postpartum perineal tears in women attended to in our obstetrics service. Methods: An observational, retrospective, hospital cohort study was carried out in women whose deliveries were attended to in the obstetrics service of the Hospital General Universitario Gregorio Marañón de Madrid (HGUGM), during the period from January 2010 to April 2017. Results: During the study period, a total of 33,026 patients were included in the study. For maternal variables, the associated increased risk of severe perineal tearing in nulliparous women is OR = 3.48, for induced labor OR = 1.29, and for instrumental delivery by forceps OR = 4.52 or spatulas OR = 4.35; for the obstetric variable of episiotomy, it is OR = 3.41. For the neonatal variables, the weight of the newborns has a directly proportional relationship with the risk of severe tears, and for birth weights of 3000 g (OR = 2.41), 3500 g (OR = 1.97), and 4000 g (OR = 2.17), statistically significant differences were found in each of the groups (p < 0.05). Conclusion: Primiparity, induction of labor, episiotomy, instrumental delivery with forceps or spatula, and a birth weight of 3000 g or more are significantly associated with an increased risk of third and fourth degree perineal tears