Búsqueda avanzada


Área de conocimiento




7 resultados, página 1 de 1

A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm.

Ali Mirzazadeh Afshin Azizi Yousef Abbaspour_Gilandeh José Luis Hernández-Hernández Mario Hernández Hernández Iván Gallardo Bernal (2021, [Artículo])

Estimation of crop damage plays a vital role in the management of fields in the agricultura sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds¿ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of Deep learning-based models to classify other damaged crops.

rapeseed classification damaged crops deep neural networks INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS

Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting.

Sajad Sabzi Razieh Pourdarbani Mohammad Hossein Rohban Alejandro Fuentes_Penna José Luis Hernández-Hernández Mario Hernández Hernández (2021, [Artículo])

Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network¿imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network¿harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the Knearest- neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network¿biogeography-based optimization (ANNBBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.

artificial neural network cucumber hyperspectral imaging majority voting nitrogen INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS

Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions.

Razieh Pourdarbani Sajad Sabzi Mario Hernández Hernández José Luis Hernández-Hernández Ginés García_Mateos Davood Kalantari José Miguel Molina Martínez (2019, [Artículo])

Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most e

ective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods.

remote sensing in agriculture artificial neural network hybridization environmental conditions majority voting plum segmentation INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS

In vitro Digestibility of Yarrowia lipolytica Yeast and Growth Performance in Whiteleg Shrimp Litopenaeus vannamei

ANA RUTH ALVAREZ SANCHEZ CLAUDIO HUMBERTO MEJIA RUIZ Héctor Gerardo Nolasco Soria Alberto Peña Rodríguez (2018, [Artículo])

"Marine yeasts used in aquaculture disease control can also be an important protein source for improving feeding and nutrition of crustaceans. Yarrowia lipolyticca has been studied for its capacity to secrete heterologous proteins and high content of unsaturated fatty acids, beta-glucan, and mannane polymers in the cell wall. We measured in vitro digestibility of Y. lipolyticca by whiteleg shrimp Litopenaeus vannamei digestive enzymes, and an in vivo assay of Y. lipolytica in feed onwhiteleg shrimp growth. We found that digestive gland enzymes of shrimp digest Y. lipolytica, based on reduced optical density of a yeast suspension. Digestion was –0.00236 ± 0.00010 OD U min–1 for intact cells and –0.00325 ± 0.00010 OD U min–1 for lysed cells. Release of reducing sugars in intact cells (5.3940 ± 0.1713 μmol h–1), and lysed cells (0.8396 ± 0.2251 μmol h–1) was measured. Digestive gland treatment significantly reduced cell viability (near 100%), relative to the control. Electron microscopy shows that the cell wall of Y. lipolytica exposed to the digestive gland enzymes was severely damaged. Shrimp diet containing Y. lipolytica resulted in significantly higher weight gain and specific growth rate of whiteleg shrimp."

Marine yeast, cell digestibility, cell viability, turbidimetry, reduced sugars BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOQUÍMICA BIOQUÍMICA DE ALIMENTOS BIOQUÍMICA DE ALIMENTOS

Indicadores industriales en el uso del agua: industria alimentaria

RAMON LOPEZ HERNANDEZ (2001, [Libro])

Tabla de contenido: 1. Industria alimentaria -- 2. Proceso de fabricación -- 3. Control de la contaminación -- 4. Parámetros e índices específicos de uso del agua -- 5. Conclusiones -- 6. Bibliografía.

1. Industria alimentaria -- 2. Proceso de fabricación -- 3. Control de la contaminación -- 4. Parámetros e índices específicos de uso del agua -- 5. Conclusiones -- 6. Bibliografía.

Industria de alimentos Consumo industrial de agua Usos del agua Indicadores industrailes CIENCIAS SOCIALES

Microbiological Analysis of the Air in a Popular Fish Processing and Marketing Area

Angélica Sinaí Quintanilla Martínez Lizet Aguirre Güitrón Luis Daniel Espinosa Chaurand MAYRA DIAZ RAMIREZ ALEJANDRO DE JESUS CORTES SANCHEZ (2022, [Artículo])

"Fish are marketed as a food and consumed worldwide. During the production of food, contamination by microorganisms is possible through the air, soil, water, surfaces, food handlers, etc. The air does not have a natural microbial composition, but it is a vehicle for the transmission of microorganisms of economic and health interest because they are associated with food spoilage and human diseases. The objective of this study was the microbiological analysis of the air in an area popular for the processing and marketing of fish products in the city of Tepic Nayarit. Using the passive or sedimentation method to collect microorganisms present in the air, the proportion of aerobic mesophile bacteria, coliform bacteria, fungi and yeast was determined at different locations in the fish processing and marketing area for four weeks. The results indicated that the aerobic mesophiles had the highest counts among all the microbial groups analyzed at the twelve different sampling points during the four weeks of the study; their numbers ranged from 2.44 to 2.95 log CFU/m3/h, followed by molds with counts from 1.44 to 2.75 log CFU/m3/h, yeasts with counts from 0.7 to 2.01 log CFU/m3/h and coliforms with counts that ranged from 0.7 to 1.68 log CFU/m3/h. We determined the proportion of the viable microbiological population present in the air at the different sampling points of the study area; several of these sampling points presented values above those recommended by various agencies around the world. Knowledge of the biological hazards transported through the air is important to establish and reduce the risk to the health of occupants and the contamination pathways of processed and marketed fishery products that may be associated with spoilage and foodborne diseases."

food safety, food quality, air pollution, airborne biohazard, environmental monitoring INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ALIMENTOS HIGIENE DE LOS ALIMENTOS HIGIENE DE LOS ALIMENTOS

Factores que contribuyen al consumo de ultraprocesados en los hogares vulnerables con jefatura femenina de Cuautla, Morelos

MÓNICA VÁZQUEZ ARELLANO (2023, [Tesis de maestría])

El objetivo de la presente investigación es analizar cuáles son los principales factores que contribuyen a promover el consumo de alimentos y bebidas ultraprocesadas (AYBUPs) en hogares vulnerables con jefatura femenina. Para tal fin, se utilizó un enfoque cualitativo basado en el estudio de caso. Las técnicas utilizadas fueron la entrevista semiestructurada y la observación. La investigación se realizó en el municipio de Cuautla. Se examinaron tres factores que inciden en las

elecciones alimentarias de las jefas de familia, a saber: sociodemográficos, económicos y socioculturales. Los resultados indican que la población entrevistada vive en zona de pobreza y marginación social, los problemas principales son la escasez de agua y vivienda adecuada. La mayoría tienen empleos en el sector informal y no cuentan prestaciones sociales. El ingreso familiar está conformado por varias personas, sin embargo, la sobrecarga de trabajo recae principalmente sobre la jefa de familia. Todas las familias presentan miembros con enfermedades crónicas. Su principal criterio de compra es que los alimentos sean baratos. Los ultraprocesados han penetrado en el ámbito de la cultural alimentaria pues su consumo acontece tanto en lo cotidiano como el festivo. La alimentación de estas familias no se centra en los alimentos ultraprocesados aunque algunos se consumen cotidianamente. Además, se observa una tendencia a su incremento pues las mujeres han ido sustituyendo ingredientes o alimentos caseros por el uso de ultraprocesados, este fenómeno se exacerbó en el periodo del confinamiento. Se concluye que los hogares liderados por mujeres son más vulnerables a la compra de alimentos ultraprocesados pues habitan ambientes alimentarios en los que predominan estos alimentos que son de fácil preparación, baratos y que tienen al alcance, este fenómeno puede ser interpretado como un indicador de que en esos hogares se padecen inseguridad alimentaria.

CIENCIAS SOCIALES SOCIOLOGÍA alimentos ultraprocesados, seguridad alimentaria, mujeres trabajadoras, alimentación en el contexto urbano, determinantes sociales de la salud.