Accumulation of wheat phenolic acids under different nitrogen rates and growing environments

Wenfei Tian Yong Zhang Zhonghu He (2022, [Artículo])

Functional Wheat Trans-Ferulic Acid Nitrogen Management Environment Interaction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PHENOLIC ACIDS NITROGEN ENVIRONMENT ANTIOXIDANTS

Crop performance and nitrogen use-efficiency in maize under conservation agriculture coupled with sub-surface drip fertigation

C.M. Parihar Hari Sankar Nayak Renu Pandey ML JAT (2021, [Artículo])

Biological Yield Permanent Beds Yield Attributes CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA YIELDS NITROGEN NUTRIENT UPTAKE CROP PERFORMANCE MAIZE

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

Análisis de la dinámica del monzón de Norteamérica usando modelos globales y regionales

SALVADOR CASTILLO LIÑAN (2021, [Tesis de maestría])

Maestro en Ciencias y Tecnología del Agua - Hidrometeorología) -- Instituto Mexicano de Tecnología del Agua. Coordinación de Desarrollo Profesional e Institucional. Subcoordinación de Posgrado.

El Monzón de Norteamérica (NAM) es un sistema atmosférico intraestacional causante de aproximadamente el 70% de las precipitaciones anuales en el noroeste de México y suroeste de Estados Unidos. Su estudio utilizando modelos numéricos es un reto debido a la compleja dinámica asociada a la abrupta orografía y al contraste térmico océano-continente que contribuyen a su desarrollo durante el verano. A pesar de que la gran mayoría de los modelos globales del experimento CMIP5 (Proyecto de Intercomparación de Modelos Acoplados), logran describir el periodo intraestacional de precipitaciones máximas sobre el dominio del NAM y reproducir su variabilidad espacial y temporal, se han identificado sesgos en las simulaciones con respecto a las observaciones y los datos de Reanálisis. Con el propósito de abordar estos sesgos, así como identificar y explicar el inicio-final del monzón, en este estudio se analiza el papel de los mecanismos entre la atmósfera, del continente y el océano, utilizando simulaciones numéricas regionales generadas con el modelo sueco RCA4 (Rossby Centre regional atmospheric model 4), el cual fue forzado con 10 modelos globales del CMIP5.

Monzón de Norteamérica Modelación numérica Precipitaciones INGENIERÍA Y TECNOLOGÍA

Genotype by Environment Interaction (GEI) of maize varieties resistant to maize lethal necrosis for Mid Altutides of Rwanda

Claver Ngaboyisonga Suresh L.M. (2022, [Artículo])

Additive Main Effect and Multiplicative Interaction Area Under Disease Progress Curve Maize Lethal Necrosis Variety x Environment Interaction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE CHLOROTIC MOTTLE VIRUS RESISTANCE VARIETIES INOCULATION DISEASE MANAGEMENT SUGARCANE MOSAIC VIRUS

Tutoring to novel teachers. A fragment of skylight from the border of Ciudad Juarez

Silvia Gabriela Alvídrez Minora (2022, [Artículo, Artículo])

This article describes and interprets the tutoring of novice teachers in a preschool area of ​​Ciudad Juarez. The study is situated in the interpretive paradigm; It makes use of the ethnographic method and research techniques such as participant observation and interview. The partial results show that tutoring is considered an important process for the insertion and professionalization of new teachers; however, it is mentioned that this enriching relationship is only possible when there is communication between all the people involved in the tutoring, in addition to being open to peer learning.

Teacher training Novice teachers Tutoring Ciudad Juarez Formación docente Tutoría Ciudad Juárez docentes nóveles CIENCIAS SOCIALES CIENCIAS SOCIALES

Discovery of genomic regions associated with grain yield and agronomic traits in Bi-parental populations of maize (Zea mays. L) under optimum and low nitrogen conditions

Noel Ndlovu Vijay Chaikam Berhanu Tadesse Ertiro Biswanath Das Yoseph Beyene Charles Spillane Prasanna Boddupalli Manje Gowda (2023, [Artículo])

Grain Yield Low Soil Nitrogen CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GRAIN NITROGEN SOIL CHEMICOPHYSICAL PROPERTIES MAIZE QUANTITATIVE TRAIT LOCI

Como las redes sociales contribuyen al conocimiento de la sistemática de plantas

RODRIGO STEFANO DUNO GERMAN CARNEVALI FERNANDEZ CONCHA (2022, [Artículo])

La ciencia y la sistemática, en particular, se han beneficiado enormemente de la tecnología. Pregúntele a cualquier amante de la naturaleza y de la fotografía, el cambio que significó la llegada de las cámaras digitales en sustitución de las cámaras convencionales. Hoy deseamos destacar otra novedad tecnológica, no menos tangible, y de enorme repercusión mundial; las redes sociales. En este breve texto presentaremos casos concretos de cómo las redes sociales pueden ser una herramienta fundamental para incrementar el conocimiento de la diversidad biológica.

APOCYNACEAE DROSERACEAE FACEBOOK NATURALISTA NOVEDADES TAXONOMICAS REDES SOCIALES BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

Biofortification to avoid malnutrition in humans in a changing climate: Enhancing micronutrient bioavailability in seed, tuber, and storage roots

Ana Luisa Garcia-Oliveira Mahalingam Govindaraj Rodomiro Ortiz (2023, [Artículo])

Bioaccessibility and Absorption Biofortified Crop Cultivars Genes and Genetic Markers Nutrient Acquisition Transport and Storage CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BIOAVAILABILITY ABSORPTION CLIMATE CHANGE GENETIC MARKERS GENETIC ENGINEERING NUTRIENTS TRANSPORT STORAGE

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