On-farm storage loss estimates of maize in Kenya using community survey methods

Hugo De Groote Anani Bruce (2023, [Artículo])

Maize is the most important staple in sub-Saharan Africa (SSA), with highly seasonal production. High storage losses affect food security, but good estimations are lacking. A new method using focus group discussions (FGDs) was tested with 121 communities (1439 farmers, 52% women) in Kenya's six maize-growing zones, to estimate the maize losses to storage pests and analyze farmer practices. As control strategies, half of the farmers used chemical pesticides (49%), while hermetic bags (16%) and botanicals (15%) were also popular. Relative loss from weevils in the long rains was estimated at 23%, in the short rains 18%, and annually 21%. Fewer farmers were affected by the larger grain borer (LGB) than by maize weevils: 42% in the long rainy season and 32% in the short rainy season; losses from LGB were also smaller: 19% in the long season, 17% in the short season, and 18% over the year. Total storage loss, from both species combined, was estimated at 36%, or 671,000 tonnes per year. The greatest losses occur in the humid areas, especially the moist mid-altitudes (56%), and with smaller loss in the drylands (20–23%). Extrapolating the point data and overlaying with the maize production map shows the geographic distribution of the losses, with the most important area found around Lake Victoria. FGDs provide convenient and cheap tools to estimate storage losses in representative communities, but a total loss estimate of 36% is higher than is found in other studies, so its accuracy and framing effects need to be assessed. We conclude that storage pests remain a major problem, especially in western Kenya, and that the use of environmentally friendly technologies such as hermetic storage and botanicals needs more attention, both by the public extension service and private agrodealers.

Larger Grain Borer Maize Weevil CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE STORAGE LOSSES PESTS SURVEY METHODS

MLN disease diagnostics, MLN disease-free seed production and MLN disease management

Suresh L.M. (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA YIELD LOSSES DISEASES MAIZE PHENOTYPING GERMPLASM SYMPTOMS ECONOMIC ASPECTS

El envejecimiento en las enfermedades neurológicas y psiquiátricas

MARISOL LOPEZ LOPEZ NANCY MONROY JARAMILLO ALBERTO ORTEGA VAZQUEZ ERNESTO SOTO REYES SOLIS (2023, [Libro])

Es un placer presentar el libro "El envejecimiento en las enfermedades neurológicas y psiquiátricas" escrito por un grupo de autores destacados y expertos en diversas áreas del conocimiento. Cada capítulo ha sido cuidadosamente elaborado para ofrecer una visión actualizada y rigurosa de los temas abordados, en los cuales se ha integrado una amplia gama de enfoques interdisciplinarios y de técnicas de investigación. Este libro se enfoca en el tema del envejecimiento desde varios vértices relacionados con las enfermedades neurológicas y psiquiátricas. A través de sus trece capítulos, los autores discutes detalladamente la relación del envejecimiento con la enfermedad del Alzheimer, la enfermedad de Parkinson, la enfermedad de Huntington, la esquizofrenia, el trastorno bipolar, la epilepsia y otras enfermedades neurológicas que impactan en la salud

MEDICINA Y CIENCIAS DE LA SALUD Vejez y salud mental - México Envejecimiento - Aspectos sociales - México Enfermedades mentales - México Enfermedades de los ancianos -aspectos sociales - México

MLN disease diagnostics, MLN disease-free seed production and MLN disease management

Suresh L.M. (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DISEASES DISEASE MANAGEMENT SEED PRODUCTION MAIZE NECROSIS YIELD LOSSES ECONOMIC IMPACT SURVEILLANCE SYSTEMS TRAINING

Agricultural value chains in Sudan: an annotated bibliography

Hugo De Groote Paswel Marenya (2023, [Libro])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURAL VALUE CHAINS CASH CROPS FOOD CROPS LIVESTOCK SOCIOECONOMIC ASPECTS GENDER SOCIAL INCLUSION POSTHARVEST LOSSES BIBLIOGRAPHIES

Identificación de acciones de restauración hidrológico forestal en cuencas

PEDRO RIVERA RUIZ HECTOR GREGORIO CORTES TORRES (2008, [Documento de trabajo])

Se presentan los resultados obtenidos en la cuenca del río Huixtla, en la costa de Chiapas, con la restauración hidrológico forestal de la zona. Se utilizó una metodología generada en la Universidad Politécnica de Madrid, la cual se basa en dos ejes: uno, se realiza un estudio descriptivo de la cuenca; y el segundo es un estudio y análisis de los datos pluviométricos de la zona.

Cuencas Conservación del agua Reforestación Costa de Chiapas INGENIERÍA Y TECNOLOGÍA

Fabricación de nanopartículas de oro dentro de óxido de aluminio (γ-Al2O3) nanoestructurado con cavidades esféricas

Manufacturing of Gold Nanoparticles within Nanostructured Aluminum Oxide (γ-Al2O3) with Spherical Cavities

Mariela de Jesús Franco Gallegos (2023, [Tesis de maestría])

Los catalizadores basados en nanopartículas de oro han generado gran interés, gracias a su capacidad de ser selectivos en la promoción de reacciones específicas o en la producción de productos deseados, minimizando la formación de productos secundarios no deseados; sus propiedades electrónicas únicas; y su utilización bajo condiciones ambientales. Sin embargo, la desventaja principal de los catalizadores de oro es la sinterización de las nanopartículas debido a su baja temperatura de fusión, lo que provoca la pérdida de actividad catalítica y la desactivación del catalizador. Una de lassoluciones que ofrece el uso de la nanociencia y la nanotecnología es la utilización de soportes nanoestructurados que den mejor estabilidad a las nanopartículas y las protejan de la desactivación. En este trabajo se sintetizaron catalizadores basados en nanopartículas de oro soportados y encapsulados en alúmina macroporosa, por un método de impregnación húmeda asistida por ultrasonido; un método sencillo, rápido y ecológico. El desempeño catalítico de materiales sintetizados se analizó mediante espectroscopía UV-Visible in-situ en la reducción de 4-Nitrofenol a 4-Aminofenol. Así mismo, se presentan las caracterizaciones por TEM, SEM, FT-IR, espectroscopía UV-Visible, y XRD de catalizadores obtenidos. Se obtuvieron catalizadores altamente activos con alto rendimiento gracias al uso de un soporte nanoestructurado.

Catalysts -based on gold nanoparticles have recently gained interest due to their ability to selectively promote specific catalytic reactions or produce desired products, while minimizing the formation of unwanted byproducts, their unique electronic properties, and their utilization under ambient conditions. However, the main drawback of gold catalysts is the sintering of nanoparticles due to their low melting temperature, which leads to loss of catalytic activity and catalyst deactivation. One of the solutions offered by nanoscience and nanotechnology is the use of nanostructured supports that provide better stability to the nanoparticles and protect them from deactivation. In this work, gold nanoparticle-based catalysts supported and encapsulated in macroporous alumina were synthesized using a simple, fast, and eco-friendly method of ultrasound-assisted wet impregnation. The catalytic performance of synthetized materials was evaluated by in-situ UV-Visible spectroscopy in the reduction of 4-Nitrophenol to 4-Aminophenol. In addition, their characterization by TEM, SEM, FT-IR, UV Visible spectroscopy and XRD are presented. Highly active catalysts with high performance were obtained thanks to the use of a nanostructured supports.

nanopartículas de oro, alúmina macroporosa, impregnación, reducción 4-NF gold nanoparticles, macroporous alumina, impregnation, 4-NF reduction INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE MATERIALES PROPIEDADES DE LOS MATERIALES PROPIEDADES DE LOS MATERIALES

REAL TIME EMBBEDED RGB-D SLAM USING CNNS FOR DEPTH ESTIMATION AND FEATURE EXTRACTION

Marcos Renato Rocha Hernández (2023, [Tesis de maestría])

"A robust and efficient Simultaneous Localization and Mapping (SLAM) system is essential for intelligent mobile robots to work in unknown environments. For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically de signed in most cases, and can be vulnerable in complex environments. Also, most of the most robust SLAM algorithms rely on special devices like a stereo camera or depth sensors, which can be expensive and give more complexity to the system, that is why monocular depth estimation is an essential task in the computer vision community. This work shows that feature extraction and depth estimation using a monocular camera with deep convolutional neural networks (CNNs) can be incorporated into a modern SLAM framework. The proposed SLAM system utilizes two CNNs, one to detect keypoints in each im age frame, and to give not only keypoint descriptors, but also a global descriptor of the whole image and the second one to make depth estimations from a single image frame, all using only a monocular camera."

SLAM Inteligencia Artificial CNN Sistemas embebidos Redes neuronales Cámara monocular INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ORDENADORES INTELIGENCIA ARTIFICIAL INTELIGENCIA ARTIFICIAL

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