This research tested the impact on the soil when implementing a Clean Intensive Livestock Production System (CILPS) via Voisin Rational Grazing (VRG). The methodology consists of the characterization of soil-grass-cattle management, including the soil chemical and biological analysis in the case study farm and reference land (comparison between extensive and CILPS-VRG systems). In addition, the VRG impact on agroecological management was evaluated by calculating the Soil Organic Matter Indicator (SOMI). Once the samples were processed, the T-Student test was applied, verifying the homogeneity of variances (F-Fischer) for the chemical variables and the Kruskal-Wallis test for the biological variables. The results identified substantial differences in some edaphic parameters such as organic matter, percentage of organic carbon, nitrogen, sulfur, and calcium. Moreover, to diversify nematodes and differences in the Rhabdhitids sp., the process characterized products from the natural organic fertilization in the soil with VRG. Finally, the calculation of the SOMI with a value of 41.6 for VRG indicates soil with no alteration or natural balance.
In Colombia, agricultural exports have become notoriously prevalent in recent years, causing the creation of new methods capable of increasing production in order to meet the global demands. A very efficient option is the use of greenhouses, given their low building cost, ease of construction, ability to protect crops from natural phenomena and plagues, and the possibility to keep the internal temperature steady during day and night, thus allowing crops to grow fast and healthy. Nowadays, advancements in electronics have allowed boosting the positive effects of these environments, which is why this document introduces a procedure for the implementation of an automated pyramid-type greenhouse, utilizing techniques related to Precision Agriculture (PA) and based on concepts related to the Internet of Things (IoT) for remote monitoring through emerging communication technologies such as the NFRL2401 cards and the Arduino Nano and Mega boards. Inside the greenhouse, variables such as temperature and ambient humidity are measured and controlled via the PCE-P30U Universal Input Signal Converter Data Logger, while ground humidity is monitored by ZD510 capacitive sensors. Outside, variables such as temperature, ambient humidity, negative and positive pressure, and wind speed are measured. Data obtained is taken wirelessly to the server using Windows Server 2019 Datacenter, with Broker MQTT EMQ-X services and MYSQL databases, providing a suitable and efficient environment for agricultural research processes. With the procedure developed in this document, a baseline is proposed for the implementation of a smart greenhouse that can be replicated and used as a test system for smart sowing processes, adapting to the different climate and production conditions of the country.
Los sistemas de instrumentación deben responder a necesidades reales del entorno y deben presentar una información confiable de las medidas realizadas, es por esto que los sistemas requieren de elementos que puedan ser reconfigurados a las necesidades de cada entorno, permitiendo mejorar la precisión del sistema. Este trabajo muestra una adaptación de tecnologías de diferente naturaleza para la realización de una conexión full dúplex con una estación de medida de condiciones climatológicas adaptadas al contexto.
The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased.
Este documento presenta la evaluación de un método de clasificación de fallas en productos terminados utilizando la combinación de descriptores de color, forma y textura. Se utiliza una Máquina de Vectores de Soporte multiclase (SVM-Support Vector Machine) y se construye una base de datos anotada capturando botellas de plástico con 11 situaciones de fabricación entre botellas en buen estado y botellas con imperfectos como rasgaduras, golpes, hendiduras, etc; bajo diferentes condiciones no controladas (ruido, iluminación, escala, entre otras). La etapa de fusión propone una combinación lineal de características y para calcular el desempeño de descriptores y fusión de datos, se utilizó una metodología de validación cruzada aplicando el método de Montecarlo. La configuración de SVM utiliza la metodología multiclase “One-vs-All” con Kernel Radial Gaussiano. La detección se realiza inicialmente aplicando descriptores individuales y combinados.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.