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La tecnología CRISP hace el maiz más resistente |
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 El maíz es una de las principales fuentes de material alimentario y componentes nutricionales para la salud humana y los piensos para el ganado. Unos investigadores de la Unión Europea (UE) han acelerado la producción de líneas de maíz resistentes a la sequía empleando técnicas de modificación genética. Más información |
El sistema Greenhouse Models as a Service ayudará al agricultor en la toma de decisiones |
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El Grupo "Automática, Robótica y Mecatrónica TEP-197" de la Universidad de Almería ha desarrollado el sistema Greenhouse Models as a Service para ayudar al agricultor en la toma de decisiones en base a los datos disponibles en la nube y generados directamente en las instalaciones agrarias mediante estaciones de medición tipo IoT. más información |
Artículos de interés: visión e inteligencia artificial |
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A machine learning method to estimate reference evapotranspiration using soil moisture sensors.
One of the most important applications of remote imaging systems in agriculture, with the greatest impact on global sustainability, is the determination of optimal crop irrigation. The methodology proposed by the Food and Agriculture Organization (FAO) is based on estimating crop evapotranspiration (ETc), which is done by computing the reference crop evapotranspiration (ETo) multiplied by a crop coefficient (Kc). Some previous works proposed methods to compute Kc using remote crop images. The present research aims at complementing these systems, estimating ETo with the use of soil moisture sensors. A crop of kikuyu grass (Pennisetum clandestinum) was used as the reference crop. Four frequency-domain reflectometry sensors were installed, gathering moisture information during the study period from May 2015 to September 2016. Different machine learning regression algorithms were analyzed for the estimation of ETo using moisture and climatic data. The values were compared with respect to the ETo computed in an agroclimatic station using the Penman–Monteith method. The best method was the randomizable filtered classifier technique, based on the K* algorithm. This model achieved a correlation coefficient, R, of 0.9936, with a root-mean-squared error of 0.183 mm/day and 6.52% mean relative error; the second-best model used artificial neural networks, with an R of 0.9470 and 11% relative error. Thus, this new methodology allows obtaining accurate and cost-efficient prediction models for ETo, as well as for the water balance of the crops.
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Artículos de interés: Visión e Inteligencia Artificial |
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Estimation of the constituent properties of red delicious apples using a hybrid of artificial neural networks and artificial bee colony algorithm.
Non‐destructive estimation of the constituent properties of fruits and vegetables has led to a dramatic change in the agriculture and food industry, allowing accurate and efficient sorting of the products based on their internal properties. Therefore, the present study utilized visible (VIS) and near‐infrared (NIR) spectroscopy data in the range from 200 to 1100 nm for the estimation of several properties of Red Delicious apples, namely Brix minus acid (BrimA), firmness, acidity and starch content, using a hybrid of Artificial Neural Networks and Artificial Bee Colony (ANN–ABC) algorithm. Furthermore, the hybrid Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) algorithm was utilized to select the most effective properties to estimate these characteristics. The results indicated that there are different peaks within this spectral range, and the spectral range for each peak gives different results. To ensure the stability of the proposed method, 1000 replications were performed for each estimate. The highest coefficients of determination, R2, for estimating the studied properties among the 1000 replicates were 0.898 for BrimA, 0.8 for firmness, 0.825 for acidity and 0.973 for starch content. The selection of the most effective wavelengths for estimating the properties produced five effective wavelengths for BrimA, nine for firmness, seven for acidity and five for starch content. In this case, the best R2 of the hybrid ANN–ABC among the 1000 iterations were 0.828, 0.738, 0.9 and 0.923, respectively.
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