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Artificial neural networks for sustainable agribusiness: a case study of five energetic crops Agrociencia
Untaru,Mircea; Rotarescu,Vasile; Dorneanu,Liliana.
The growing agricultural economic environment referred to as agribusiness requires continuous balanced cost-benefit solutions. The use of artificial intelligence in this area provides complex solutions that are easily applicable. The objective of this study was to elaborate on innovative instruments from the field of artificial intelligence for the decision-making process related to energetic crops. The field of expertise of this paper is strongly related to current issues of sustainable development. The methodology used is artificial neural networks (ANN) and compares it with other tools. The targeted results regard the optimization of decision-making processes and the forecast of financial results in an agricultural economy. A case study of forecasting...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Energetic crops; Neural networks; Agribusiness; Resource saving.
Ano: 2012 URL: http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-31952012000500008
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Classificação de imagens de sementes de soja coloridas pelo tetrazólio baseada em aprendizado profundo. Repositório Alice
SOUZA JUNIOR, M.; FRANÇA-NETO, J. B.; SAITO, P. T. M.; BUGATTI, P. H..
bitstream/item/181375/1/Classificacao-de-imagens-p.1052-1054.pdf
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Teste de Tetrazólio; Soja; Semente; Aprendizagem; Controle de Qualidade; Soybeans; Seed quality; Neural networks; Tetrazolium.
Ano: 2018 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1094098
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CLASSIFICATION OF MACAW PALM FRUITS FROM COLORIMETRIC PROPERTIES FOR DETERMINING THE HARVEST MOMENT REA
Costa,Anderson G.; Pinto,Francisco de A. de C.; Motoike,Sérgio Y.; Braga Júnior,Roberto A.; Gracia,Luis M. Navas.
ABSTRACT Macaw palm (Acrocomia aculeata) is a promising crop for biofuel production due to the high concentration of its fruit oil, but the harvest date is an issue to be better understood so it could be cultivated on an industrial scale. The aim of this study was to use the colorimetric properties of the macaw palm fruits to develop a neural network classifier to determine the ideal moment for harvesting, based on the oil content of the fruit mesocarp. During nine weeks of maturation were sampled 900 fruits of macaw palm fruits and the colorimetric properties of the RGB, HSI and CIELab color models were used to classify the fruits into immature and mature fruits. Kappa index and the overall accuracy values were used to access the classifier performance....
Tipo: Info:eu-repo/semantics/article Palavras-chave: Digital images; Maturation; Neural networks; Oil content.
Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000400634
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Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping Rev. Bras. Ciênc. Solo
Chagas,César da Silva; Vieira,Carlos Antônio Oliveira; Fernandes Filho,Elpídio Inácio.
Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Terrain attributes; Neural networks; Maximum likelihood.
Ano: 2013 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832013000200005
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Crop yield estimation using satellite images: comparison of linear and non-linear models Agriscientia (Córdoba)
Sayago,S; Bocco,M.
Development of models for crop yield prediction using remote sensing allows accurate, reliable and timely estimations over large areas. Particularly, this information is necessary to ensure the adequacy of a nation's food supply as well as to aid policy makers and farmers. In Argentina, soybean (Glycine max (L.) Merr.) and corn (Zea mays L.) are the most important crops. The goal of this research was to develop and evaluate linear and non-linear models to estimate crop yield from satellite data. Particularly, we proposed and applied those models to obtain soybean and corn yield in the central region of Córdoba (Argentina) using Landsat and SPOT images. The models were designed taking into account all or some bands included in the images from one or both...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Neural networks; Multiple linear regression; Soybean; Corn; Modelling.
Ano: 2018 URL: http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S1668-298X2018000100001
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Deficiencias de hierro y manganeso en hojas de frijol (Phaseolus vulgaris L.) identificadas mendiante análisis textural, color de imágenes digitales y redes neuronales artificiales. Colegio de Postgraduados
García Cruz, Edgar.
En la presente investigación se analizaron imágenes digitales de hojas de frijol (Phaseolus vulgaris L.) para identificar con un clasificador, deficiencias de hierro (Fe) y manganeso (Mn). A los 24 días después de la siembra (dds) se les suministró la solución nutritiva de acuerdo a ocho tratamientos: dos deficiencias parciales, una de 50 % Fe y otra de 50 % Mn; dos deficiencias totales totales, 0 % Fe y una más de 0 % Mn además de una interacción (0 % Fe, 0 % Mn) y dos dosis excedentes (200 % Fe y 200 % Mn); finalmente un tratamiento testigo (100 % Fe, 100 % Mn) usando como referencia la solución Steiner. A partir de imágenes digitales de muestras de hojas de los tratamientos obtenidas a los 63 dds, se calcularon variables de color con los valores...
Palavras-chave: RGB; Textura; Redes neuronales; Phaseolus vulgaris; Hierro; Manganeso; Texture; Neural networks; Iron; Manganese; Edafología; Maestría.
Ano: 2013 URL: http://hdl.handle.net/10521/2076
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Detecção automática de uvas e folhas em viticultura com uma rede neural YOLOv2. Repositório Alice
SANTOS, A. A. dos; AVILA, S.; SANTOS, T. T..
RESUMO - Neste trabalho, o problema de detecção de frutas e folhas em viticultura para aplicações envolvendo sensoriamento próximo foi modelado como um problema de aprendizado supervisionado de máquina. Uma base de dados foi criada e manualmente anotada a partir de imagens obtidas em abril de 2017 na Vinícola Guaspari. No total são 11.883 imagens contendo exemplos de cachos de uvas e folhas. Uma rede convolutiva com arquitetura YOLOv2 foi treinada para localização e classificação de cachos e folhas. Testes quantitativos demonstraram resultados para a detecção e classificação com precisão de 100%, revocação de até 74,2% e F1-Score de 85,2% para classe "uva" e precisão de 100%, revocação de até 67,9% e F1-Score de 80,9% para a classe "folha". Testes...
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Detecção de frutos; Reconhecimento de Imagens; Aprendizagem profunda; Aprendizado de máquina; Redes neurais; Aprendizado supervisionado; Image Recognition; Fruit detection; Deep Learning; Learning machine; Viticultura; Viticulture; Neural networks.
Ano: 2018 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1096173
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Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram BJMBR
Chacón,M.; Curilem,G.; Acuña,G.; Defilippi,C.; Madrid,A.M.; Jara,S..
The aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Functional dyspepsia; Electrogastrography; Wavelet transform; Neural networks.
Ano: 2009 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2009001200014
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Diagnóstico de deficiencias de nitrógeno y maganesio con imágenes digitales. Colegio de Postgraduados
Reyes Flores, Maciel.
La detección oportuna de deficiencias nutrimentales en hojas de plantas cultivadas permite tomar medidas correctivas inmediatas asi como predecir rendimientos. Las características espectrales y de textura de las imágenes se pueden utilizar para obtener información y correlacionarlos con el estado nutrimental de elementos esenciales que generan sintomatología similar en hojas de las plantas. En la presente investigación se estableció un experimento para medir las propiedades espectrales y característica texturales del cultivo de frijol con diferentes concentraciones de nitrógeno y magnesio de imágenes obtenidas con escáner. A partir de los valores de reflectancia se generaron modelos de regresión para asociar la concentración de nitrógeno y magnesio en el...
Palavras-chave: Reflectancia; Discriminación; Espacios de color; Textura; Redes neuronales; Reflectance; Discrimination; Color spaces; Texture; Neural networks; Edafología; Maestría.
Ano: 2013 URL: http://hdl.handle.net/10521/2077
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Digital soilscape mapping of tropical hillslope areas by neural networks. Repositório Alice
CARVALHO JUNIOR, W. de; CHAGAS, C. da S.; FERNANDES FILHO, E. I.; VIEIRA, C. A. O.; SCHAEFER, C. E. G.; BHERING, S. B.; FRANCELINO, M. R..
2011
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Digital soilscape mapping; Neural networks.
Ano: 2011 URL: http://www.alice.cnptia.embrapa.br/handle/doc/919783
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Estimativa de precipitação através de imagens do satélite GOES-8 utilizando redes neurais. Repositório Alice
ÁVILA, A. M. H. de; PINTO, H. S.; ZULLO JÚNIOR, J.; ASSAD, E. D..
O presente trabalho tem por objetivo utilizar as informações disponíveis nos canais espectrais do satélite GOES-8 para estimar a precipitação em regiões tropicais utilizando métodos de rede neurais.
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Imagens de satélites; Redes neurais; Satélite GOES-8; Sensoriamento remoto; Neural networks; Remote sensing.
Ano: 2003 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/8861
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FEEDFORWARD NEURAL NETWORK ESTIMATION OF A CROP YIELD RESPONSE FUNCTION AgEcon
Joerding, Wayne H.; Li, Ying; Young, Douglas L..
Feedforward networks have powerful approximation capabilities without the "explosion of parameters" problem faced by Fourier and polynomial expansions. This paper first introduces feedforward networks and describes their approximation capabilities, then we address several practical issues faced by applications of feedforward networks. First, we demonstrate networks can provide a reasonable estimate of a Bermudagrass hay fertilizer response function with the relatively sparse data often available from experiments. Second, we demonstrate that the estimated network with a practical number of hidden units provides reasonable flexibility. Third, we show how one can constrain feedforward networks to satisfy a priori information without losing their flexible...
Tipo: Journal Article Palavras-chave: Biological process models; Feedforward networks; Production function; Neural networks; Research Methods/ Statistical Methods.
Ano: 1994 URL: http://purl.umn.edu/15430
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Forecasting Hog Prices with a Neural Network AgEcon
Hamm, Lonnie; Brorsen, B. Wade.
Neural network models were compared to traditional forecasting methods in forecasting the quarterly and monthly farm price of hogs. A quarterly neural network model forecasted poorly in comparison to a quarterly econometric model. A monthly neural network model outperformed a monthly ARIMA model with respect to the mean square error criterion and performed similarly to the ARIMA model with respect to turning point accuracy. The more positive results of the monthly neural network model in comparison to the quarterly neural network model may be due to nonlinearities in the monthly data which are not in the quarterly data.
Tipo: Journal Article Palavras-chave: Forecasting; Hog prices; Neural networks; ARIMA; Econometric; Agribusiness; Livestock Production/Industries.
Ano: 1997 URL: http://purl.umn.edu/90646
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IDENTIFICATION OF NAVEL ORANGE LESIONS BY NONLINEAR DEEP LEARNING ALGORITHM REA
Yang,Guoliang; Xu,Nan; Hong,Zhiyang.
ABSTRACT It is difficult for humans to recognize recessive diseases in navel oranges. Therefore, deep neural networks are applied to plant disease identification. To improve the feature extraction ability of convolutional neural networks, the Parameter Exponential Nonlinear Activation Unit (PENLU) is proposed to replace the activated function of the neural network. This function not only adds multiple parameters but also brings better generalization ability to the neural network. In addition, the proposed function parameters can be updated by the inverse Stochastic Gradient Descent (SGD) algorithm, which has unparalleled advantages over the existing activated functions. The Residual Network (ResNet), improved by PENLU, is applied to navel orange lesion...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Neural networks; Activation function; Plant image classification; Lesion detection.
Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000500783
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Learning about brain physiology and complexity from the study of the epilepsies BJMBR
Garcia-Cairasco,N..
The brain is a complex system, which produces emergent properties such as those associated with activity-dependent plasticity in processes of learning and memory. Therefore, understanding the integrated structures and functions of the brain is well beyond the scope of either superficial or extremely reductionistic approaches. Although a combination of zoom-in and zoom-out strategies is desirable when the brain is studied, constructing the appropriate interfaces to connect all levels of analysis is one of the most difficult challenges of contemporary neuroscience. Is it possible to build appropriate models of brain function and dysfunctions with computational tools? Among the best-known brain dysfunctions, epilepsies are neurological syndromes that reach a...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Complex systems; Emergence; Epileptology; Neural networks; E-Neuroscience; Neuroinformatics.
Ano: 2009 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2009000100012
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Modeling of soil penetration resistance using statistical analyses and artificial neural networks - doi: 10.4025/actasciagron.v34i2.11627 Agronomy
Santos, Fábio Lúcio; Universidade Federal de Viçosa; Jesus, Valquíria Aparecida Mendes de; Universidade Federal de Viçosa; Valente, Domingos Sárvio Magalhães; Universidade Federal de Viçosa.
An important factor for the evaluation of an agricultural system’s sustainability is the monitoring of soil quality via its physical attributes. The physical attributes of soil, such as soil penetration resistance, can be used to monitor and evaluate the soil’s quality. Artificial Neural Networks (ANN) have been employed to solve many problems in agriculture, and the use of this technique can be considered an alternative approach for predicting the penetration resistance produced by the soil’s basic properties, such as bulk density and water content. The aim of this work is to perform an analysis of the soil penetration resistance behavior measured from the cone index under different levels of bulk density and water content using statistical analyses,...
Palavras-chave: 5.03.00.00-8 modeling; Soil physical properties; Neural networks.
Ano: 2012 URL: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/11627
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Modelos de predição da ferrugem do cafeeiro (Hemileia vastatrix Berkeley & Broome) por técnicas de mineração de dados. Repositório Alice
DI GIROLAMO NETO, C.; RODRIGUES, L. H. A.; MEIRA, C. A. A..
RESUMO: A ferrugem é a principal doença do cafeeiro, podendo gerar perdas significativas na produção, caso medidas de controle não sejam adotadas. Modelos de alerta de doenças agrícolas são capazes de gerar informações para aplicações de defensivos somente quando necessário, reduzindo gastos por parte do produtor e impactos ambientais. Objetivou-se, neste trabalho, desenvolver, comparar e selecionar modelos de alerta baseados em técnicas de mineração de dados para a predição da ferrugem do cafeeiro, em anos de alta e baixa carga pendente de frutos. Foram utilizados dados obtidos em lavouras de café em produção, ao longo de 13 anos (1998-2011). Vinte e três atributos foram considerados como variáveis independentes (preditoras) e, como variável dependente, a...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Ferrugem do cafeeiro; Alerta de doenças; Florestas aleatórias; Máquinas de vetores suporte; Redes neurais artificiais; Árvores de decisão; Decision tree; Neural networks.
Ano: 2014 URL: http://www.alice.cnptia.embrapa.br/handle/doc/991078
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Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market Ciência Rural
Pinheiro,Carlos Alberto Orge; Senna,Valter de.
ABSTRACT: The purpose of this study is to apply the methodology proposed by PINHEIRO & SENNA (2015) to a set of agricultural products traded in Brazil. The multivariate and nonlinear character of this methodology has shown to be suitable, as compared to the neural network model, since it allows for a better predictive performance. Results obtained in an out-of-sample period, by using the calculated error and statistical test, confirmed this statement. This study will be useful to farmers as price forecasting based on their tendency is relevant.
Tipo: Info:eu-repo/semantics/article Palavras-chave: Neural networks; Multivariate analysis; Agricultural products; Forecasting.
Ano: 2017 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000100931
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Neural modeling of bromelain extraction by reversed micelles BABT
Fileti,Ana Maria Frattini; Fischer,Gilvan Anderson; Tambourgi,Elias Basile.
A pulsed-cap microcolumn was used for bromelain extraction from pineapple juice by reversed micelles. The cationic micellar solution used BDBAC as the surfactant, isooctane as the solvent and hexanol as the co-solvent. In order to capture the dynamic behavior and the nonlinearities of the column, the operating conditions were modified in accordance with the central composite design for the experiment, using the ratio between the light phase flow rate and the total flow rate, and the time interval between pulses. The effects on the purification factor and on total protein yield were modeled via neural networks. The best topology was defined as 16-9-2, and the input layer was a moving window of the independent variables. The neural model successfully...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Bromelain; Reversed micelles; Extraction; Neural networks; Pineapple.
Ano: 2010 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132010000200026
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Neural network approximation of sampling yield-effort curves of rice invertebrates International Rice Research Institute
Zhang, W. J.; Qi, Y. H.; Barrion, A. T..
p.40-42
Palavras-chave: Invertebrates; Neural networks; Sampling; Taxa.
Ano: 2004 URL: http://hdl.handle.net/123456789/1034
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