Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production

Abraham, Emerson Rodolfo; Reis, João Gilberto Mendes dos; Vendrametto, Oduvaldo; Neto, Pedro Luiz de Oliveira Costa; Toloi, Rodrigo Carlo; Souza, Aguinaldo Eduardo de; Morais, Marcos de Oliveira

doi:10.3390/agriculture10100475

Cited by 56 publications

(24 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In research with artificial neural networks to predict the harvest area, productivity, and production for soybean in Brazil by gathering data from a 1961-2016 time series, Reference [41] obtained reliable models for future predictions and support to farmers and the market in anticipating productive information. In the analysis carried [30], a large percentage of farmers in Brazil pointed to the expectation of implementing more complex and advanced technologies, such as sensor systems, to support the planting and application of fertilizers at different rates (67%), analysis, and integration of different databases in agriculture (78%).…”

Section: Resultsmentioning

confidence: 99%

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

et al. 2020

View full text Add to dashboard Cite

The rapid population growth has driven the demand for more food, fiber, energy, and water, which is associated to an increase in the need to use natural resources in a more sustainable way. The use of precision agriculture machinery and equipment since the 1990s has provided important productive gains and maximized the use of agricultural inputs. The growing connectivity in the rural environment, in addition to its greater integration with data from sensor systems, remote sensors, equipment, and smartphones have paved the way for new concepts from the so-called Agriculture 4.0 or Digital Agriculture. This article presents the results of a survey carried out with 504 Brazilian farmers about the digital technologies in use, as well as current and future applications, perceived benefits, and challenges. The questionnaire was prepared, organized, and made available to the public through the online platform LimeSurvey and was available from 17 April to 2 June 2020. The primary data obtained for each question previously defined were consolidated and analyzed statistically. The results indicate that 84% of the interviewed farmers use at least one digital technology in their production system that differs according to technological complexity level. The main perceived benefit refers to the perception of increased productivity and the main challenges are the acquisition costs of machines, equipment, software, and connectivity. It is also noteworthy that 95% of farmers would like to learn more about new technologies to strengthen the agricultural development in their properties.

show abstract

Section: Resultsmentioning

confidence: 99%

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Furthermore, in training for environmental impacts and output energy, the researchers reported the R 2 in the range from 0.923-0.986 in planted farms and 0.942-0.982 in ratoon farms. Based on obtained data from a time series (1961-2016), Abraham et al [42] practiced the ANN method to estimate some soybean harvest parameters such as the area, yield, and production in Brazil, and compared the results with classical methods of time series analysis. They stated that, in the case of harvest area and production, ANN was the best approach, while a classical linear function was more effective for the yield prediction.…”

Section: Evaluation Of Ann Modelsmentioning

confidence: 99%

Prediction of Almond Nut Yield and Its Greenhouse Gases Emission Using Different Methodologies

et al. 2022

View full text Add to dashboard Cite

The evaluation of a production system to analyze greenhouse gases is one of the most interesting challenges for researchers. The aim of the present study is to model almond nut production based on inputs by employing artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) procedures. To predict the almond nut yield with respect to the energy inputs, several ANN and ANFIS models were developed, evaluated, and compared. Among the several developed ANNs, a network with an architecture of 8-12-1 and a log-sigmoid, and a linear transfer function in the hidden and output layers, respectively, is found to be the best model. In general, both approaches had a good capability for predicting the nut yield. The comparison results revealed that the ANN procedure could predict the nut yield more precisely than the ANFIS models. Furthermore, greenhouse gas (GHG) emissions in almond orchards are determined where the total GHG emission is estimated to be about 2348.85 kg CO2eq ha−1. Among the inputs, electricity had the largest contribution to GHG emissions, with a share of 72.32%.

show abstract

“…Full, however, forecast the future price of cotton, dependence on cotton futures can be conducive to significant failures of it [28]. In the opinion of Bernake (2008), quotes from upcoming markets were underestimated when this can contribute to subsequent under-predictions of overall inflation as a result of the pace of commodity price gains [29]. And in this regard, a new model is needed in order to precisely predict the cotton prices exploit, aside from supply and demand.…”

Section: Related Workmentioning

confidence: 99%

An AI Based Web Portal for Cotton Price Analysis and Prediction

Bohara¹,

Patel²,

Patel³

et al. 2021

Atlantis Highlights in Computer Sciences

View full text Add to dashboard Cite

Artificial Intelligence based Web portal is extensively used in worldwide because it used to solve various primary low level problems in agriculture such as predicting precise prices of agriculture product using machine learning algorithms, enabling direct connection between buyer and seller without any middlemen, decreasing wastage of post-harvest crop, fixing appropriate price margin for farmers and so on. These steps helps to increase the capital income and creates the beneficial changes in the agriculture and also it leads to take the agriculture to next level. Cotton is a crop that is possibly the most expensive including all agricultural products sold. Owing to the high uncertainty of cotton markets, it is incredibly difficult to forecast future demand patterns and develop an acceptable pricing plan in order to compete in the market and maximize the company's volume for long-term development. Keeping in mind all the factors stated above for the cultivation of cotton, conclusion can be inferred as the cotton is highly volatile in nature. In this paper, we tend to build an Artificial Intelligence based Web Portal which provides data analysis of price of past years and also gives predictions about the cotton prices to the fellow traders. The paper aims to create a web model for cotton price prediction. Furthermore, the study tries to run a multiple regression model based on different variables to estimate the price of cotton.

show abstract

Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production

Cited by 56 publications

References 39 publications

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

Precision and Digital Agriculture: Adoption of Technologies and Perception of Brazilian Farmers

Prediction of Almond Nut Yield and Its Greenhouse Gases Emission Using Different Methodologies

An AI Based Web Portal for Cotton Price Analysis and Prediction

Contact Info

Product

Resources

About