Estimation of Fuel Consumption in Agricultural Mechanized Operations Using Artificial Neural Networks

Abstract

This study aimed to develop artificial neural networks for the estimation of tractor fuel consumption during soil preparation, according to the adopted system. The multilayer perceptron network was chosen. As input data: the soil mechanical penetration resistance, the mobilized area by implements, the working gear and the tractor engine speed. The number of layers and neurons varied to form different architectures. The adjustment was verified based on various statistical criteria. The values estimated by the networks did not differ significantly from those obtained experimentally. The conclusion was that the networks showed adequate reliability and accuracy to predicting the fuel consumption in each tillage system, in function of the input data and this can be a useful tool for planning and management of agricultural operations.

K-Nearest Neighbors Method for Prediction of Fuel Consumption in Tractor-Chisel Plow Systems

“…In Table 1, for fuel consumption data, the skewness coefficient is a negative value, which indicates that data are skewed left; also, the kurtosis coefficient is a negative value, which indicates that data have a platykurtic distribution. However, Borges et al (2017) also obtained negative values for skewness and kurtosis for tractor fuel consumption data. In addition, the variation coefficient is slightly high (31.3%), because the aforementioned fuel rate data were collected from different sources.…”

“…Developing the ability to predict fuel consumption of tractor-machinery systems is extremely beneficial for farms for budgeting and management; however, fuel consumption is measured by the amount of fuel used during a specific time period (Grisso et al, 2010). Furthermore, efficient planning of mechanized farming operations is a complex task, because it involves multiple factors related to the soil composition, the implemented machine, and the decisionmaking personnel (Borges et al, 2017). Additionally, predicting tractor fuel consumption can lead to more decisions that are appropriate for tractor management (Karparvarfard & Rahmanian-Koushkaki, 2015).…”

“…Thus, predictive models capable of forecasting the fuel consumption of tractor-machinery systems under different conditions can help farmers optimize their fuel expenditure. Considerable research has addressed the prediction of fuel consumption during the tillage of selected regions with solutions using traditional, statistical, and modern computational methods, or combinations among those literature results (Karparvarfard & Rahmanian-Koushkaki, 2015;Tayel et al, 2015;Almaliki et al, 2016;Borges et al, 2017;Ranjbarian et al, 2017). Successful prediction of the fuel consumption of tractor-chisel systems can aid in selecting tractors that minimize the cost of fuel for tillage.…”

Artificial Neural Networks for Prediction of Physiological and Productive Variables of Broilers

“…In this study, of all trained network architectures (3000) using the mentioned methodology, the tested architecture that presented the best performance for prediction of tclo, FC, and Cwater was the multi-layer network (multi-layer perceptron; MLP) with 50 neurons in the hidden layer. This MLP architecture has been widely used for the development of ANN (Rocha Neto et al, 2015;Rigo Júnior et al, 2016;Borges et al, 2017;Felix et al, 2017).…”

Artificial Neural Networks for Predicting Animal Thermal Comfort

“…Similarly, Miguel et al (2015) applied neural networks using satellite data to model the volume of wood and biomass of a semi-deciduous seasonal forest. Borges et al (2017) proposed the use of architectures to estimate the fuel consumption of tractors as a function of performance parameters of mechanized assemblies. Multilayer perceptron networks were used in these studies.…”