Modelling fuel consumption in wheat production using artificial neural networks

A fuel consumption model for off-road use of mobile machinery in agriculture

“…Most FCIs increase with smaller field plot sizes but not always. For wheat production in New Zealand, where farms are clearly larger, grain yield are higher, and average tractor power seems comparable, an estimated average of 65.3 L/ha was found with slightly lower (64.9 L/ha) values on irrigated land and slightly higher values (66 L/ha) on dry farm land [27,28]. Fuel consumption data for ploughing (29.6 L/ha) and harvesting (18 L/ha) are very similar to those of Belgium.…”

“…Disregarding the differences in agro-ecological conditions and cultivation practices, comparable fuel consumption indicators have been calculated and reported in literature for either field operations [8,16,30] or crop production [16,21,25,27,30]. Fuel consumption indicators for field operations very often do not distinguish between field sizes and soil conditions, and show little variation in their values.…”

“…Ref. [27] used artificial neural networks and inter alia survey data from 40 farmers to predict fuel consumption in wheat production. The same authors previously predicted direct and indirect energy consumption in wheat production via artificial neural networks and multiple linear regression [26].…”

Predictive model for assessing and optimizing solar still performance using artificial neural network under hyper arid environment

“…Every neuron is connected with no less than one other neuron and every connection is represented by a real number called a weight. Iteratively, the weights are adjusted so as to the network attempts to produce the desired output (Safa et al, 2009). Demuth and Beale (2004) stated that each artificial neuron is a unitary computational processor, which has a summing junction operator ( P ) and a transfer (activation) function f(z).…”

Prediction of Tire Tractive Performance by Using Artificial Neural Networks

“…At present, various techniques in soft computing such as statistics, machine learning, neural network and fuzzy data analysis are being used for exploratory data analysis. In recent years, the methods of artificial intelligence have widely been used in different areas including agricultural applications [10][11][12].…”