Predicting the draught requirement of tillage implements in sandy clay loam soil using an artificial neural network

Artificial Neural Network Model for Predicting Specific Draft Force and Fuel Consumption Requirement of a Mouldboard Plough

“…The proposed neural network model by Al-Janobi et al (2001), by testing, indicated that there was a small variation of measured and predicted data with linear correlation coefficient equals to 0.987 and mean squared error between experimental and predicted specific draft equals to 0.1445. Roul et al (2009) found that good agreement between measured and predicted draught requirement of tillage implements values was found with a coefficient of determination of 0.99, indicating that the ANN model had successfully learnt from the training data set to enable correct interpolation.…”

“…The draft force of tillage equipments is one of the most important forces that has been used for measuring and evaluation of energy requirement for tillage equipments. This force is a function of following parameters (Godwin et al, 2007;Roul et al, 2009): 1. Soil conditions such as its moisture content and texture.…”

“…They reported that the developed neural network model for the draft force prediction had good generalisation ability within the range of input parameters. Roul et al, (2009) developed an ANN model for predicting the draught requirement of tillage implements, such as mouldboard ploughs, cultivators and disc harrows for different soil physical conditions, namely, moisture content and bulk density. A 5-9-1 neural network was capable of predicting draught requirement of tillage implements in sandy clay loam soil under varying operating and soil conditions as indicated by high coefficient of determination (0.99), low mean squared error (0.0264) and low percentage error (6.4 %).…”

Models for Prediction of Physiological Responses of Holstein Dairy Cows

“…This approach is being used in fields such as aviculture (Lopes et al 2008), applied geography (Spellman 1999), thermal sciences and engineering (Yang 2008), hydrology (Kurtulus and Razack 2010), the study of thermal comfort in cattle (Brown-Brandl, Jones, and Woldt 2005), and growth performance in swine (Bridges et al 1995) and in humans (Moustris et al 2010). ANNs have been used in predicting leather handle (Wang et al 2011), discriminating varieties of tea plant (Li and He 2008), classifying genera and identifying species in mosquitoes (Venkateswarlu, Kiran, and Eswari 2012), estimating leaf chlorophyll concentration in rice under stress from heavy metals (Liu et al 2010), modeling total volume of dominant pine trees in reforestations (Diamantopoulou and Milios 2010), predicting the draught requirement of tillage implements in sandy clay loam soil (Roul et al 2009), predicting nitrate release from polymer-coated fertilizers (Du et al 2008), and analyzing thermodynamic properties of refrigerants (Şahin, Köse, and Selbaş 2012).…”

Nota Técnica: Avaliação De Uma Caixa De Solo Para Estudos Da Interação Dinâmica Máquina Solo

“…Com base nos resultados em caixa de solo, foi selecionada a velocidade de 1,5 m s -1 como velocidade máxima de coleta de dados de campo, abaixo do qual os efeitos de velocidade nas IPSS (força dividida pela área da base da ponta prismática do penetrômetro operando horizontalmente) foram considerados desprezíveis. Roul et al (2009) utilizaram uma caixa de solo de 15,0 m de comprimento, 1,8 m de largura e 0,6 m de profundidade do laboratório Engenharia Agrícola e de Alimentos do Instituto de Tecnologia da Índia para determinação da resistência à penetração pelo índice de cone em compactações realizados por um rolo compactador montado em um carro porta ferramentas à diferentes velocidades.…”