Yield Estimation and Clustering of Chickpea Genotypes Using Soft Computing Techniques

Abstract

A gronomy J our n al • Volu me 10 0 , I s sue 4 • 2 0 0 8 ABSTRACT Crop growth is a multifactorial nonlinear process and diff erent kinds of models have been developed to predict crop yield. In recent years, crop growth models have become increasingly important as major components of agriculture-related decision-support systems. Moreover, clustering is a multivariate analysis technique widely adopted in agricultural studies. Using this method, diff erent genotypes (accessions) of crops can be classifi ed and characterized. Th is paper discusses the use of soft computing techniques such as artifi cial neural networks (ANN) and fuzzy logic based approaches in regression and clustering problems. Th e ANN technology was used for modeling the correlation between crop yield and 10 yield components of chickpea (Cicer arietinum L.). Also, the fuzzy c-means (FCM) clustering technique was used for the classifi cation of 362 chickpea genotypes based on their agronomic and morphological traits. Th e ANN performed very well. Among the various ANN structures, a model of good performance was produced by 10-14-3-1 structure with a training algorithm of back-propagation and hyperbolic tangent transfer function in the hidden and output layers. Th e model was able to predict the chickpea yield data of 0.32 to 14.38 g plant -1 with a RMSE and T value of 0.0195 g plant -1 and 0.988, respectively. T statistics measures the scattering around line (1:1). When T is close to 1.0, the fi tting is desirable. Th e mean absolute error, relative error, and coeffi cient of determination between actual and predicted data were 0.0109 g plant -1 , -1.07%, and 0.991, respectively. Th e ANN model predicted 90.3% of the yield data with relative errors ranging between ±5%. Th e consequent reduction in the number of training data from 250 to 50, decreased the training RMSE, but increased the prediction error. It was found that even with a few number of patterns in the training dataset (50 patterns), the prediction error of the ANN model was in the range of acceptance for yield modeling. Obviously, with 25 × 10 3 iterations, the ANN models with 5 and 10 input variables gave almost the same estimation of the chickpea yield. Th e results of clustering showed that the FCM clustering technique can be successfully applied to classify chickpea genotypes in terms of agronomic and morphological traits.

Development of Prediction Model and Experimental Validation in Predicting the Curcumin Content of Turmeric (Curcuma longa L.)

“…The use of ANNs has gained increasing applications where the dependency between dependent and independent variables is either unknown or very complex ( Almeida, 2002 ; Altun et al, 2007 ; Khazaei et al, 2008 ). Neural network models provide accurate results for complicated system analysis than conventional mathematical models ( Alam and Naik, 2009 ).…”

Transfer Function and Time Series Outlier Analysis: Modelling Soil Salinity in Loamy Sand Soil by Including the Influences of Irrigation Management and Soil Temperature

“…The use of stochastic models has increased significantly over the last decade. For example, they have been employed in: artificial neural networks (ANNs) (Huang et al ., ); agronomic applications that model crop development (Zhang et al ., ; Fortin et al ., ); and predicting crop yields (Park et al ., ; Green et al ., ; Khazaei et al ., ). Zou et al .…”

“…The use of stochastic models has increased significantly over the last decade. For example, they have been employed in: artificial neural networks (ANNs) (Huang et al, 2010); agronomic applications that model crop development (Zhang et al, 2009;Fortin et al, 2010); and predicting crop yields (Park et al, 2005;Green et al, 2007;Khazaei et al, 2008). Zou et al (2010) collected silt loam soil profile data on a monthly basis from 2001 to 2006 and used them to compare the back-propagation neural network (BPNN) model and the autoregressive integrated moving average (ARIMA) model.…”

Advances in Artificial Neural Networks – Methodological Development and Application

“…Recently, the BP ANN method was used in regression for modeling the correlation between crop yield and 10 yield components of chickpea [104]. A BP ANN was employed and trained with extracted features from the data collected by electronic nose to identify the wheat age [105].…”