Application of artificial neural network in predicting crop yield: a review

Agricultural system is very complex since it deals with large data situation which comes from a number of factors. A lot of techniques and approaches have been used to identify any interactions between factors that affecting yields with the crop performances. The application of neural network to the task of solving non-linear and complex systems is promising. This paper presents a review on the use of artificial neural network (ANN) in predicting crop yield using various crop performance factors. General overview on the application of ANN and the basic concept of neural network architecture are also presented. From the literature, it has been shown that ANN provides better interpretation of crop variability compared to the other methods.

[1]  Eduard Llobet,et al.  Non-destructive banana ripeness determination using a neural network-based electronic nose , 1999 .

[2]  S. Prasher,et al.  Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data , 2005 .

[3]  V. Alchanatis,et al.  Review: Sensing technologies for precision specialty crop production , 2010 .

[4]  Angelo Fabbri,et al.  Discrimination of apricot cultivars by gas multisensor array using an artificial neural network , 2007 .

[5]  John A. Marchant,et al.  Comparison of a Bayesian classifier with a multilayer feed-forward neural network using the example of plant/weed/soil discrimination , 2003 .

[6]  Raphael Linker,et al.  Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models , 2004, Math. Comput. Simul..

[7]  H. Murase,et al.  Identification of a Moss Growth System using an Artificial Neural Network Model , 2006 .

[8]  A. Sasao,et al.  Estimation of citrus yield from airborne hyperspectral images using a neural network model , 2006 .

[9]  Daniel Kersten,et al.  Introduction to neural networks , 1993 .

[10]  Rudolf Kruse,et al.  Data Mining with Neural Networks for Wheat Yield Prediction , 2008, ICDM.

[11]  Chengwei Ma,et al.  Modeling greenhouse air humidity by means of artificial neural network and principal component analysis , 2010 .

[12]  Minghua Zhang,et al.  Prediction of Soybean Growth and Development Using Artificial Neural Network and Statistical Models , 2009 .

[13]  Mohammad Safdar Baloch,et al.  Dynamic optimization of watering Satsuma mandarin using neural networks and genetic algorithms , 2007 .

[14]  C. Walthall,et al.  Artificial neural networks for corn and soybean yield prediction , 2005 .

[15]  Bernard A. Engel,et al.  AE—Automation and Emerging Technologies: Neural Network Prediction of Maize Yield using Alternative Data Coding Algorithms , 2002 .

[16]  Sharon B. Hornick,et al.  Factors affecting the nutritional quality of crops , 1992 .

[17]  M. M. Rahman,et al.  MODELLING OF JUTE PRODUCTION USING ARTIFICIAL NEURAL NETWORKS , 2010 .

[18]  Fathi Fourati,et al.  A greenhouse control with feed-forward and recurrent neural networks , 2007, Simul. Model. Pract. Theory.

[19]  I. Seginer,et al.  Some artificial neural network applications to greenhouse environmental control , 1997 .

[20]  Alfred Schultz,et al.  Neural networks in agroecological modelling - stylish application or helpful tool? , 2000 .

[21]  H. Noh,et al.  A Neural Network Model of Maize Crop Nitrogen Stress Assessment for a Multi-spectral Imaging Sensor , 2006 .

[22]  Graeme L. Hammer,et al.  Future contributions of crop modelling—from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement , 2002 .

[23]  Y. Chtioui,et al.  A generalized regression neural network and its application for leaf wetness prediction to forecast plant disease , 1999 .

[24]  Yu Song,et al.  Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses , 2009 .

[25]  B. In,et al.  A neural network technique to develop a vase life prediction model of cut roses , 2009 .

[26]  Ralf Wieland,et al.  Adaptive fuzzy modeling versus artificial neural networks , 2008, Environ. Model. Softw..

[27]  Kenneth A. Sudduth,et al.  Soil electrical conductivity and topography related to yield for three contrasting soil-crop systems , 2003 .

[28]  M. Ponnavaikko,et al.  ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW , 1998 .

[29]  Jose D. Salas,et al.  Relating crop yield to topographic attributes using Spatial Analysis Neural Networks and regression , 2007 .

[30]  Finn Plauborg,et al.  Estimating plant root water uptake using a neural network approach , 2010 .

[31]  Yubin Lan,et al.  Review: Development of soft computing and applications in agricultural and biological engineering , 2010 .

[32]  R. Alvarez.  Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach , 2009 .