Techniques for Yield Prediction from Corn Aerial Images - A Neural Network Approach -

  • Zhang, Q. (Dept. of Agricultural & Biosystems Engineering, North Dakota State University, North Dakota, USA) ;
  • Panigrahi, S. (Dept. of Agricultural & Biosystems Engineering, North Dakota State University, North Dakota, USA) ;
  • Panda, S.S. (Dept. of Agricultural & Biosystems Engineering, North Dakota State University, North Dakota, USA) ;
  • Borhan, Md.S. (Dept. of Agricultural & Biosystems Engineering, North Dakota State University, North Dakota, USA)
  • Published : 2002.06.01

Abstract

Neural network based models were developed and evaluated for predicting corn yield from aerial images based on 1998 and 1994 image data. The model used images in multi-spectral bands such as R, G, B, and IR (Red, Green, Blue and Infrared). The inputs to the neural network consisted of mean and standard deviation of multispectral bands of the aerial images. Performances of several neural network architectures using back-propagation with momentum were compared. The maximum yield prediction accuracy obtained was 97.81%. The BPNN model prediction accuracy could be enhanced by using more number of observations to the model, other data transformation techniques, or by performing optical calibration of the aerial image.