:: Volume 13, Issue 41 (4-2013) ::
جغرافیایی 2013, 13(41): 171-180 Back to browse issues page
Preparation of the Autumn Brassica napus Yield Map by Using Perceptron Neural Network, Case Study: Sabzevar Township
Abstract:   (14418 Views)
Brassica napus as an oilseed is a strategic agriculture product in Iran. Knowing the best area for cultivation, helps plan and explore suitable cultivation areas efficiently. At this research, we study 24-Brassica napus farm samples to calculate the actual yields by using GPS set. The independent data are variables introduced such as mean temperature, growing degree-day, mean absolute minimum temperature, mean absolute maximum temperature , mean temperature in sowing time, mean potential evapotranspiration, slope , EC of groundwater, pH of groundwater, and mean relative humidity. We analyzed the consequences of environmental potential on autumn Brassica napus yield using Perceptron neural network by multi-layers structure with 3 hidden layers and feed backward algorithm entered to database and Brassica napus cultivation suitability map was prepared in geographic information system environment. Results show that T-test between actual and prediction values do not have significant difference in 0.05 level. We calculated Pearson correlation coefficient at 0.98, RMSE and MAE. Thus 243 and 101 Kg/h indicate the ability of neural network methods for agricultural product yield predictions.
Keywords: Yield estimation, Brassica napus, Neural Network, GIS, Sabzevar Township
Full-Text [PDF 504 kb]   (1891 Downloads)    
Type of Study: Research | Subject: Special
Received: 2013/05/25 | Accepted: 2013/07/17 | Published: 2014/12/4


XML   Persian Abstract   Print



Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 13, Issue 41 (4-2013) Back to browse issues page