An Artificial Neural Network Model for Crop Yield Responding to Soil Parameters
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This paper presents an artificial neural network model for crop yield responding to soil parameters. The experimental data had been obtained via a precision agriculture experiment, which is carried out by PAC in a demo farm locating in Shunyi district, Beijing in 2000. The model has been established by training a back propagation neural network with 58 samples and tested with other 14 samples. The model consists of 6, 11 and 1 processing units in the input, hidden and output layers, and the step length is 0.05, the momentum coefficient is 0.5. The training was terminated after 20000 times and the convergence effect was very good. The training results are that the correlation coefficient is 0.916 and the average error value is 2.8×10-2. It shows that the model can precisely describe crop yield responding to soil parameters.
KeywordsHide Layer Crop Yield Output Layer Artificial Neural Network Model Back Propagation
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- 1.Wang, M.: The Roadmap of ICT for Agriculture and Precision Farming In Less Developed Regions. In: 2004 CIGR International Conference, Beijing, China (2004)Google Scholar
- 2.Kuang, P.: The Precision Agricultural Development Approach of Developing Countries (Areas). In: 2004 CIGR International Conference, Beijing, China (2004)Google Scholar
- 3.Zhou, M., Chen, H.: Artificial Neural Network Model for Soil Moisture Forecast in Deficit Irrigation Rice Field. In: 2004 CIGR International Conference, Beijing, China (2004)Google Scholar
- 4.Huo, Z., Shi, H., Qiao, D.: Study on Artificial Neural Network Model for Crop Response to Soil Water-Salt. In: 2004 CIGR International Conference, Beijing, China (2004)Google Scholar
- 5.Zhang, X., Engel, B.A., Benady, N.: Locating Melons Using Artificial Neural Networks. ASAE Paper (1992)Google Scholar
- 6.Fraisse, C.W., Sudduth, K.A., Kitchen, N.R.: Evaluation of Crop Models to Simulate Site- Specific Crop Development and Yield. In: Proceedings of the 4th International Conference on Precision Agriculture, St. Paul, MN, pp. 1297–1308 (1998)Google Scholar
- 7.Zaidi, M.A., Murase, H.: Evaluation of Seeding Vigour Using Neural Network Model under Clinostated Conditions. In: International Conference on Agricultural and Science and Technology, Beijing, China, pp. 504–507 (2001)Google Scholar
- 8.Li, X., Qiao, X., Ye, T.: A New Fuzzy Neural Network Controller Applied in The Greenhouse. In: Progress of Information Technology in Agriculture, Beijing, China, pp. 546–549 (2003)Google Scholar
- 9.Liu, G., Kuang, J.: A Study on Spatial Variability of Soil Nutrient within Field. In: Proceedings of International Conference on Engineering and Technological Sciences, Beijing, China, pp. 189–193 (2000)Google Scholar
- 10.Yuan, H., Xiong, F.: A Novel Approach For Extracting Rules from Trained Neural Network. In: International Symposium on Intelligent Agricultural Information Technology, Beijing, China, pp. 305–309 (2000)Google Scholar