Saffron Agronomy and Technology

Saffron Agronomy and Technology

Evaluation of the efficiency of Lazy Algorithm in Estimating Yield of Saffron Based on Climatic Parameters

Document Type : Short Communication

Authors
Fahime KHadempour 1
Abbas Khashei Siuki 2
Mohammad Ali Behdani 3
1 PhD. Student, Department of Science and Water Engineering, Faculty of Agriculture, Ph.D. Student of water Resource Engineering, University of Birjand, Birjand, Iran.
2 Associate professor, Department of Science and Water Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran.
3 professor, Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Birjand, Birjand, Iran
10.22048/jsat.2020.174803.1338
Abstract
Saffron as the most precise agricultural and pharmaceutical product of the world has a specific place in industrial and export products of Iran. Nowadays, Iran is the largest producer and exporter of saffron in world and up to 93.7% of production of this valuable commodity belongs to Iran. Despite the antiquity of saffron cultivation and added value of this product compared to other current crops of Iran, fewer shares of new technologies are dedicated to saffron and its production is mainly based on indigenous knowledge.In thispaper, multiple models are employed to evaluate and develop the performance of KStar and LWL in order to get an estimate on production yield of saffron based on climate parameters. Thecalibration and evaluation of models are obtained from the statistics of crop yield and climate factors betweenyears 1988–2017. In order to evaluate the employed models, the following statistical criteria were used: Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Nash- Sutcliffe (NSE). From among the proposed models, the KStar model is in the e-scenario with an R2 of 1.00, MAE and RMSE of 0.00 and NSE of 1.00, which has good accuracy in estimating production yield of the saffron plant. This precision of the KStar model has made it easy to estimate performance of saffron in different areas of the country based on the data available at different stations.
Keywords
LWL
Kstar
Saffron Plant
Statistical Indicators

Subjects

Adams, R.M. 2000. Climate variability and climate change: Implications for agriculture. IRI Proceedings. Oregon State University, U.S.A.
Akbarpour, A., Kharashadizadeh, O., Shahidi, A., and Ghochanian, E. 2013. Performance evaluation of artificial neural network models in estimate production of yield saffron based on climate parameters. Journal Saffron Research 1 (1): 27-35. (In Persian with English Summary).
Ataee, Sh. 2007. Data WEKA Software. University of Science and Technology, Iran, College of Raliway Engineering. (In Persian).
Behdani, M.A. Koocheki, A.R., Nassiri Mahallati, M., and Rezvani Moghaddam, P. 2005. Evaluating the relationships between revenue and consumption of nutrients in Crocus sativus. Iran Journal Field Crop Resource. (In Persian with English Summary).
Cleary, J.G., and Trigg, L.E. 1995. An: Instance- based learner using an entropic distance measure. Proceedings of the 12th International Conference on Machine Learning. pp.108-114.
Cravener, T.L., and Roush, W.B. 2001. Prediction of amino acid profiles in feed ingredients: genetic algorithm calibration of artificial neural networks. Journal Animal Feed Science Tecnology 90: 131-141.
Oftadeh- Fadafen, A., Aminifard, M.H., Behdani, M.A., and Moradinezhad, F. 2016. Evaluation of nitroxin and vermicompost on quantitative characteristics and photosynthetic pigments of saffron (Crocus sativus L.). Journal Saffron Research 5 (2): 163-179. (In Persian with English Summary).
KhasheiSiuki, A., Hashemi, S.R., and Ahmadee, M. 2016. Application of the Taguchi approach in the evaluation of saffron (Crocus sativus L.) emergence affeceted by Zeolite and Irrigation scheduling. Journal of Saffron Research 4 (2): 266-278. (In Persian with English Summary).
Mzabri, I., Legsayer, M., Chetouani, M., Aamar, A., and Kouddane, N. 2017. Saffron (Crocus sativus L.) yield parameter assessment of abiotic stressed corms stored in low temperature. Journal of Materials and Environmental Sciences 8 (10): 3588-3597.
Rosenzweig, C., and Parry, M.L. 1994. Potential impacts of climate change on world food supply. Nature 367: 133-138.
Sadeghi, B. 1993. Effect of Corm Weight on Saffron Flower Collection. Publication of Scientific Resource- Technology Research Center of Khorasan.
Witten, I.H., and Frank, E. 2000. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers.
Witten, I.H., and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, San Francisco.
Wu, F.Y., and Yen, K.K. 1992. Application of neural network in regression analysis. Computer and Industrial Engineering 23: 93-95.
 
 
Saffron Agronomy and Technology
Volume 8, Issue 2 - Serial Number 28
Spring 2020
Pages 295-304