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Gadallah, Ahmed M.
- Crops Plantation Planning Respecting Climate Changes:Fuzzy-Based Approach
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Authors
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1 Department of Computer Science, Institute of Statistical Studies and Research, Cairo University, Giza, EG
1 Department of Computer Science, Institute of Statistical Studies and Research, Cairo University, Giza, EG
Source
Indian Journal of Science and Technology, Vol 11, No 25 (2018), Pagination: 1-13Abstract
Objectives: Commonly, agriculture in many areas of the world represents an example of fields that are affected hardly by climate changes. This work attempts to find and rank candidate plantation plans of a given set of crops that can be planted in a specific area with regard to the climate change. Methods/Statistical Analysis: This work represents a fuzzy-based approach. Firstly, a prediction process takes place in order to predict the incoming year values of climate variables like temperature, humidity and sun shining. In consequence, the crops climatic requirements are represented using the flexibility of fuzzy set theory. After that, the year under planning is scanned to find the more suitable plantation periods for each crop. Finally, the proposed approach finds a set of candidate plantation plans each is associated with an overall suitability degree greater than a predefined threshold. Findings: Many previous works attempt to find the more suitable areas for planting a specific crop or to modify the plantation dates to be more suitable. These works don’t care about the consequent planted crops in the same area which almost affect the overall outcome of the plantation process. In contrary, the proposed approach gives a ranked set of candidate plantation plans based on their overall suitability degrees. Accordingly, it helps farmers in selecting the more suitable plantation plan respecting climate change. Application/Improvements: An application is built respecting the proposed approach. Its database includes the area historical climatic data and the crops climatic requirements. The application starts predicting the incoming year climatic values. Consequently, it finds each crop suitable plantation periods which are used to get the candidate plantation plans with an overall suitability degree. An illustrative case study is presented showing the added value of the proposed approach compared with some other previous ones.References
- Humid RO, Mohammad AA, Mohammad AH. Consideration of Climate Conditions in Reservoir Operation Using Fuzzy Inference System (FIS). British Journal of Environment & Climate Change. 2013; 3(3): 444-63. Crossref
- Zane L. Fuzzy sets. Information and Control. 1995; 8:338-53.
- Salam MA, Mahmood MA, Awad YM, Hazman M, Bendary EN, Hassanien AE, Tolba MF, Saleh SM. Climate Recommender System for Wheat Cultivation in North Egyptian Sinai Peninsula. Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA. 2014; p. 121-30. Crossref.
- Branco A, Evsukoff A, Ebecken N. Generating fuzzy queries from weighted fuzzy classifier rules. ICDM workshop on Computational Intelligence in Data Mining. 2005; p. 21-8.
- Defang NJ, Manu I, Bime MJ, Tabi O, Defangn HF. Impact of climate change on crop production and development of Muyuka subdivision - Cameroon. International Journal of Agriculture, Forestry and Fisheries. 2014; 2(2):40-5.
- Moussa W, Patrick L, Seydou B. A Crop Model and Fuzzy Rule Based Approach for Optimizing Maize Planting Dates in Burkina Faso, West Africa. Journal of Applied Meteorology and Climatology. 2017; 53:598-613.
- Mohaddes SA, Mohayidin MG. Application of the Fuzzy Approach for Agricultural Production Planning in a Watershed, a Case Study of the Atrak Watershed, Iran. American-Eurasian Journal of Agricultural & Environmental Sciences. 2008; 3(4):636-48.
- Kumar P. Crop Yield Forecasting by Adaptive Neuro Fuzzy Inference System. Mathematical Theory and Modeling. 2011; 1(3):1-8.
- Hartati S, Sitanggang IS. A Fuzzy Based Decision Support System for Evaluating Land Suitability & Selecting Crops. Journal of Computer Science. 2010; 6(4):417-24. Crossref.
- Rajeshwar GJ, Bhalchandra P, Khmaitkar SD. Predicting Suitability of Crop by Developing Fuzzy Decision Support System. International Journal of Emerging Technology and Advanced Engineering. 2013; 3(2):1-9.
- El-Boraie G, El-Raman A. Optimizing water use efficiency of wheat under South Sinai condition (Wadi Sudr). Journal of Agricultural Science Mansoura University. 2009; 34(12):11477-88.
- Gadallah AM, Mohamed AM, Hefny HA. Fuzzy Query Approach for Crops Planting Dates Optimization Based on Climate Data. International Conference on Advanced Machine Learning Technologies and Applications. 2014; p. 436-45. Crossref.
- Mohammed AH, Gadallah AM, Hefny HA. Fuzzy query approach for crops planting dates adaptation with climate changes. 9th International Conference on Informatics and Systems. 2014; p. 52-60.Crossref.
- Gadallah AM, Mohammed AH. Fuzzy-based approach for reducing the impacts of climate changes on agricultural crops. Handbook of research on machine learning innovations and trends. IGI Global. 2017; p. 272-94. Crossref.
- Fuzzy based approach for discovering crops plantation knowledge from huge agro-climatic data respecting climate changes. Available from: https://www.springerprofessional.de/en/fuzzy-based-approach-for-discovering-crops-plantation-knowledge-/15583660. Date accessed: 02/04/2018.
- Yadav S. Climatic conditions for growing wheat. Important India. Avialable from: https://www.importantindia.com/12612/climatic-conditions-for-growing-wheat/. Date accessed: 02/04/2018.
- Kenanaonline: Wheat plantation-suitable climate. Available from: http://kenanaonline.net/page/1966. Date accessed: 14/10/2017.
- Central Administration for Agricultural Extension. Available from: https://caae-eg.com/index.php/2012-12-25-10-49-19.html. Date accessed: 27/03/2014.