The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of SYSTEMS ENGINEERING, APPLICATIONS AND DEVELOPMENT
Transactions ID Number: 20-563
Full Name: Kefaya Qaddoum
Position: Ph.D. Candidate
Age: ON
Sex: Female
Address: Tocil Block 8 Flat44 Warwick University
Country: UNITED KINGDOM
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E-mail address: k.s.qaddoum@warwick.ac.uk
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Title of the Paper: Adaptive Neuro-Fuzzy Inference System based prediction modeling for Crop Yield Prediction
Authors as they appear in the Paper: Kefaya Qaddoum, Evor Hines, Daciana Illiescu
Email addresses of all the authors: k.s.qaddoum@warwick.ac.uk,E.L.Hines@warwick.ac.uk,D.D.Iliescu@warwick.ac.uk
Number of paper pages: 8
Abstract: Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to explore the dynamics of neural networks in forecasting crop (tomato) yield using environmental variables; here we aim at giving accurate yield amount. We use the Adaptive Neuro-Fuzzy Inference System (ANFIS). The input to ANFIS is several parameters derived from the crop growth model (temperature, Co2, vapor pressure deficit (VPD), yield, and radiation). ANFIS has only one output node, the yield. One of the difficult issues in predicting yield is that remote sensing data do not go long back in time. Therefore any predicting effort is forced to use a very restricted number of past years in order to construct a model to forecast future values. The system is trained by leaving one year out and using all the other data. We then evaluate the deviation of our estimate compared to the yield of the year that is left out. The proce!
dure is applied to all the years and the average forecasting accuracy is given. The implementation framework utilizes MATLAB's standard configuration. Despite the simple design procedure, the simulated test prediction indicates the capability of the approach in achieving the desired performance and improving prediction accuracy.
Keywords: Neural Networks, Adaptive-network-based fuzzy inference system (ANFIS), Crop, yield forecasting.
EXTENSION of the file: .pdf
Special (Invited) Session: ADAPTIVE NEURO-FUZZY MODELING FOR CROP YIELD PREDICTION
Organizer of the Session: 650-530
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