Long-Term Yield Prediction of Greenhouse Sweet Pepper Crops
- Reem Al-HalimiAffiliated withRensselaer Polytechnic InstituteUniversity of Waterloo
- , Medhat MoussaAffiliated withAmerican UniversityUniversite de MonctonUniversity of WaterlooUniversity of Guelph
In this paper, a new model for predicting the yield of greenhouse sweet peppers (Capsicum annuum L.) is presented. The model can provide long-term prediction up to 7 weeks in advance with the same accuracy it can predict yield one week in advance. It uses both past and expected environmental readings as well as physiological data as input to a specially designed artificial neural network. The model was tested using 4 years of data that was obtained from commercial pepper growers. Short-term prediction accuracy (one week) is consistent with other predictive models in the literature for sweet peppers. This validates our long-term results.
KeywordsBell peppers crop models greenhouse long-term yield prediction neural networks
- Long-Term Yield Prediction of Greenhouse Sweet Pepper Crops
- Open Access
- Available under Open Access This content is freely available online to anyone, anywhere at any time.
GSTF Journal on Agricultural Engineering (JAE)
- Online Date
- February 2016
- Online ISSN
- Global Science and Technology Forum
- Additional Links
- Bell peppers
- crop models
- long-term yield prediction
- neural networks
- Author Affiliations
- 1. Rensselaer Polytechnic Institute, New York, U.S.
- 2. University of Waterloo, Waterloo, Canada
- 3. American University, Cairo, Egypt
- 4. Universite de Moncton, Moncton, Canada
- 5. University of Guelph, Ontario, Canada