Stabilizing the Predictive Performance for Ear Emergence in Rice Crops Across Cropping Regions
- 20 February 2021
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 8 references indexed in Scilit:
- Leveraging AI in Service Automation Modeling: From Classical AI Through Deep Learning to Combination ModelsPublished by Springer Science and Business Media LLC ,2019
- Accounting for soil moisture improves prediction of flowering time in chickpea and wheatScientific Reports, 2019
- Support vector machine-based open crop model (SBOCM): Case of rice production in ChinaSaudi Journal of Biological Sciences, 2017
- Random Forests for Global and Regional Crop Yield PredictionsPLOS ONE, 2016
- Wheat yield prediction using machine learning and advanced sensing techniquesComputers and Electronics in Agriculture, 2016
- Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological modelJournal of Experimental Botany, 2014
- Improving predictions of developmental stages in winter wheat: a modified Wang and Engel modelAgricultural and Forest Meteorology, 2003
- Modelling and prediction of developmental process in rice. I. Structure and method of parameter estimation of a model for simulating developmental process toward heading.Japanese Journal of Crop Science, 1990