I work on the estimation and prediction of vegetation traits (including biomass, leaf area, nutrition quality) with remote- and proxy-sensing, crop and statistical models. My main focus is to apply these methods to forage crops and develop models that can be further used as decision support tools by farmers and advisors.
Sashuang Sun, Ning Liang, Zhiyu Zuo, David Parsons, Julien Morel, Jiang Shi, Zhao Wang, Letan Luo, Lin Zhao, Hui Fang, Yong He and Zhenjiang Zhou, 2021. Estimation of Botanical Composition in Mixed Clover–Grass Fields Using Machine Learning-Based Image Analysis. Frontiers in Plant Science, https://doi.org/10.3389/fpls.2021.622429
Morel, J., Parsons, D., Halling, M.A., Kumar, U., Peake, A., Bergkvist, G., Brown, H., Hetta, M., 2020. Challenges for Simulating Growth and Phenology of Silage Maize in a Nordic Climate with APSIM. Agronomy. https://doi.org/10.3390/agronomy10050645
Zhou, Z., Morel, J., Parsons, D., Kucheryavskiy, S.V., Gustavsson, A.-M., 2019. Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2019.03.038
Morel, J., Jay, S., Féret, J.-B., Bakache, A., Bendoula, R., Carreel, F., Gorretta, N., 2018. Exploring the potential of PROCOSINE and close-range hyperspectral imaging to study the effects of fungal diseases on leaf physiology. Scientific Reports. https://doi.org/10.1038/s41598-018-34429-0
Jay, S., Gorretta, N., Morel, J., Maupas, F., Bendoula, R., Rabatel, G., Dutartre, D., Comar, A., Baret, F., 2017. Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.06.008
Morel, J., Todoroff, P., Bégué, A., Bury, A., Martiné, J.-F., Petit, M., 2014. Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island. Remote Sensing. https://doi.org/10.3390/rs6076620