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Prediction of fingerling biomass with deep learning
Affiliation:1. College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China;2. Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China;3. Zhaotong University, Zhaotong 657000, China;1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;1. Laboratory of Integrative Research in Biodiversity (PIBiLab), Centro de Ciências Biológicas e da Saúde, Universidade Federal de Sergipe, São Cristóvão-SE 49100-000, Brazil;2. Departamento de Sistemática e Ecologia, Centro de Ciências Exatas e da Natureza, Universidade Federal da Paraíba, João Pessoa, PB 58059-900, Brazil;1. Tarbiat Modares University, Faculty of Natural sciences, Department of Environment Science, Tehran, Iran;2. Tarbiat Modares University, Faculty of Natural sciences, Department of Forestry, Tehran, Iran;3. Caspian Forest Tree Seed Center, Forests, Rangelands and Watershed Organization, Mahmudabad, Mazandaran, Iran;4. Department of Biology and Botanic Garden, University of Fribourg, Chemin du Musée 10, CH-1700 Fribourg, Switzerland;5. Natural History Museum Fribourg, Chemin du Musée 6, CH-1700 Fribourg, Switzerland;6. Eastern China Conservation Centre for Wild Endangered Plant Resources, Shanghai Chenshan Botanical Garden, 3888 Chenhua Road, Songjiang, 201602 Shanghai, China;7. Institute of Dendrology, Polish Academy of Sciences, Parkowa 5, 62-035 Kórnik, Poland
Abstract:
Keywords:Pintado Real  Deep Belief Network  Convolutional Neural Networks  Computer vision  Machine learning
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