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Daniel Marc G dela Torre, Jay Gao & Cate Macinnis-Ng. (2021) Remote sensing-based estimation of rice yields using various models: A critical review. Geo-spatial Information Science 24:4, pages 580-603.
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Pablo Pozzobon de Bem, Osmar Abílio de Carvalho Júnior, Osmar Luiz Ferreira de Carvalho, Roberto Arnaldo Trancoso Gomes, Renato Fontes Guimarāes & Concepta Margaret McManus Pimentel. (2021) Irrigated rice crop identification in Southern Brazil using convolutional neural networks and Sentinel-1 time series. Remote Sensing Applications: Society and Environment 24, pages 100627.
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Haixiao Ge, Fei Ma, Zhenwang Li, Zhengzheng Tan & Changwen Du. (2021) Improved Accuracy of Phenological Detection in Rice Breeding by Using Ensemble Models of Machine Learning Based on UAV-RGB Imagery. Remote Sensing 13:14, pages 2678.
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Hugo N. Bendini, Leila M. G. Fonseca, Anderson R. Soares, Philippe Rufin, Marcel Schwieder, Marcos A. Rodrigues, Raian V. Maretto, Thales S. Korting, Pedro J. Leitao, Ieda D. A. Sanches & Patrick Hostert. (2020) Applying A Phenological Object-Based Image Analysis (Phenobia) for Agricultural Land Classification: A Study Case in the Brazilian Cerrado. Applying A Phenological Object-Based Image Analysis (Phenobia) for Agricultural Land Classification: A Study Case in the Brazilian Cerrado.
Lingyue Wang, Meiling Liu, Xiangnan Liu, Ling Wu, Peng Wan & Chuanyu Wu. (2020) Pretrained convolutional neural network for classifying rice-cropping systems based on spatial and spectral trajectories of Sentinel-2 time series. Journal of Applied Remote Sensing 14:01, pages 1.
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