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  1. 1
    Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project

    Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project von Nelson, Andrew, Setiyono, Tri, Rala, Arnel B, Quicho, Emma D, Raviz, Jeny V, Abonete, Prosperidad J, Maunahan, Aileen A, Garcia, Cornelia A, Bhatti, Hannah Zarah M, Villano, Lorena S


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  2. 2
    Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images

    Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images von Tanaka, Yu, Watanabe, Tomoya, Katsura, Keisuke, Tsujimoto, Yasuhiro, Takai, Toshiyuki, Tanaka, Takashi Sonam Tashi, Kawamura, Kensuke, Saito, Hiroki, Homma, Koki, Mairoua, Salifou Goube, Ahouanton, Kokou, Ibrahim, Ali, Senthilkumar, Kalimuthu, Semwal, Vimal Kumar, Matute, Eduardo Jose Graterol, Corredor, Edgar, El-Namaky, Raafat, Manigbas, Norvie, Quilang, Eduardo Jimmy P, Iwahashi, Yu, Nakajima, Kota, Takeuchi, Eisuke, Saito, Kazuki

    Veröffentlicht in Plant phenomics

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  3. 3
    Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan

    Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan von Boschetti, Mirco, Nelson, Andrew, Nutini, Francesco, Manfron, Giacinto, Busetto, Lorenzo, Barbieri, Massimo, Laborte, Alice, Raviz, Jeny, Holecz, Francesco, Mabalay, Mary Rose O


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