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Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

机译：卷积神经网络精确地预测无人机图像中植物物种和社区的覆盖分数

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Teja Kattenborn; Jana Eichel; Susan Wiser; Larry Burrows; Fabian E. Fassnacht; Sebastian Schmidtlein;
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1. Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery [J] . Teja Kattenborn, Jana Eichel, Fabian Ewald Fassnacht Scientific reports. . 2019,第1期

机译：卷积神经网络能够高分辨率UAV图像实现植物物种和社区的高效，准确和细粒度的细分
2. Yield estimation of soybean breeding lines under drought stress using unmanned aerial vehicle-based imagery and convolutional neural network [J] . Zhou Jing, Zhou Jianfeng, Ye Heng, Biosystems Engineering . 2021,第1期

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3. An Enhanced Randomly Initialized Convolutional Neural Network for Columnar Cactus Recognition in Unmanned Aerial Vehicle imagery [J] . Safa Ben Atitallah, Maha Driss, Wadii Boulila, Procedia Computer Science . 2021,第a期

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4. Detecting Wildlife in Unmanned Aerial Systems Imagery Using Convolutional Neural Networks Trained with an Automated Feedback Loop [C] . Connor Bowley, Marshall Mattingly, Andrew Barnas, International conference on computational science . 2018

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5. Convolutional Neural Network-based Vision Systems for Unmanned Aerial Vehicles [D] . Verbickas, Rytis 2013

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6. A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery [O] . Huasheng Huang, Jizhong Deng, Yubin Lan, -1

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7. Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks [O] . Ovidiu Csillik, John Cherbini, Robert Johnson, 2018

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Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

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