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Updated: Jul 21, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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精准农业中的深度学习:人工生成的VNIR图像细分用于预测果早期收获后腐烂

Nikita Stasenko1, Islomjon Shukhratov1, Maxim Savinov2

  • 1Skolkovo Institute of Science and Technology, 121205 Moscow, Russia.

Entropy (Basel, Switzerland)
|July 29, 2023
PubMed
概括

本研究引入了一种使用生成对抗网络 (GAN) 和卷积神经网络 (CNN) 早期检测果腐烂和真菌区域的新方法. 该方法通过分析合成和细分的VNIR成像数据来加强食品质量控制.

关键词:
在美国,CNN是CNN.没有了,没有了,没有了.菌是一种真菌.图像处理是图像处理的过程.收获后的腐烂 收获后的腐烂精准农业 精准农业 精准农业

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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