一个快速的里埃卷积深度神经网络,用于准确和可解释的对小麦黄色生和缺乏的区别,从Sentinel-2时间序列数据中获取
Yue Shi1, Liangxiu Han2, Pablo González-Moreno3
1School of Electronic and Electrical Engineering, University of Leeds, Leeds, United Kingdom.
Frontiers in plant science
|October 20, 2023
概括
这项研究引入了一个新的快速里埃卷积神经网络 (FFCNN),用于高效和可解释的植物应激检测. 通过遥感数据,FFCNN准确地区分小麦黄色生和缺乏症.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
背景情况:
- 准确的植物应激检测对于作物产量保护和有针对性的干预措施至关重要.
- 现有的深度学习模型在计算效率和区分类似的压力症状方面面临挑战.
- 在植物压力检测中,宿主压力相互作用的解释性仍然是一个重大障碍.
研究的目的:
- 提出一种新的快速里埃卷积神经网络 (FFCNN),用于准确和可解释地检测植物压力.
- 解决计算效率低下和错误分类问题,以区分具有类似症状的植物应力.
- 在植物压力检测中提高宿主压力相互作用的解释性.
主要方法:
- 开发了一种FFCNN模型,结合了快速里埃卷积块和囊特征编码器.
- 利用快速的富里埃转换内核,以高效地捕捉全球和本地植物应激反应.
- 实现基于光化学植被指数的过器,用于预处理Sentinel-2时间序列数据以消除噪音.
主要成果:
- FFCNN模型在检测和区分小麦黄色生和缺乏症方面表现得非常准确.
- 该模型在分类准确性,稳定性和对Sentinel-2时间序列数据的概括性方面取得了竞争优势.
- 来自囊特征编码器的高级向量特征提供了对主机压力相互作用的见解.
结论:
- 拟议的FFCNN提供了一种高效和可解释的解决方案,用于使用遥感检测工厂压力.
- 该模型能够区分类似的压力并解释相互作用,这意味着精密农业的进步.
- 这种方法具有显著的潜力,可以通过自动化压力监测来改善作物管理和产量保护.
更多相关视频
相关概念视频
02:16Light Acquisition
8.5K
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.
8.5K
02:35Key Elements for Plant Nutrition
18.8K
Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
18.8K