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相关概念视频

Light Acquisition02:16

Light Acquisition

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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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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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相关实验视频

Updated: Feb 26, 2026

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

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一个新的深度学习框架用于田间小麦产量预测.

M Lokeshwari1,2, Girish Kumar Jha3, A Praveenkumar1,2

  • 1The Graduate School, ICAR-Indian Agricultural Research Institute, Pusa, New Delhi, India.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
|February 25, 2026
PubMed
概括

一个新的深度神经网络,通过遗传算法进行优化,使用近距离传感数据准确预测小麦产量. 这种先进的模型超越了传统的作物监测和产量估计方法.

相关实验视频

Last Updated: Feb 26, 2026

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.9K

科学领域:

  • 农业科学 农业科学
  • 机器学习 机器学习
  • 遥感 遥感 遥感 遥感

背景情况:

  • 近距离传感技术通过光谱植被指数提供快速,非破坏性的作物监测.
  • 准确的收割前小麦产量预测对于农业管理和育种计划至关重要.

研究的目的:

  • 开发和评估一个基因算法优化的深度神经网络,用于田间小麦产量预测.
  • 将拟议的深度学习模型与传统机器学习算法的性能进行比较.

主要方法:

  • 利用近距离传感数据,包括规范差异植被指数 (NDVI),树冠温度 (CT) 和植物高度 (PH).
  • 开发了一个通过遗传算法 (GA) 优化的深度神经网络 (DNN).
  • 在灌和雨水条件下,对3350种不同种类的小麦生殖质进行了培训和验证.

主要成果:

  • 在小麦产量预测中,GA优化的DNN显著优于随机森林回归 (RFR),LASSO和SVR.
  • 在五个生长阶段进行的NDVI测量显示出强大的预测能力 (R2 ≥60%灌,R2 ≥50%雨水).
  • 随机森林回归确定了影响谷物产量预测的关键特征.

结论:

  • 使用近距离传感数据的GA优化的DNN为准确的小麦产量预测提供了强大而可扩展的解决方案.
  • 这种方法代表了GA优化的DNN与近距离传感用于印度农业作物产量预测的首次应用.
  • 该模型支持高效的基因型选择,并通过改进产量估计,为可持续发展目标做出贡献.