Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Light Acquisition02:16

Light Acquisition

8.4K
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.
8.4K
What is Climate?01:16

What is Climate?

18.4K
Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
18.4K
Responses to Drought and Flooding02:41

Responses to Drought and Flooding

10.6K
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
10.6K
Multiple Regression01:25

Multiple Regression

2.9K
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...
2.9K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

11.4K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
11.4K
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Relational Modelling for Automotive Cybersecurity: Structural Transition and Graph-Topology-Based CAN Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same author

Optimized hybrid neural hierarchical interpolation time series with STL for flow forecasting in hydroelectric power plants.

Scientific reports·2026
Same author

Autonomous Waste Classification Using Multi-Agent Systems and Blockchain: A Low-Cost Intelligent Approach.

Sensors (Basel, Switzerland)·2025
Same author

Intelligent sensors in assistive systems for deaf people: a comprehensive review.

PeerJ. Computer science·2024
Same author

Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD·2024
Same author

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation.

Sensors (Basel, Switzerland)·2024

相关实验视频

Updated: Jun 5, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

11.4K

通过文本挖掘探索农产品预测方法的当前趋势:统计和人工智能方法的发展.

Luana Gonçalves Guindani1, Gilson Adamczuk Oliveirai1, Matheus Henrique Dal Molin Ribeiro1

  • 1Industrial & Systems Engineering Graduate Program (PPGEPS), Federal University of Technology - Parana (UTFPR), Via Do Conhecimento, KM 01 - Fraron, Pato Branco, PR, 85503-390, Brazil.

Heliyon
|December 10, 2024
PubMed
概括

本研究确定了农业企业时间序列的关键预测方法,其中机器学习混合和统计模型是最普遍的. 它通过突出农业商品预测的文献差距来帮助决策.

关键词:
农业企业是农业企业.农业大宗商品 农业大宗商品预测 预测 预测 预测潜在的迪里克莱特分配.机器学习是机器学习.文本挖掘 (Text Mining) 是一种文字挖掘方式.

更多相关视频

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

9.2K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.7K

相关实验视频

Last Updated: Jun 5, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

11.4K
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

9.2K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.7K

科学领域:

  • 农业经济学 农业经济学
  • 数据科学数据科学数据科学
  • 图书统计学 图书统计学

背景情况:

  • 农业是面临供应链风险的全球经济驱动力.
  • 数学模型对于农业企业管理中的预测至关重要.
  • 无法控制的因素需要强大的风险管理策略.

研究的目的:

  • 在农业企业预测研究中自动识别主题.
  • 构建2015-2022年相关研究的文献组合.
  • 分析和分类农业商品分析中的预测方法.

主要方法:

  • 系统的图书统计分析与潜伏迪里克莱特分配 (LDA) 相结合.
  • 基于预测模型类型的30篇文章的分类:机器学习 (ML),ML-NN,ML-Ensemble,ML-混合和统计.
  • 专注于用于农产品时间分析的方法.

主要成果:

  • 确定的主题是"应用于农业企业时间序列的预测方法".
  • 机器学习混合 (41.95%) 和统计 (29.31%) 模型是最常用的.
  • 机器学习与神经网络 (ML-NN) 紧随其后的是14.94%.

结论:

  • 在农业企业的预测方法中发现了文献上的差距.
  • 提供了关于预测方法的实用见解,以改善决策.
  • 强调了农业时间序列分析中混合和统计方法的普遍性.