子 (Cocos nucifera) 树疾病数据集:用于机器学习应用的疾病检测和分类的数据集
Sandip Thite1, Yogesh Suryawanshi1, Kailas Patil1
1Vishwakarma University, Pune, India.
Data in brief
|November 6, 2023
概括
一个由5798个子树图像组成的新数据集,有助于机器学习检测疾病. 该资源支持植物病理学和可持续子种植的进展.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物病理学 植物病理学
背景情况:
- 子树对热带经济至关重要,但易患各种疾病.
- 准确和早期的疾病检测对于有效的管理和产量保存至关重要.
- 现有的数据集可能缺乏强大的机器学习模型所需的多样性或规模.
研究的目的:
- 为检测子树疾病引入一个全面的数据集.
- 促进植物病理学机器学习模型的开发和评估.
- 通过改善疾病管理,支持子种植的可持续实践.
主要方法:
- 收集了5,798张子树的图像,显示了五种不同的疾病:芽根掉落,芽腐烂,灰色叶斑,叶腐烂和茎出血.
- 图像的分类,以实现疾病分类的监督学习.
- 数据集准备用于用于图像识别和分析的机器学习算法.
主要成果:
- 一个大规模,多样化的数据集,专门为子树疾病策划.
- 该数据集为培训和验证机器学习模型提供了一个基准.
- 能够对疾病的流行和特征进行定量分析.
结论:
- "子树疾病数据集"是农业研究和人工智能开发的宝贵资源.
- 促进了自动疾病诊断的进步,从而改善了作物管理.
- 促进全球子种植园的长期可持续性和经济可行性.
更多相关视频
08:20Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
Published on: October 27, 2023
1.5K
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.3K
相关概念视频
Classification of Illness
7.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.5K
Methods of Classification and Identification
19
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
19
Classification of Systems-I
190
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
190
Classification of Signals
482
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
482
Classification of Systems-II
150
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
150
Aggregates Classification
328
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
328
