葡萄数据集:用于通过环境参数对机器学习应用进行疾病预测和分类的数据集
Apeksha Gawande1, Swati Sherekar1
1Sant Gadge Baba University, SGBAU, Amravati, India.
Data in brief
|June 24, 2024
概括
这项研究引入了用于葡萄疾病检测的新数据集,利用环境传感器数据来训练机器学习模型,以识别常见的葡萄疾病,如粉状菌和细菌叶斑.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 数据科学数据科学数据科学
背景情况:
- 葡萄是水果和葡萄酒行业的关键全球作物.
- 葡萄病对产量,质量和经济价值都有重大影响.
- 有效的疾病管理对于可持续的葡萄种植至关重要.
研究的目的:
- 引入"葡萄疾病数据集",用于基于机器学习的疾病检测.
- 为开发自动化葡萄疾病识别系统提供资源.
- 支持研究,以提高疾病管理的准确性和效率.
主要方法:
- 该数据集包括10,000个环境参数记录 (温度,湿度,叶子湿度).
- 数据被分类和分类用于机器学习模型训练.
- 机器学习技术,如特征提取和模式识别是适用的.
主要成果:
- 该数据集涵盖了常见的葡萄疾病,包括粉状菌,状菌和细菌叶斑.
- 它促进了用于早期和准确的疾病识别算法的开发.
- 提高疾病检测准确性和效率的潜力.
结论:
- "葡萄疾病数据集"是推动葡萄疾病自动检测的宝贵工具.
- 应用于此数据集的机器学习可以增强疾病管理策略.
- 该资源支持全球水果和葡萄酒行业的可持续性.
相关概念视频
Aggregates Classification
317
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...
317
Classification of Systems-I
179
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:
179
Classification of Systems-II
139
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,
139
Statistical Methods for Analyzing Epidemiological Data
349
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
349
Classification of Leukocytes
1.8K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
1.8K
End Point Prediction: Gran Plot
314
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
314


