优化玉米发芽预测随机森林和数据融合技术
Lili Wu1, Yuqing Xing2, Kaiwen Yang1
1College of Sciences, Henan Agricultural University, Zhengzhou, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究介绍了一种非破坏性方法,使用多源信息融合和随机森林 (RF) 算法来预测玉米种子发芽率. 射频模型实现了92.88%的准确性,为传统方法提供了更快,更可靠的替代方案.
科学领域:
- 农业科学 农业科学
- 生物技术是生物技术.
- 数据科学数据科学数据科学
背景情况:
- 传统的种子发芽测试耗时,可能会损害种子.
- 准确和快速的发芽预测对于农业效率至关重要.
- 开发非破坏性方法是种子科学的一个关键目标.
研究的目的:
- 开发一种快速,非破坏性的方法来预测玉米种子发芽率.
- 评估多源信息融合与机器学习算法相结合的有效性,用于此预测.
- 为了比较各种算法的性能,包括随机森林 (RF),与标准发芽测试.
主要方法:
- 在研究中使用了丹-958玉米品种.
- 收集的多源数据包括数字图像 (种子外观,内部裂) 和介电常数测量 (转换为电压).
- 从颜色,形状,纹理,裂数和规范电压等特征中设计的特征向量.
- 开发并测试了预测模型:随机森林 (RF),辐射基函数 (RBF),神经网络 (NN),支持矢量机 (SVM) 和极端学习机 (ELM).
主要成果:
- 随机森林 (RF) 模型在测试的算法中显示出卓越的性能.
- 射频实现了最高的预测准确度,达到92.88%.
- 射频模型表现出5.18秒的短训练时间,平均绝对误差 (MAE) 为0.913和根平均平方误差 (RMSE) 为1.163.
结论:
- 多源信息融合与随机森林 (RF) 算法相结合,为预测玉米种子发芽提供了一种可行且准确的方法.
- 这种方法为传统的发芽测试提供了一种非破坏性和高效的替代方案.
- 这些发现对改善种子质量评估和农业生产率有重大影响.
更多相关视频
10:01Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays
Published on: April 23, 2012
18.1K
05:55High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
Published on: June 16, 2018
6.9K
相关概念视频
02:16Light 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
01:19Survival Tree
60
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
60
01:03Prediction 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.
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