一个在田间使用的花生和杂草检测模型,基于BEM-YOLOv7-tiny
Yong Hua1, Hongzhen Xu1,2, Jiaodi Liu1,2
1College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541004, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
一个新的BEM-YOLOv7微型模型可以准确地检测不同生长阶段的花生和杂草,从而实现智能机械除草. 这一进步提高了现场应用的精度和回忆.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 在花生田中的杂草特征随着生长阶段的不同而有很大变化,需要适应性的检测模型.
- 目前的机械除草系统需要改进不同除草期的花生和杂草的实时识别.
研究的目的:
- 开发一个通用的花生和杂草检测模型,用于花生田中的智能机械除草.
- 为了提高杂草识别和定位在各种除草阶段的准确性和效率.
主要方法:
- 提出了BEM-YOLOv7微型目标检测模型,其中包括ECA,MHSA和BiFPN模块.
- 利用SIoU损失函数来优化模型融合和现场检测性能.
- 基于精度,回忆,mAP,F1得分,定位错误和检测速度评估模型性能.
主要成果:
- 与原始YOLOv7-tiny相比,BEM-YOLOv7-tiny模型在杂草和所有目标上都展示了改进的性能指标.
- 对于杂草目标,精度,回忆,mAP和F1的改进分别为1.6%,4.9%,4.4%和3.2%.
- 显示了低于16像素的花生定位偏移误差和33.8 f/s的检测速度,满足实时要求.
结论:
- BEM-YOLOv7-tiny模型为实时花生和杂草检测和定位提供了一个强大的解决方案.
- 这项技术为在花生种植中推进智能机械除草提供了必要的技术支持.
- 该模型适应不同杂草期的适应性解决了精准农业的关键需求.
相关概念视频
02:16Light Acquisition
8.5K
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.
8.5K
00:56Extraction: Advanced Methods
450
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
450
01:05Difference from Background: Limit of Detection
6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
6.4K
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
72
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
72