相关实验视频

Updated: Jan 18, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

基于改进的DeepLabv3的轻量级大米叶斑点细分模型

Jianian Li¹, Long Gao¹, Xiaocheng Wang¹

¹Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.

Frontiers in plant science

|September 8, 2025

概括

一个新的轻量级大米叶点细分模型 (MMPC-DeepLabv3+) 显著提高了疾病检测准确度,同时降低了计算成本. 这一进步使精准农业的有效现场部署和更好的水作物管理成为可能.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
机器学习机器学习

背景情况:

米是重要的粮食作物,但其对疾病的易感性需要有效的监测.
现有的叶点细分模型遭受了高计算开销,阻碍了实际的现场应用.
准确的细分对于诊断诸如大米爆发,棕色斑点和细菌叶片炎等疾病至关重要.

研究的目的:

为现场部署开发一种轻量级和高效的叶点位细分模型.
提高细分精度,特别是在过渡区和损伤边界.
为了减少计算复杂性和模型参数,在资源有限的环境中提高可用性.

主要方法:

开发了MMPC-DeepLabv3+,一个使用MobileNetV3_Large (MV3L) 作为骨干的轻量级模型.
整合了多尺度细节增强 (MSDE) 模块,并采用哈尔波段下采样,以改进边界和差距细分.
使用PagFm-Ghostconv功能融合 (PGFF) 模块与协调注意 (CA) 来减少计算开销并提高稳定性.
采用混合损失函数 (焦点损失+子损失) 来解决疾病图像中的类失衡.

主要成果:

在自然照明图像上,MMPC-DeepLabv3+ 实现了 81.23% 的平均交叉度 (MIoU) 和 89.79% 的平均像素精度 (MPA).
显著减少计算资源:9.695 G 失败和3.556 M 参数.
超过了基线DeepLabv3+在MIoU的1.89%和MPA的0.83%,分别减少了Flops和Params的93.1%和91.6%.

结论:

与DeepLabv3+,U-Net,PSPNet,HRNetV2和SegFormer等现有模型相比,MMPC-DeepLabv3+提供了精度和计算效率的卓越平衡.
该模型的轻量级设计和高性能为精密农业中的大米损伤细分制定了新的标准.
这项研究促进了人工智能在米种植中的疾病管理方面的实际应用.

关键词:

深度实验室V3 + 3 功能融合5 功能融合5 轻量级的4型车型叶疾病 1 叶疾病 1 分段化 2 分段化 2 分段化

更多相关视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

相关概念视频

Light Acquisition

Light Acquisition

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.

Reducing Line Loss

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...