改善U-net网络,用于在田间的玉米种植阶段对玉米和杂草进行语义细分
Jiapeng Cui1,2,3, Feng Tan2, Nan Bai2
1College of Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
Frontiers in plant science
|February 26, 2024
概括
智能机械除草需要精确的杂草检测. 改进的语义细分网络RDS_Unet精确地识别了玉米田中的杂草,提高了自动除草效率并减少了作物损害.
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:
- 杂草控制对作物产量至关重要,人们对智能机械解决方案的兴趣越来越大.
- 准确的杂草识别对于有效的自动除草系统至关重要.
研究的目的:
- 开发和评估一个新的语义细分网络,RDS_Unet,用于在玉米苗田中精确检测杂草.
- 在复杂的田间条件下提高杂草识别的准确性和效率.
主要方法:
- 提出了RDS_Unet,这是一个增强的U-net架构,利用ResNeXt-50进行特征提取.
- 在网络的解码器阶段内嵌有可变形卷曲和并发空间和通道挤压和激发模块.
- 在定制的玉米苗草语义细分数据集 (CGSSD) 上训练和评估网络.
主要成果:
- 与CGSSD数据集上的现有模型 (U-net,Pspnet,DeeplabV3) 相比,RDS_Unet实现了更高的性能.
- 该网络在欧盟的平均交叉点 (MIoU) 为82.36%,精度为91.36%,回忆率为89.45%.
- 展示了每秒12.6的检测速度,并进行了废弃研究,证实了个别改进的有效性.
结论:
- 在农业环境中,RDS_Unet模型为准确的杂草细分提供了强大的解决方案.
- 为开发智能机械除草设备提供理论和技术支持.
- 提高了自动除草控制的潜力,为可持续农业做出了贡献.
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
02:35Key Elements for Plant Nutrition
18.7K
Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
18.7K