一个对所有:走向统一的计数谷物作物头部模型,基于几次学习
Qiang Wang1, Xijian Fan1, Ziqing Zhuang1
1Nanjing Forestry University, Nanjing 210037, China.
Plant phenomics (Washington, D.C.)
|December 16, 2024
概括
谷物作物数量计算网 (CHCNet) 提供了一种统一的,短时间的学习方法,用于准确计算多种谷物作物数量,降低标签成本,提高跨不同作物品种的概括性.
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:
- 准确的谷物作物人数计数对于全球粮食安全和谷物产量估计至关重要.
- 目前的方法缺乏通用性,专注于特定的作物品种,需要广泛的标签.
研究的目的:
- 开发一个统一的模型,谷物作物数量计算网 (CHCNet),用于使用少数射击学习计算多作物数量.
- 提高模型的概括性,降低数据注释的成本.
主要方法:
- 采用了精致的视觉编码器和分段任何模型 (SAM) 来强调作物头部和减少背景噪音.
- 引入了一个多级特征交互模块,用于规模不变特征学习的相似度量.
- 使用了两阶段的培训程序:潜伏特征挖掘,然后进行域特定特征提取以推断.
主要成果:
- 与最先进的方法相比,CHCNet在六个不同的数据集 (地面和无人机图像) 上展示了优越的交叉作物概括.
- 实现了较低的平均绝对误差 (MAE):玉米为9.96/9.38,为13.94,大米为7.94,混合作物为15.62.
- 该模型有效地处理不同规模的作物头大小和形状的变化.
结论:
- CHCNet提供了一个强大的和可通用的解决方案,用于计算多种谷物作物类型,减少注释工作.
- 拟议的方法显著提高了自动作物监测和产量预测能力.
相关概念视频
Light 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
Aggregates Classification
303
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...
303
Force Classification
1.1K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.1K
Multi-input and Multi-variable systems
96
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
96
Introduction to Seed Plants
60.6K
Most plants are seed plants—characterized by seeds, pollen, and reduced gametophytes. Seed plants include gymnosperms and angiosperms.
60.6K
Cluster Sampling Method
11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K


