一个框架,用于通过回归来计数单面板花,并进行多任务学习
Jiaquan Lin1, Jun Li1,2,3, Zhe Ma1
1College of Engineering, South China Agricultural University, Guangzhou 510642, China.
Plant phenomics (Washington, D.C.)
|April 17, 2024
概括
这项研究介绍了FlowerNet,这是一个深度学习模型,用于准确地计数花. 这种自动化方法改进了手动计数和密度图方法,用于估计花的开花情况.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 精确的花量化对于评估树木生长和进行表型研究至关重要.
- 手动计数花朵是劳动密集型,容易出现错误,而现有的自动化方法则在密集的花和背景干扰方面扎.
- 需要先进的深度学习技术来可靠地估计花的数量,特别是在单个花层面.
研究的目的:
- 开发和评估一个新的深度学习框架,FlowerNet,用于精确的自动计数小,密集的雄性花在花.
- 通过最小化背景干扰来解决基于当前密度图的方法的局限性.
- 提供一个强大的工具来估计花的数量,并支持果园管理.
主要方法:
- 采用了两阶段的框架:YOLACT++用于单独的litchi panicle细分,其次是FlowerNet用于每个细分的panicle内的花数.
- FlowerNet使用多任务学习方法来进行密度图回归,有效地整合前景和背景信息以提高像素级准确性.
- 使用花数据建立了一个回归方程,以验证FlowerNet的性能与手动计数相比.
主要成果:
- 在构建的花朵数据集上,FlowerNet实现了 47.71 的平均绝对误差 (MAE) 和 61.78 的根平均平方误差 (RMSE).
- 建立的回归方程显示了FlowerNet预测的花数和手动计数之间的强烈相关性,确定系数 (R2) 为0.81.
- 拟议的方法有效地克服了背景干扰,为密集的花花量化提供了更高的准确性.
结论:
- 开发的FlowerNet算法为自动估计花的数量提供了一个有希望的解决方案.
- 这种深度学习方法为果园管理提供了有价值和可靠的参考,特别是在关键的开花期间.
- 该框架提高了表型研究的精度,通过准确量化花上单个花的精确量化.
更多相关视频
11:38Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
Published on: October 4, 2024
548
08:04Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
Published on: April 23, 2020
6.8K
相关概念视频
Light 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
Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Multi-input and Multi-variable systems
106
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...
106
Force Classification
1.2K
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,...
1.2K
Aggregates Classification
317
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...
317
