YOLOv11-GSF:一个优化的深度学习模型用于农业中的草成熟度检测
Haoran Ma1, Qian Zhao1, Runqing Zhang1
1College of Software, Shanxi Agricultural University, Taigu, China.
Frontiers in plant science
|September 5, 2025
概括
这项研究介绍了YOLOv11-GSF,这是一种在具有挑战性的温室条件下实时检测草成熟度的先进算法. 它的准确性和效率高于现有的水果质量评估方法.
科学领域:
- 计算机视觉
- 农业技术
- 机器学习
背景情况:
- 在温室中检测草成熟度是很困难的,因为草的密,遮蔽和照明的变化.
- 现有的方法在效率,计算成本和小目标的准确性方面扎.
研究的目的:
- 在复杂的环境中开发实时算法来准确检测草的成熟度.
- 通过提高效率和准确性来改进现有的检测方法.
主要方法:
- 引入了YOLOv11-GSF,其中包含了Ghost Convolution (GhostConv) 以实现高效的特征映射.
- 使用具有自动移动点卷积 (SMPConv) 和卷积门式线性单元 (CGLU) 的C3K2-SG模块进行详细的特征捕捉.
- 实现了F-PIoUv2损失函数以加速融合和优化分类.
主要成果:
- YOLOv11-GSF的平均精度为97. 8%,准确度为95. 99%,回忆率为93. 62%.
- 与原始YOLOv11相比,显著改善,精度提高了1. 8%,精度提高了1. 3%,回忆能力提高了2.1%.
- 与其他算法相比, 显示出更高的识别精度和稳定性.
结论:
- YOLOv11-GSF提供了一种实用且高效的草成熟度检测解决方案.
- 该算法有效地解决了复杂的温室环境带来的挑战.
- 提高了农业自动化质量评估系统的性能.
相关概念视频
Light Acquisition
8.6K
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.
8.6K
Improving Translational Accuracy
11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
Extraction: Advanced Methods
524
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...
524
Plant Breeding and Biotechnology
19.7K
Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
19.7K
Force Classification
1.6K
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,...
1.6K
Aggregates Classification
378
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...
378


