通过使用视觉转换器进行多阶段深度合体学习来进行不可抗拒的类病疾病分类
Nagineni Venkata Sireesha1,2, Gillala Rekha3, Reem A Almenweer4
1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, Telangana, India.
Scientific reports
|November 18, 2025
概括
这项研究引入了一种深度组合模型,用于使用现场图像早期检测类疾病. 将纹理特征与深度学习相结合,显著提高了子和的诊断准确性.
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:
- 农作物疾病导致农业遭受重大损失,并威胁到粮食安全.
- 准确和早期的疾病诊断对于有效的作物管理至关重要.
- 现有的方法可能难以应对现场条件的变化和微妙的疾病指标.
研究的目的:
- 开发一个强大的,用于早期病诊断的自动化系统.
- 通过将传统的纹理特征与深度学习模型相结合,提高疾病检测的准确性.
- 创建一个轻量级和可部署的解决方案,用于果园内疾病的识别.
主要方法:
- 使用子和的现场图像开发了一个三级深层合奏模型.
- 局部二进制模式 (LBP) 和灰色级共发生矩阵 (GLCM) 纹理特征被提取并与深层嵌入相结合.
- 该模型集成了卷积神经网络 (CNN) 和视觉变换器 (ViT),最后阶段使用支持矢量机器 (SVM).
- 数据增强,分层分割,规范化和主要组件分析 (PCA) 用于预处理和缩小维度.
主要成果:
- 整体模型实现了高诊断准确率:99%,97%
- 整合LBP和GLCM纹理先验显著改善了单个脊柱深度学习模型.
- 第二阶段的视觉转换器增强了长距离的上下文理解,提高了第一阶段的结果高达98%.
- 最终的管道证明了在果园中检测类疾病的卓越稳定性和概括能力.
结论:
- 将手工制作的纹理特征 (LBP,GLCM) 与深度学习 (CNNs,ViTs) 结合起来,为病诊断提供了一种强大的方法.
- 拟议的三级组合方法为现实世界农业应用提供了一种轻量级但非常准确的解决方案.
- 这种方法有效地解决了深度学习模型在从可变现场数据中捕获微妙的纹理信息方面的局限性.
相关概念视频
01:17Classification of Illness
8.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.5K
01:26Classification of Systems-I
543
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
543
01:31Classification of Systems-II
447
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
447
01:29Aggregates Classification
956
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...
956
01:22Force Classification
2.3K
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,...
2.3K
01:30Classification of Signals
1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K