相关实验视频
Updated: Sep 14, 2025
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
9.4K
一个新的框架GRCornShot用于玉米疾病检测,使用一些射击学习与原型网络.
Ruchi Rani1,2, Jayakrushna Sahoo3, Sivaiah Bellamkonda3
1Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam, Kerala, 686635, India. ruchiasija20@gmail.com.
Scientific reports
|July 21, 2025
概括
这项研究介绍了GRCornShot,这是一种用于诊断玉米疾病的几次射击学习模型. 它以最小的数据实现了高精度,解决了农业传统深度学习的局限性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 准确及时检测植物疾病对全球粮食安全至关重要.
- 植物疾病诊断的深度学习模型需要大量的标记数据,这对现实世界的应用构成了挑战.
研究的目的:
- 开发一种新型模型,GRCornShot,用于使用几次射击学习诊断玉米疾病.
- 解决数据稀缺问题,培训植物疾病分类的深度学习模型.
主要方法:
- 拟议的GRCornShot模型使用原型网络和度量学习.
- 集成的Gabor过器与ResNet-50骨干进行增强的纹理特征提取.
- 在模型培训和评估中采用了几次射击的学习策略 (4-way N-shot).
主要成果:
- GRCornShot实现了很高的分类准确率:96.19% (2次射击),96.54% (3次射击),96.90% (4次射击) 和97.89% (5次射击).
- 证明了有效的玉米疾病分类,显著减少了标签数据要求.
- 突出了Gabor过器在纹理特征提取中的稳定性,以提高性能.
结论:
- 在农业应用中,近距离学习为植物疾病检测提供了一个有希望的解决方案.
- GRCornShot提供了一种精确且数据效率高的方法来诊断玉米疾病.
- 该模型的性能表明其在精密农业中的实际实施潜力.
相关概念视频
01:18Introduction to Learning
534
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
534
01:26Classification of Systems-I
313
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:
313