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基于深度迁移学习的农业病害图像识别

花匠小妙招
2024-09-17 11:42

摘要：

【目的】本文针对农业病害图像识别问题,探讨在不同数据规模条件下融合不同的机器学习方法,以提高农业病害图像识别准确率。【方法】重点围绕农业病害图像数据规模较小条件下的机器学习建模问题,引入深度迁移学习方法,通过具体实验探讨如何提高小样本条件下的建模效果。【结果】在高质量的农业病害图像数据集上,引入深度迁移学习方法能够有效提高农业病害图像识别准确率。【局限】在基于深度神经网络的机器学习方法中,农业病害图像数据集的质量及规模对于建模效果均有一定的影响,未来将进一步探索在数据质量和规模等方面具有更佳普适性的建模方法。【结论】在农业病害图像识别技术研究中,引入深度迁移学习方法能够有效提高小样本条件下的机器学习建模效果以及最终的病害图像识别准确率,可为后续构建各种农业病害图像识别系统平台提供良好的技术支撑。

关键词: 图像识别, 迁移学习, 深度学习, 农业病害, 大数据

Abstract:

[Objective] This paper focuses on the issue of image recognition of agricultural diseases and explores the integration of different machine learning methods under different data scales to improve the accuracy of agricultural disease image recognition. [Methods] Focusing on the problem of machine learning modeling under the condition of small scale of agricultural disease image data, the deep transfer learning method is introduced and some specific experiments are conducted to explore how to improve the modeling effect under the condition of small samples. [Results] On the high-quality agricultural disease image data set, the introduced deep transfer learning method can effectively improve the accuracy of agricultural disease image recognition. [Limitations] In the machine learning method based on deep neural networks, both the quality and the scale of agricultural disease images have certain influence on the modeling effect. In the future, we will further explore the modeling method with better universality in data quality and scale. [Conclusions] In the research of agricultural disease image recognition technology, the adaptation of deep transfer learning method can effectively improve the machine learning modeling effect and the final disease image recognition accuracy under the condition of small samples, which can provide good technical support for the subsequent construction of various agricultural disease image recognition systems.

Key words: image recognition, transfer learning, deep learning, agriculture diseases, big data