使用深度转移学习与优化算法的自动杂草和作物识别和分类模型
K Gopalakrishnan1, R Sivaraj2, M Vijayakumar3
1Department of Computer Science and Business Systems, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India, 641048. gopalakrishnanbtech1@gmail.com.
Scientific reports
|August 10, 2025
概括
本研究介绍了用于精准农业的使用深度学习模型与虫优化 (AWRC-DLMLO) 的自动杂草识别和分类. AWRC-DLMLO方法有效地检测和分类杂草,改善作物管理和减少对环境的影响.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 杂草和作物对资源的竞争导致作物产量减少和农业成本增加.
- 传统的除草方法,如大量使用农药,造成环境污染,并导致对除草剂的耐药性.
- 有机农业和无污染物产品的需求日益增长,需要创新的杂草管理解决方案.
研究的目的:
- 开发一种使用深度学习模型与虫优化 (AWRC-DLMLO) 的自动杂草识别和分类.
- 使用先进的人工智能技术,准确地检测和分类除草和作物.
- 提高农业杂草管理的效率和有效性.
主要方法:
- 使用高斯过 (GF) 进行图像预处理以减少噪声.
- 使用剩余注意力U-Net (RA-UNet) 的植物细分.
- 使用ShuffleNetV2进行特征提取,使用Lemurs优化算法 (LOA) 进行超参数优化,并使用级联Q网络 (CQN)进行分类.
主要成果:
- 拟议的AWRC-DLMLO方法在杂草检测和分类方面表现优异,与现有模型相比.
- 模拟证实了深度学习模型在农业应用中优化的有效性.
- 整合了GF,RA-UNet,ShuffleNetV2,LOA和CQN,在识别杂草和作物方面取得了很高的准确性.
结论:
- AWRC-DLMLO技术为智能农业中的自动化杂草管理提供了一个有前途的AI驱动解决方案.
- 这种方法可以通过最大限度地减少作物损失和减少对化学除草剂的依赖,为可持续的农业实践做出重大贡献.
- 该研究强调了深度学习和优化算法的潜力,以推进精准农业和确保粮食安全.
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