智能农业机器人检测系统用于温室番茄叶病,使用软计算技术和深度学习
Thi Thoa Mac1, Tien-Duc Nguyen2, Hong-Ky Dang1
1School of Mechanical Engineering, Hanoi University of Science and Technology, 1st Dai Co Viet Road, Hai Ba Trung District, Hanoi, 100000, Vietnam.
Scientific reports
|October 12, 2024
概括
这项研究引入了升级的深度卷积生成对抗网络 (DCGAN),以改善在自主温室中的番茄植物疾病检测. 增强的数据集显著提高了智能农业的深度学习模型准确性.
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:
- 软计算方法对于推进自主智能农业至关重要.
- 在像番茄这样的作物中准确识别疾病对于温室管理至关重要.
- 现有的深度学习模型需要大型,多样化的数据集以实现最佳性能.
研究的目的:
- 开发一个自主温室导航系统,使用模糊控制和深度学习来对番茄植物疾病进行分类.
- 引入一个升级的深度卷积生成对抗网络 (DCGAN) 来增强番茄叶病图像数据集.
- 为了比较各种深度学习模型的性能,以准确识别疾病.
主要方法:
- 实现了一个模糊的控制算法,用于自主温室导航.
- 使用升级的DCGAN生成病变番茄叶的增强图像,增强训练数据集.
- 在用于疾病分类的PlantVillage数据集上比较了四个深度学习网络 (VGG19,Inception-v3,DenseNet-201,ResNet-152).
- 在原始和增强数据集上使用验证准确度评估模型性能.
主要成果:
- 在比较模型中,ResNet-152在原始PlantVillage数据集上实现了最高的准确性 (97.07%).
- 当DCGAN增强数据集与ResNet-152一起使用时,结果是精度明显提高了99.69%.
- 拟议的DCGAN有效地提高了深度学习模型在温室植物监测和疾病检测方面的性能.
结论:
- 升级后的DCGAN显著提高了用于检测番茄植物疾病的深度学习模型的准确性.
- 开发的系统为自主智能农业提供了强大的解决方案,增强了温室管理.
- 该方法显示了在各种农业场景中广泛应用的潜力,推进了自主农业实践.
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