OptiNet-B3:一种轻量级可解释的深度学习模型,用于对水果和叶子疾病进行多类分类
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
Scientific reports
|November 25, 2025
概括
这项研究介绍了OptiNet-B3,一个高效的深度学习模型,用于检测果,香和子中的水果和叶植物疾病. 它实现了高精度,实现了实时,人工智能驱动的作物疾病管理.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 准确和早期发现作物疾病对于可持续农业至关重要.
- 现有的方法在识别各种植物疾病时可能缺乏效率或准确性.
研究的目的:
- 提出OptiNet-B3,一种新且高效的深度学习模型,用于对水果和叶子疾病进行多类分类.
- 评估模型在果,香和色数据集上的表现.
主要方法:
- 开发了OptiNet-B3,一个集成Mish激活,卷积块注意模块 (CBAM),组规范化和知识蒸的深度模型.
- 使用了两种全面的数据集来对水果 (13,602张图像) 和叶子 (11,199张图像) 疾病进行分类.
- 采用严格的数据预处理和增强技术.
主要成果:
- OptiNet-B3在水果疾病分类方面达到98.12%的高准确率,在叶病分类方面达到99.23%的高准确率.
- 性能优于像DenseNet121,ResNet50,MobileNetV3和InceptionV3.3这样的最先进的模型.
- 证明了低计算预算的高效学习.
结论:
- OptiNet-B3为识别水果和叶植物疾病提供了高度准确和高效的解决方案.
- 它的轻量级架构可在移动和边缘设备上实时部署,用于现场诊断.
- 突出了可解释的人工智能在革命性植物疾病管理中的潜力.
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