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果品种,疾病,并通过深度学习方法区分新鲜和腐烂

Tao Zhang¹, Mustafa Mhamed^1,2,3,4,5, Qu Zhang^2,3,4

¹Department of Materials and Architectural Engineering, Hebei Institute of Mechanical and Electrical Technology, Xingtai, China.

|May 15, 2025

概括

这项研究引入了新的AI模型用于果识别,质量评估和疾病检测,大大改善了自动化农业系统. 这些进步提高了机器人视觉和果生产中的分拣效率.

科学领域:

农业人工智能农业人工智能
计算机视觉计算机视觉
机器学习机器学习

背景情况:

自动化果系统在识别腐烂的水果,类别内变异和品种间相似性方面面临挑战.
果疾病和质量变化对农业经济和市场能力产生重大影响.

研究的目的:

开发先进的AI模型,精确识别果品种,检测腐烂的果和诊断疾病.
为培训和评估这些AI模型在农业应用中创建新的数据集.

主要方法:

果果品种收集 (AFVC),果果品质分类 (AFQC) 和果疾病广泛收集 (ADEC) 数据集的开发.
实现一个优化果园模型 (OAOM),使用一种新的测量焦点交叉 (MFCE) 损失函数.

主要成果:

在果品种识别 (AFVC) 中,OAOM的准确度达到93.85%,在腐烂果识别 (AFQC) 中达到98.28%,在疾病识别 (ADEC) 中达到99.66%.
与现有的基线模型相比,拟议的OAOM表现出优越的性能.

结论:

开发的AI模型和数据集显著提高了果生产的自动化,改善了机器人视觉和分类系统.
这项研究为推进果行业决策政策和整体效率提供了基础.

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