摘要
林果品质与消费者和果农密切相关,在保障消费者安全、优化运输贮藏、改善后续分级、实现优质优价、提高果农收益等方面均有重要作用,然而传统的林果品质检测方法存在效率低、检测范围有限和鲁棒性差等问题。近年来,随着深度学习的迅猛发展,林果品质无损检测技术也取得了突破性的进步。该研究梳理了目前主流的深度学习模型及其作用,然后从林果安全品质、外部品质和内部品质3个方面阐述了深度学习的研究进展,发现卷积神经网络是林果品质无损检测领域应用最广泛的深度学习模型。同时结合深度学习应用现状与模型不足分析了该领域仍存在数据质量低、模型泛化能力差、实际部署困难等问题,提出未来应围绕数据增强与评价指标、建立公共数据集、多模态融合、模型融合、元学习、拓展应用、模型压缩等方面展开研究。该文旨在为深度学习在林果品质无损检测中的进一步发展提供参考,以加快林果产业的数字化、信息化进程。
Forestry and fruit industry are two of the most significant components in the agricultural sector,high ecological and economic benefits in China.The forest fruits have been ever-increasingly produced annually.However,it is a high demand for the high-quality forest products,as living standards rise.Accurate assessment of fruit quality can dominate food safety to optimize the transportation and storage for the subsequent grading.Therefore,it is necessary to develop rapid,accurate,and non-destructive quality detection for forest fruits.Particularly,deep learning has been widely applied in the non-destructive quality detection of forest fruits in recent years,due to the automatic features extraction,wide applicability,and end-to-end learning.This review was focused on the research progress of deep learning in forest fruit quality from three aspects:safety,external and internal quality.It was found that Convolutional Neural Networks(CNN)were the most widely used deep learning models in the field of non-destructive quality detection of forest fruits.However,several challenges still remained,including data quality,generalization,and practical implementation.Therefore,some recommendations were then proposed for further application of deep learning.The publicly available datasets should be established to validate the model performance.Future endeavors should explore the application of deep learning-based data augmentation to generate spectral data,beyond only image datasets.Additionally,interdisciplinary collaboration should be fostered to combine microbiology,insect ecology,and plant pathology.Furthermore,standardized evaluation metrics should be developed to validate the scientific soundness of deep learning models,in order to generate various pest and disease data.In terms of models,meta-learning can be expected to design models that can adapt and improve learning strategies.Some challenges are to overcome the influences of origin,batch,environment,and variety on the accuracy of the model.Since most existing studies ha
作者
蒋雪松
计恺豪
姜洪喆
周宏平
JIANG Xuesong;JI Kaihao;JIANG Hongzhe;ZHOU Hongping(Co-Innovation Center of Efficient Processing and Utilization of Forest Resources,Nanjing Forestry University,Nanjing 210037,China;College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing 210037,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2024年第17期1-16,共16页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划项目(2022YFD2202103-5)
国家自然科学基金项目(32102071)。
关键词
深度学习
品质保障
卷积神经网络
林果
无损检测
deep learning
quality assurance
convolutional neural networks
forest fruits
non-destructive detection
