高光谱成像技术在农作物品质与安全原位感知中的研究进展

doi:10.3969/j.issn.1004-1524.20250318

浙江农业学报 ›› 2026, Vol. 38 ›› Issue (5): 1035-1047.DOI: 10.3969/j.issn.1004-1524.20250318

高光谱成像技术在农作物品质与安全原位感知中的研究进展

张昊(), 谭烽, 周禹, 王大臣, 周宏平, 姜洪喆^*()

南京林业大学机械电子工程学院, 江苏南京 210037

收稿日期:2025-04-22 出版日期:2026-05-25 发布日期:2026-06-02
作者简介:张昊,研究方向为农林产品智能检测技术。E-mail:1109751417@qq.com
通讯作者: *姜洪喆,E-mail:jianghongzhe@njfu.edu.cn
基金资助:
国家自然科学基金项目(32102071);中国博士后科学基金项目(2023M741724);江苏省农业科技自主创新资金项目(CX(24)3051);江苏省高等学校大学生创新创业训练计划项目(202410298018Z)

Research progress of hyperspectral imaging technology in the in-situ sensing of crop quality and safety

ZHANG Hao(), TAN Feng, ZHOU Yu, WANG Dachen, ZHOU Hongping, JIANG Hongzhe^*()

College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China

Received:2025-04-22 Published:2026-05-25 Online:2026-06-02

摘要/Abstract

摘要：

传统高光谱成像技术广泛应用于农作物无损检测领域,但实验室条件无法完全模拟田间环境,导致模型外推性差。基于高光谱成像技术的原位感知直接在田间采集光谱信息,保留样本在真实环境下的状态,所获数据更贴近实际农业生产场景,有助于构建更具实用性的预测模型。因此,该技术在农作物品质与安全原位感知方面潜力显著。本文综述了基于高光谱成像技术的原位感知的基本原理与流程,重点阐述了其在农作物品质与安全方面的研究进展,针对当前存在的模型泛化能力不足、田间部署困难和环境干扰严重等问题,从硬件、算法和技术融合等维度展望了未来的研究方向,旨在为相关研究提供参考并推动该技术的发展。

关键词: 高光谱成像, 原位感知, 品质感知, 安全感知, 无损检测

Abstract:

Traditional hyperspectral imaging technology has been widely used in the nondestructive detection of crops in recent years. However, laboratory conditions can not fully simulate the field environment in which crops grow, leading to poor extrapolation performance of models. In contrast, in-situ sensing based on hyperspectral imaging technology directly collects spectral information from crops in the field without damaging samples, thereby preserving their state in real environments. This approach yields data that aligns more closely with actual agricultural production scenarios, enabling the development of more practical predictive models. Consequently, hyperspectral imaging technology holds great potential for in-situ sensing of crop quality and safety. This review briefly introduces the fundamental principles and processes of in-situ sensing based on hyperspectral imaging technology, focusing on its research progress in crop quality and safety. It further summarizes major challenges such as insufficient model generalization, difficulties in field deployment, and substantial environmental interference. Future research directions are discussed from the perspectives of hardware advancement, algorithmic innovation, and technological integration, aiming to support continued progress in this field.

Key words: hyperspectral imaging, in-situ sensing, quality sensing, safety sensing, non-destructive testing

中图分类号:

S123

张昊, 谭烽, 周禹, 王大臣, 周宏平, 姜洪喆. 高光谱成像技术在农作物品质与安全原位感知中的研究进展[J]. 浙江农业学报, 2026, 38(5): 1035-1047.

ZHANG Hao, TAN Feng, ZHOU Yu, WANG Dachen, ZHOU Hongping, JIANG Hongzhe. Research progress of hyperspectral imaging technology in the in-situ sensing of crop quality and safety[J]. Acta Agriculturae Zhejiangensis, 2026, 38(5): 1035-1047.

图/表 9

参考文献 61

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样品 Sample	感知内容 Test items	建模方法 Modeling approach	性能评估 Result validation	参考文献 Reference
葡萄Grape	TSS	PLSR	${R}_{\mathrm{C}}^{2}$=0.92、${R}_{\mathrm{P}}^{2}$=0.95	[11]
	花青素含量Anthocyanin content	PLSR	${R}_{\mathrm{C}}^{2}$=0.75、${R}_{\mathrm{P}}^{2}$=0.79	[11]
	TP	PLSR	${R}_{\mathrm{C}}^{2}$=0.42、${R}_{\mathrm{P}}^{2}$=0.43	[11]
葡萄Grape	TSS	PLSR	${R}_{\mathrm{P}}^{2}$=0.82	[12]
	花青素含量Anthocyanin content	PLSR	${R}_{\mathrm{P}}^{2}$=0.88	[12]
	TP	PLSR	${R}_{\mathrm{P}}^{2}$=0.55	[12]
	TA	PLSR	${R}_{\mathrm{P}}^{2}$=0.81	[12]
葡萄Grape	TSS、成熟度Ripeness	PLS-DA	ACC=86%~91%	[13]
蓝莓Blueberry	成熟度Ripeness	KNN、SVM、AdaBoost	ACC=88%	[14]
杧果Mango	成熟度Ripeness	PLS-DA、CNN	F1>0.97	[15]
草莓Strawberry	成熟度Ripeness	SVM	ACC=98.6%	[17]
桃Peach	成熟度Ripeness	LIBSVM	ACC=91.7%	[18]
油茶果Camellia oleifera fruit	成熟度Ripeness	PLS-DA	ACC=93.82%	[19]
油茶果Camellia oleifera fruit	成熟度Ripeness	PLS-DA	ACC=88.6%	[21]
辣椒Pepper	成熟度Ripeness	SG-Der-SAM PCA-KELM	ACC=99.8% ACC=89.4%	[20] [20]
辣椒Pepper	成熟度Ripeness	PCA-KELM	ACC=97.3%	[22]

样品 Sample	感知内容 Test items	建模方法 Modeling approach	性能评估 Result validation	参考文献 Reference
葡萄Grape	TSS	PLSR	${R}_{\mathrm{C}}^{2}$=0.92、${R}_{\mathrm{P}}^{2}$=0.95	[11]
	花青素含量Anthocyanin content	PLSR	${R}_{\mathrm{C}}^{2}$=0.75、${R}_{\mathrm{P}}^{2}$=0.79	[11]
	TP	PLSR	${R}_{\mathrm{C}}^{2}$=0.42、${R}_{\mathrm{P}}^{2}$=0.43	[11]
葡萄Grape	TSS	PLSR	${R}_{\mathrm{P}}^{2}$=0.82	[12]
	花青素含量Anthocyanin content	PLSR	${R}_{\mathrm{P}}^{2}$=0.88	[12]
	TP	PLSR	${R}_{\mathrm{P}}^{2}$=0.55	[12]
	TA	PLSR	${R}_{\mathrm{P}}^{2}$=0.81	[12]
葡萄Grape	TSS、成熟度Ripeness	PLS-DA	ACC=86%~91%	[13]
蓝莓Blueberry	成熟度Ripeness	KNN、SVM、AdaBoost	ACC=88%	[14]
杧果Mango	成熟度Ripeness	PLS-DA、CNN	F1>0.97	[15]
草莓Strawberry	成熟度Ripeness	SVM	ACC=98.6%	[17]
桃Peach	成熟度Ripeness	LIBSVM	ACC=91.7%	[18]
油茶果Camellia oleifera fruit	成熟度Ripeness	PLS-DA	ACC=93.82%	[19]
油茶果Camellia oleifera fruit	成熟度Ripeness	PLS-DA	ACC=88.6%	[21]
辣椒Pepper	成熟度Ripeness	SG-Der-SAM PCA-KELM	ACC=99.8% ACC=89.4%	[20] [20]
辣椒Pepper	成熟度Ripeness	PCA-KELM	ACC=97.3%	[22]

样品Sample	建模方法Modeling approach	性能评估Result validation	参考文献Reference
生菜Lettuce	MC-UVE-CARS-PLS	R_C=82.71%,R_P=84.67%	[23]
生菜Lettuce	PCA	AVG=0.952 6、0.047 7	[24]
冬小麦Winter wheat	BPNN	${R}_{\mathrm{C}}^{2}$=0.889,${R}_{\mathrm{P}}^{2}$=0.891	[25]
玉米Corn	LR	R²>0.6	[26]
紫花苜蓿Alfalfa	PLS	R²=0.86,RMSE=2.00%	[27]
葡萄藤Grapevine	RF	ACC=83.3%	[28]

样品Sample	建模方法Modeling approach	性能评估Result validation	参考文献Reference
生菜Lettuce	MC-UVE-CARS-PLS	R_C=82.71%,R_P=84.67%	[23]
生菜Lettuce	PCA	AVG=0.952 6、0.047 7	[24]
冬小麦Winter wheat	BPNN	${R}_{\mathrm{C}}^{2}$=0.889,${R}_{\mathrm{P}}^{2}$=0.891	[25]
玉米Corn	LR	R²>0.6	[26]
紫花苜蓿Alfalfa	PLS	R²=0.86,RMSE=2.00%	[27]
葡萄藤Grapevine	RF	ACC=83.3%	[28]

样品Sample	感知内容Test items	建模方法Modeling approach	性能评估Result validation	参考文献Reference
牧草Forage	磷含量Phosphorus content	FD-IB+SVM	R²=0.67,RMSE=0.047 2%	[29]
水稻Rice	氮含量Nitrogen content	GA-ELM	R²>0.68	[30]
水稻Rice	钾含量Potassium content	PROSPECT-5	R²=0.74	[36]
冬小麦Winter wheat	氮含量Nitrogen content	SVR	R²=0.88	[31]
		RFR	R²=0.91	[31]
冬小麦Winter wheat	氮含量Nitrogen content	N-PROSAIL	R²=0.75%,RMSE=0.38%	[32]
苹果树Apple tree	氮含量Nitrogen content	PLSR	R²=0.772 8	[35]
		MLR	R²=0.784 3	[35]

高光谱成像技术在农作物品质与安全原位感知中的研究进展

Research progress of hyperspectral imaging technology in the in-situ sensing of crop quality and safety

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样品 Sample	感知内容 Test items	建模方法 Modeling approach	性能评估 Result validation	参考文献 Reference
番茄Tomato	综合品质Comprehensive quality	RNN	${R}_{\mathrm{P}}^{2}$=0.649,RMSEP=0.56	[37]
番茄Tomato	综合品质Comprehensive quality	MLR	${R}_{\mathrm{V}}^{2}$=0.87,RMSEV=1.33,PRD=2.58	[38]
油茶果Camellia oleifera fruit	综合成熟度Composite maturity index	SPA-CNNR	R_P=0.839,RMSEP=0.261,RPD=1.849	[21]
油茶果Camellia oleifera fruit	综合品质Comprehensive quality	RF	R²=0.853 4,RMSE=0.052 6	[19]

样品 Sample	建模方法 Modeling approach	性能评估 Result validation	参考文献 Reference
山核桃 Pecan	XGboost	${R}_{\mathrm{P}}^{2}$=0.788, RMSEP=0.842	[39]
柑橘 Citrus	H-ELR、PLSR	R²=0.876, RMSE=3.447, RPD=2.601	[41]
植物Plants	3D-CNN-LSTM	${R}_{\mathrm{P}}^{2}$=0.87~0.97	[42]
小麦Wheat	K-means、PLSR	${R}_{\mathrm{P}}^{2}$>0.81	[43]

样品Sample	感知内容Test items	建模方法Modeling approach	性能评估Result validation	参考文献Reference
小麦Wheat	FHB	ETR	R²=0.94	[44]
小麦Wheat	FHB	SB+VI+WF	R²=0.88,RMSE=2.68%	[46]
小麦Wheat	FHB	BPNN	ACC=98%	[47]
小麦Wheat	条锈病Stripe rust	PCA-loadings-BPNN	${R}_{\mathrm{C}}^{2}$=0.921,${R}_{\mathrm{P}}^{2}$=0.918	[48]
小麦Wheat	条锈病Stripe rust	PLSR	R²=0.65、0.75、0.82	[49]
小麦Wheat	PM	PLSR	ACC=82.35%	[50]
小麦Wheat	SR	RFC	ACC=85%	[51]
马铃薯Potato	晚疫病Late blight	RFC	ACC=0.99±0.04	[52]
马铃薯Potato	晚疫病Late blight	CropdocNet	ACC=98.09%	[53]
水稻Rice	稻曲病False smut	GBDT	ACC=85.62%	[54]
水稻Rice	稻曲病False smut	RF	ACC=74.23%、85.19%	[55]
柑橘Citrus	流胶病Gummosis	SAM	ACC=94%	[56]

样品 Sample	感知内容 Test items	建模方法 Modeling approach	性能评估 Result validation	参考文献 Reference
马铃薯Potato	黑胫病Blackleg	SVM	BA=0.915	[57]
柑橘Citrus	溃疡病Citrus canker	RBF、KNN	ACC=100%	[58]
柑橘Citrus	黄龙病Huanglongbing disease	GA、SAE	ACC=99.72%	[59]
杧果Mango	虫害Insect pest	MD-FCM、XCS-RBFNN	ACC=97.02%	[60]
棉花Cotton	蚜害Aphid infestation	PLSR	R²=0.612,RMSE=0.89	[61]

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