智慧农业（中英文）2025，Vol.7Issue(4)：1-17,17.DOI:10.12133/j.smartag.SA202504027
光谱技术在蔬菜生产检测中的研究进展、挑战与建议
Spectral Technology in Vegetable Production Detection:Research Progress,Challenges and Suggestions
摘要
Abstract
[Significance]Vegetables are indispensable to global food security and human nutrition,yet approximately 33%of the annu-al 1.2 billion-ton harvest is lost or wasted,largely because of undetected biotic and abiotic stresses,poor post-harvest management,and chemical safety hazards.Conventional analytical workflows,based on wet chemistry and chromatography,are destructive,labour-intensive,and difficult to scale,creating an urgent need for rapid,non-invasive sensing tools that can operate across the full produc-tion-to-consumption continuum.Optical spectroscopy,spanning near-infrared(NIR),Raman,fluorescence,laser-induced breakdown spectroscopy(LIBS),and UV-Vis modalities,offers label-free,multiplexed,and second-scale measurements directly on living plants or minimally processed products.Existing reviews have concentrated on isolated techniques or single application niches,leaving criti-cal knowledge gaps regarding hardware robustness under open-field conditions,algorithmic generalisability across cultivars and cli-mates,data interoperability,and cost-driven adoption barriers for smallholders.[Progress]This paper presents a holistic,chain-wide appraisal of spectroscopic sensing in vegetable production.It shows that hardware evolution has been dominated by miniaturisation and functional integration.Hand-held NIR units(e.g.,Neospectra MEMS,NirVana AG410)now weigh<300 g and achieve R2>0.95 for soluble solids and moisture in tomato,zucchini,and pepper.Palm-top Raman systems(9×7×4 cm)equipped with 1 064 nm lasers and InGaAs detectors suppress fluorescence sufficiently to quantify lycopene(RMSE=1.14 mg/100 g)and classify ripeness stages with 100%accuracy.Battery-powered fluorescence sensors coupled with smartphones wirelessly stream data to cloud-based convolutional neural networks(CNNs),delivering 93%～100%correct cultivar identification for spinach,onion,and tomato seeds within 5 s per sample.Methodological advances combine advanced chemometrics and deep learning.Transfer learning enables a model trained on greenhouse tomatoes to predict field-grown cherry tomatoes with only 10%recalibration samples,cutting data acqui-sition costs by 70%.SERS substrates,fabricated as flexible"place-and-play"nano-mesh films,boost Raman signals by 106～108,push-ing limits of detection for carbaryl,imidacloprid,and thiamethoxam below 1 mg/kg on pak-choi and lettuce.Multi-modal fusion(LIBS-NIR)simultaneously quantifies macro-elements(Ca,K,Mg)and micro-elements(Fe,Mn)with relative errors<5%.Chain-wide demonstrations span five critical stages:(i)breeding—NIR screens seed viability via starch and moisture signatures;(ii)cultiva-tion—portable Raman"leaf-clip"sensors detect nitrate deficiency(1 045 cm-1 peak)and early pathogen attack(LsoA vs.LsoB,80%accuracy)in lettuce and tomato before visible symptoms emerge;(iii)harvest—non-invasive lycopene monitoring in tomato and ca-rotenoid profiling in chilli guides optimal picking time and reduces post-harvest losses by 15%;(iv)storage—chlorophyll fluores-cence tracks water loss and senescence in black radish and carrot over six-month cold storage,enabling dynamic shelf-life predic-tion;(v)market entry—LIBS inspects incoming crates for Pb and Cd in seconds,while fluorescence-SVM pipelines simultaneously verify pesticide residues,ensuring compliance with EU and Chinese MRLs.Data governance initiatives are emerging but remain frag-mented.Several consortia have released open spectral libraries(e.g.,VegSpec-1.0 with 50 000 annotated spectra from 30 vegetable species),yet differences in acquisition parameters,preprocessing pipelines,and metadata schemas hinder cross-study reuse.[Conclu-sions and Prospects]Spectroscopic sensing has matured from laboratory proof-of-concept to robust field prototypes capable of guid-ing real-time decisions across the entire vegetable value chain.Nevertheless,four priority areas must be addressed to unlock global adoption:Model generalisation—curate large-scale,multi-environment,multi-cultivar spectral repositories and embed meta-learning algorithms that continuously adapt to new genotypes and climates with minimal retraining.Hardware resilience—develop self-cali-brating sensors with adaptive optics and real-time environmental compensation(temperature,humidity,ambient light)to maintain lab-oratory-grade SNR in dusty,humid,or high-irradiance field settings.Standardisation and interoperability—establish ISO-grade proto-cols for hardware interfaces,data formats,calibration transfer,and privacy-preserving data sharing,enabling seamless integration of devices,clouds,and decision-support platforms.Cost-effective commercialisation—pursue modular,open-hardware designs leverag-ing printed optics and economies of scale to reduce unit costs below USD 500,and introduce service-based models(leasing,pay-per-scan)tailored to smallholder economics.If these challenges are met,spectroscopy-based digital twins of vegetable production systems could become a reality,delivering safer food,reduced waste,and climate-smart agriculture within the next decade.关键词
光谱技术/近红外光谱/拉曼光谱/荧光光谱/蔬菜/现场检测Key words
spectrum technology/near-infrared spectroscopy/Raman spectroscopy/fluorescence spectroscopy/vegetables/on-site detection分类
化学化工引用本文复制引用
白珏坤,陈怀勐,董大明,刘亚超,岳晓龙,杜秀可..光谱技术在蔬菜生产检测中的研究进展、挑战与建议[J].智慧农业（中英文）,2025,7(4):1-17,17.基金项目
国家重点研发计划项目(2022YFD2002101) National Key Research and Development Program of China(2022YFD2002101) （2022YFD2002101）
