[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117566-en":3,"doc-seo-117566-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117566,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Non-Destructive Estimation of Biochemical and Physiological Traits in Lettuce Using Hyperspectral Imaging and Machine Learning","This master thesis investigates hyperspectral imaging combined with machine learning to estimate chemical and physiological traits of lettuce without damaging plant tissue. The work centers on regression of biochemical contents and fluorescence-derived physiological indices from hyperspectral data collected over lettuce leaves. Plants are cultivated in a controlled vertical farming setup integrating a Nutrient Film Technique and an aeroponic system. The study develops a machine-learning pipeline, compares multiple models across datasets, and emphasizes multiple waveband selection. Results show strong performance for flavonols, PI Total, and ET0/RC, while other targets are more difficult, and future improvements depend on better ground-truth data and direct chemical measurements.","Non-Destructive Estimation of Biochemical and Physiological Traits in Lettuce Using Hyperspectral Imaging and Machine Learning  \nTesi di Laurea Magistrale in  \nComputer Science and Engineering - Ingegneria Informatica  \nAuthor: Giacomo Scortecci  \nStudent ID: 233325  \nAdvisor: Prof. Matteo Matteucci  \nCo-advisors: Susanna Mirabella, Aleksander Dabek  \nAcademic Year: 2024-25  \nThanks to my family for supporting me over these years.  \n-Giacomo Scortecci  \ni  \nAbstract  \nThis master thesis explores the use of hyperspectral imaging and machine learning for non-destructive measurements of chemical and physiological traits of plants. The main focus of this thesis was the regression of different biochemical contents and fluorescencederived physiological indices over lettuce leaves using hyperspectral imaging. Lettuce was grown in a controlled environment, using a vertical farming approach that included both a Nutrient Film Technique and an aeroponic system. The regression targets included the concentration of certain pigments and photosynthetic indices, both of which are important factors for estimating the health state of the plants. The thesis developed and tested a machine learning pipeline, evaluating different models over different datasets, with a focus on multiple waveband selection approaches.  \nThe results showed promising performance for certain prediction tasks; in particular the regression models for flavonols, PI Total and ET0 /RC achieved R2 values of 0 .643, 0 .643 and 0 .779, respectively, while other targets proved to be more challenging. Overall, the pipeline shows a potential for non-invasive analysis over plants. Especially, improvements in ground truth quality, such as a bigger dataset and actual chemical analysis being performed instead of indirect sensor-based measurements, could significantly improve model performance.  \nKeywords: Hyperspectral imaging, Machine Learning, Plant phenotyping, Non-destructive analysis  \nAbstract in lingua italiana  \nQuesta tesi di laurea magistrale esplora l’utilizzo di immagini iperspettrali e di modelli di machine learning nell’ambito dell’agricoltura, per eseguire analisi fisico-chimiche in modonon distruttivo. L’obiettivo principale è stato utilizzare delle immagini iperspettrali di foglie di lattuga per applicare degli algoritmi di regressione volti a stimare degli indici fisiologici (solitamente ottenuti tramite analisi di fluorescenza) e la concentrazione di elementibiochimici. La lattuga analizzata è stata coltivata secondo un approccio di agricoltura verticale in un ambiente controllato, che includeva sia un sistema NFT (tecnica del film nutritivo) che un sistema aeroponico. I valori da prevedere includevano la concentrazionedi specifici pigmenti (clorofilla, antociani e flavonoli) e di indici fotosintetici, entrambifattori fondamentali per determinare lo stato di stress della pianta. Nella tesi è statasviluppata e testata una pipeline per il machine learning, valutando le prestazioni di diversi modelli, addestrati su diversi dataset, con particolare attenzione alle tecniche di selezione delle lunghezze d’onda.  \nI risultati hanno mostrato prestazioni promettenti per alcune variabili di output; in particolare, i modelli di regressione per flavonoli, PI Total e ET0 /RC hanno raggiunto valoridi R² pari a 0,643, 0,643 e 0,779 rispettivamente, mentre per altri target la regressione si è rivelata più complessa. La pipeline ha dimostrato potenziale per l’analisi non invasiva delle piante, e un miglioramento nella quantità e qualità dei dati raccolti potrebbe portaread un incremento significativo delle prestazione dei modelli finali.  \nParole chiave: Immagini iperspettrali, Machine Learning, Fenotipizzazione, Analisi non invasive  \nv  \nContents  \nAbstract i  \nAbstract in lingua italiana iii  \nContents v  \nIntroduction 1  \n1 Vertical Farming and Plant Monitoring 3  \n1.1 Vertical Farming ................................ 3  \n1.1.1 Nutrient Film Technique ........................ 3 ","cbCaihFxVS8tm3IP","https://ap.wps.com/l/cbCaihFxVS8tm3IP","pdf",9343052,1,88,"English","en",105,"# Introduction\n## Vertical Farming and Plant Monitoring\n## Hyperspectral imaging\n## Machine Learning\n## Existing applications in the agriculture and food industry fields\n# Experimental Setup and Data Collection\n## Experimental Plant Samples\n## Instrumentation\n## Dataset\n## Software and libraries\n# Method\n## Data pre-processing\n## Feature selection","[{\"question\":\"What measurements does the thesis estimate non-destructively in lettuce?\",\"answer\":\"It estimates biochemical contents and fluorescence-derived physiological indices using hyperspectral imaging. The main targets include concentrations of pigments and photosynthetic-related indices linked to plant health.\"},{\"question\":\"How were the lettuce plants cultivated during the experiments?\",\"answer\":\"Lettuce was grown in a controlled environment using a vertical farming approach. The setup includes both a Nutrient Film Technique (NFT) and an aeroponic system.\"},{\"question\":\"Which regression targets achieved the best performance?\",\"answer\":\"The regression models for flavonols, PI Total, and ET0/RC achieved R2 values of 0.643, 0.643, and 0.779 respectively. Other targets were reported as more challenging.\"}]","Non-Destructive Estimation of Biochemical and Physiological Traits in Lettuce Using Hyperspectral Imaging and Machine Learning | PDF",1785677023,222,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"non-destructive-estimation-of-biochemical-and-physiological-traits-in-lettuce-using-hyperspectral-imaging-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/non-destructive-estimation-of-biochemical-and-physiological-traits-in-lettuce-using-hyperspectral-imaging-and-machine-learning/117566/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What measurements does the thesis estimate non-destructively in lettuce?","Question",{"text":75,"@type":76},"It estimates biochemical contents and fluorescence-derived physiological indices using hyperspectral imaging. The main targets include concentrations of pigments and photosynthetic-related indices linked to plant health.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the lettuce plants cultivated during the experiments?",{"text":80,"@type":76},"Lettuce was grown in a controlled environment using a vertical farming approach. The setup includes both a Nutrient Film Technique (NFT) and an aeroponic system.",{"name":82,"@type":73,"acceptedAnswer":83},"Which regression targets achieved the best performance?",{"text":84,"@type":76},"The regression models for flavonols, PI Total, and ET0/RC achieved R2 values of 0.643, 0.643, and 0.779 respectively. 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