Exploring the potential of hyperspectral imaging and machine learning to assess biophysical traits of tomato crops

A. Matese; N. Hamie; R. Dainelli; P. Toscano; S.F. Di Gennaro

Jump to Content

Exploring the potential of hyperspectral imaging and machine learning to assess biophysical traits of tomato crops

In: Precision agriculture '25

Authors:

A. Matese

A. Matese alessandro.matese@cnr.it

Search for other papers by A. Matese in
Current site
Google Scholar
PubMed

N. Hamie

N. Hamie
Search for other papers by N. Hamie in
Current site
Google Scholar
PubMed

R. Dainelli

R. Dainelli
Search for other papers by R. Dainelli in
Current site
Google Scholar
PubMed

P. Toscano

P. Toscano
Search for other papers by P. Toscano in
Current site
Google Scholar
PubMed

, and

S.F. Di Gennaro

S.F. Di Gennaro
Search for other papers by S.F. Di Gennaro in
Current site
Google Scholar
PubMed

Type:: Chapter

Pages:: 599–606

DOI:: https://doi.org/10.1163/9789004725232_078

Access via:

Dar Hadith al Hassania

Download PDF

This study examined the application of hyperspectral imaging (HSI) and machine learning (ML) in agricultural research to assess biophysical traits of tomato crops in the Mediterranean region. The research was conducted at the DATI project experimental field in Italy, using a DJI M600 Pro UAV equipped with a Headwall Nano-HyperspecR camera. The results showed that irrigation deficiency significantly increased the content of chlorophyll and flavonoids in tomato leaves but had no effect on anthocyanin. The best prediction models were obtained with HS wavelength of 715 nm and the transformed chlorophyll absorbance reflectance index (TCARI) performing the best.

Citation Info

Save
Cite
Email this content

Share link with colleague or librarian

You can email a link to this page to a colleague or librarian:
Email this content
or copy the link directly:

https://brill.edhh.ma/edcollchap-oa/book/9789004725232/BP000078.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Collapse
Expand

Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

Metrics

	All Time	Past 365 days	Past 30 Days
Abstract Views	0	0	0
Full Text Views	99	78	13
PDF Views & Downloads	145	110	15

African Studies	Education	Media Studies
American Studies	History	Middle East and Islamic Studies
Ancient Near East and Egypt	Human Rights and Humanitarian Law	Musicology
Art History	International Law	Philosophy
Asian Studies	International Relations	Religious Studies
Biblical Studies	Jewish Studies	Slavic and Eurasian Studies
Biology	Languages and Linguistics	Social Sciences
Book History and Cartography	Life Sciences	Theology and World Christianity
Classical Studies	Literature and Cultural Studies

Subjects

Authors

Open Access

Product Information

Company

Contact

Exploring the potential of hyperspectral imaging and machine learning to assess biophysical traits of tomato crops

Citation Info

Share link with colleague or librarian

Precision agriculture '25

Table of Contents

Metrics

Metrics