Detecting the Unseen: Hyperspectral Image Analysis for the Detection of Early Symptoms of Late Blight in Tomato Plants and Design of Its Machine Vision Application

Setiawan, Nuri Nurlaila; Labus, Balázs; Tóth, Ferenc; Divéky-Ertsey, Anna; Bori, Dániel; Drexler, Dóra

doi:10.3390/agriengineering8090354

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Detecting the Unseen: Hyperspectral Image Analysis for the Detection of Early Symptoms of Late Blight in Tomato Plants and Design of Its Machine Vision Application

by

Nuri Nurlaila Setiawan

^*

,

Balázs Labus

,

Ferenc Tóth

,

Anna Divéky-Ertsey

,

Dániel Bori

and

Dóra Drexler

ÖMKi, Hungarian Research Institute of Organic Agriculture, Ráby Mátyás u. 26, 1038 Budapest, Hungary

^*

Author to whom correspondence should be addressed.

AgriEngineering 2026, 8(9), 354; https://doi.org/10.3390/agriengineering8090354

Submission received: 22 May 2026 / Revised: 21 August 2026 / Accepted: 22 August 2026 / Published: 25 August 2026

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Abstract

Late blight in tomato caused by Phytophthora infestans can lead to severe economic losses. Early detection is essential for effective disease management. This study investigates the spectral characteristics of healthy and late blight-infected tomato leaves and plants using non-destructive hyperspectral imaging and proposes a cost-effective machine vision system for early disease detection. Hyperspectral images from seven batches of leaf sets and six batches of whole plant sets were taken hourly over a 96 h period under both controlled and artificially infected conditions. The hyperspectral data cubes were processed with an image analysis model that identified healthy vs. infected regions. Key wavelengths (77 from leaf datasets and 24 from plant datasets) were selected using recursive feature elimination and analysed with four machine learning classifiers: k-nearest neighbour, support vector machine, random forest, and artificial neural network. The models differentiated healthy and infected tissue with high accuracy (98–99%). The hyperspectral data were simplified into a multichannel image with most informative wavelengths, using a custom spectral index and binary decision rule. Experimental limitations were addressed, and a conceptual design of practical hardware was proposed: a monochrome camera combined with a multichannel light source and polariser mounted on mobile equipment. Although further trials will be needed, this proof-of-concept study and conceptual hardware design can be adapted in other crops facing similar disease challenges.

Keywords: machine vision; machine learning; Phytophthora infestans; hyperspectral imaging; precision farming; early disease detection

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MDPI and ACS Style

Setiawan, N.N.; Labus, B.; Tóth, F.; Divéky-Ertsey, A.; Bori, D.; Drexler, D. Detecting the Unseen: Hyperspectral Image Analysis for the Detection of Early Symptoms of Late Blight in Tomato Plants and Design of Its Machine Vision Application. AgriEngineering 2026, 8, 354. https://doi.org/10.3390/agriengineering8090354

AMA Style

Setiawan NN, Labus B, Tóth F, Divéky-Ertsey A, Bori D, Drexler D. Detecting the Unseen: Hyperspectral Image Analysis for the Detection of Early Symptoms of Late Blight in Tomato Plants and Design of Its Machine Vision Application. AgriEngineering. 2026; 8(9):354. https://doi.org/10.3390/agriengineering8090354

Chicago/Turabian Style

Setiawan, Nuri Nurlaila, Balázs Labus, Ferenc Tóth, Anna Divéky-Ertsey, Dániel Bori, and Dóra Drexler. 2026. "Detecting the Unseen: Hyperspectral Image Analysis for the Detection of Early Symptoms of Late Blight in Tomato Plants and Design of Its Machine Vision Application" AgriEngineering 8, no. 9: 354. https://doi.org/10.3390/agriengineering8090354

APA Style

Setiawan, N. N., Labus, B., Tóth, F., Divéky-Ertsey, A., Bori, D., & Drexler, D. (2026). Detecting the Unseen: Hyperspectral Image Analysis for the Detection of Early Symptoms of Late Blight in Tomato Plants and Design of Its Machine Vision Application. AgriEngineering, 8(9), 354. https://doi.org/10.3390/agriengineering8090354

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Detecting the Unseen: Hyperspectral Image Analysis for the Detection of Early Symptoms of Late Blight in Tomato Plants and Design of Its Machine Vision Application

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