Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections

Abstract Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, r...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sanja Brdar, Marko Panić, Esther Hogeveen-van Echtelt, Manon Mensink, Željana Grbović, Ernst Woltering, Aneesh Chauhan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4c922f6721c34e9c9914be7060412977
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4c922f6721c34e9c9914be7060412977
record_format	dspace
spelling	oai:doaj.org-article:4c922f6721c34e9c9914be70604129772021-12-05T12:13:49ZPredicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections10.1038/s41598-021-02302-22045-2322https://doaj.org/article/4c922f6721c34e9c9914be70604129772021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02302-2https://doaj.org/toc/2045-2322Abstract Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000–1700 nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390–1420 nm contributes most to the model’s final decision.Sanja BrdarMarko PanićEsther Hogeveen-van EchteltManon MensinkŽeljana GrbovićErnst WolteringAneesh ChauhanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sanja Brdar Marko Panić Esther Hogeveen-van Echtelt Manon Mensink Željana Grbović Ernst Woltering Aneesh Chauhan Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections
description	Abstract Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000–1700 nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390–1420 nm contributes most to the model’s final decision.
format	article
author	Sanja Brdar Marko Panić Esther Hogeveen-van Echtelt Manon Mensink Željana Grbović Ernst Woltering Aneesh Chauhan
author_facet	Sanja Brdar Marko Panić Esther Hogeveen-van Echtelt Manon Mensink Željana Grbović Ernst Woltering Aneesh Chauhan
author_sort	Sanja Brdar
title	Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections
title_short	Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections
title_full	Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections
title_fullStr	Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections
title_full_unstemmed	Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections
title_sort	predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4c922f6721c34e9c9914be7060412977
work_keys_str_mv	AT sanjabrdar predictingsensitivityofrecentlyharvestedtomatoesandtomatosepalstofuturefungalinfections AT markopanic predictingsensitivityofrecentlyharvestedtomatoesandtomatosepalstofuturefungalinfections AT estherhogeveenvanechtelt predictingsensitivityofrecentlyharvestedtomatoesandtomatosepalstofuturefungalinfections AT manonmensink predictingsensitivityofrecentlyharvestedtomatoesandtomatosepalstofuturefungalinfections AT zeljanagrbovic predictingsensitivityofrecentlyharvestedtomatoesandtomatosepalstofuturefungalinfections AT ernstwoltering predictingsensitivityofrecentlyharvestedtomatoesandtomatosepalstofuturefungalinfections AT aneeshchauhan predictingsensitivityofrecentlyharvestedtomatoesandtomatosepalstofuturefungalinfections
_version_	1718372131018899456

Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections

Ejemplares similares