Prediction of berry sunburn damage with machine learning: Results on grapevine (Vitis vinifera L.) - Research Note

Aug 4, 2026

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Climate change is increasing heatwaves and prolonged drought, threatening grapevine yield and berry composition. To address this, a machine learning workflow predicts berry sunburn damage using a two-year dataset (2022–2023) from a not irrigated Sangiovese vineyard monitored from veraison to harvest. Field measurements from a weather station and a wireless sensor network, combined with thermocouple-derived variables and two-weekly visual assessments, train neural network and support vector machine models. Ten features are extracted from raw signals; the neural network reaches 90.32% accuracy in cross-validation, while SVM attains 86.22% with a radial kernel, implemented via the SHEET mobile alert application.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Heat stress and sunburn necrosis in grapevine

Study approach: monitoring and data collection

Machine learning models and feature extraction

Results and model performance

Biosystems Engineering 250 (2025) 62–67 Contents lists available at ScienceDirect Biosystems Engineering journal [homepage: www.elsevier.com/locate/issn/15375110](homepage: www.elsevier.com/locate/issn/15375110) | Research Note
Prediction of berry sunburn damage with machine learning: Results on grapevine (Vitis vinifera L.) | | | | | --- | --- | --- | --- | | Allegro Gianluca a , Ilaria Filippetti a , Chiara Pastorea, Daniela Sangiorgio a ,
Gabriele Valentinia , Gianmarco Bortolottia, Istv´an Kert´esz b , Lien Le Phuong Nguyen b , L´aszl´o Baranyaib,*
a Department of Agricultural and Food Sciences, University of Bologna, Viale Giuseppe Fanin 44, 40127, Bologna, Italy
b Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Vill´anyi str. 35-43., 1118, Budapest, Hungary | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Artificial intelligence Berry necrosis
Berry shrivel Global warming Prediction model | | Due to climate change, heatwaves and prolonged periods of drought are more frequent and cause serious consequences to yield and berry composition of grapevine (Vitis vinifera L.). In response to this challenge, machine learning model was built to predict sunburn damages on the berries. The trial was conducted over two years (2022–2023) in a not irrigated vineyard of cv. Sangiovese, trained to vertical shoot positioning (VSP) spur pruned cordon. The vineyard was monitored from veraison to harvest with a weather station and thermocouples connected to a wireless sensor network (WSN). The evolution of the sunburn damages was visually evaluated twice a week. The damages appeared soon after veraison and the severity of the symptoms increased when heatwaves occurred. Weather station data including air temperature, solar radiation and relative humidity were analysed and used to build prediction models for sunburn damage. Ten parameters were derived from raw data to supply the prediction models of neural network (NN) and Support Vector Machine (SVM) optimised with gamma tuning. The NN achieved 90.32% accuracy in cross-validation, followed by SVM with 86.22% using radial kernel. The machine learning model was created using TensorFlow framework and it is available in the mobile phone application SHEET which will alert grape growers about the risk of sunburn damages on their orchards. | | Nomenclature table | Symbol | Description | | --- | --- | A0-24 A24-48 C DOY LDA LRM0-24 M24-48 ML NN NW PAR r SE Average value of preceding 0–24 h Average value of preceding 24–48 h Control (without treatment) Day of the year, index number in the range of 1–365 Linear discriminant analysis Leaf removal treatment Maximum value of preceding 0–24 h Maximum value of preceding 24–48 h Machine learning Neural network North-west Photosynthetic active radiation Correlation coefficient South-east (continued on next column) (continued ) | Symbol | Description | | --- | --- | | SVM | Support vector machine | | VSP | Vertical shoot positioning | | WSN | Wireless sensor network | 1. Introduction Grapevine (Vitis vinifera L.) is a relevant crop cultivated worldwide intemperate climates, playing a key socioeconomic role in many countries. One of the most important challenge for modern viticulture, is facing threats coming from climate change. Indeed, global climatic records have shown a significant intensification of extreme weather events such as heatwaves, droughts, and anomalies in both the frequency and * Corresponding author. E-mail addresses: [gianluca.allegro2@unibo.it](gianluca.allegro2@unibo.it) (A. Gianluca), [ilaria.filippetti@unibo.it](ilaria.filippetti@unibo.it) (I. Filippetti), [kertesz.istvan@uni-mate.hu](kertesz.istvan@uni-mate.hu) (I. Kert´esz), nguyen.le.phuong. [lien@uni-mate.hu](lien@uni-mate.hu) (L.L.P. Nguyen), [baranyai.laszlo@uni-mate.hu](baranyai.laszlo@uni-mate.hu) (L. Baranyai). [http

FAQs

What problem does the study address in grapevines?

The study targets berry sunburn damage caused by climate-driven heatwaves and drought, which can reduce yield and alter berry composition and quality.

How was the dataset collected for building the prediction models?

Over 2022–2023 in a not irrigated Sangiovese vineyard, measurements from a weather station and thermocouples via a wireless sensor network were recorded from veraison to harvest, while sunburn symptoms were visually evaluated twice per week.

Which machine learning methods were used and what accuracy were achieved?

A neural network and a support vector machine were trained using ten derived parameters. The NN achieved 90.32% accuracy in cross-validation, and the SVM reached 86.22% using a radial kernel.

Aug 4, 2026

Upload by Oliver