DOI: 10.5281/zenodo.14177888
title: Spectral and Chemical Dataset for Ripeness Monitoring in cv. Tempranillo Grapes Using a Multispectral Sensor

description: The dataset  consists of 1010 samples of Tempranillo grape berries, offering a comprehensive record of spectral and chemical measurements that serve as a valuable resource for evaluating berry ripeness and sugar content (ºBrix). Each row in the dataset corresponds to a single berry, and the columns include a unique identifier (ID), the date of sampling (spanning 21 different days during the ripening period in 2024), expressed as Day of the Year (DOY) from DOY 210 to DOY 284. The dataset also includes measurements for nine spectral bands which represent the reflectance values recorded by the sensor (F1–F8 and NIR), a dedicated channel to detect ambient light flicker (CLEAR), and the sugar content (°Bx), ranging from 4.8 to 45 °Bx, encompassing all maturity stages from early ripeness to over-ripeness. The dataset is structured so that rows correspond to individual berries, and columns represent the measured variables, enabling statistical and machine learning analyses

Center wavelength (λp) (F1: 415 nm, F2: 445 nm, F3: 480nm, F4: 515nm, F5: 555nm, F6: 590nm, F7: 630nm, F8: 680nm)
description: The sampling of grape berries began in August 2024, after veraison and continuing through over-ripening. Samples were collected twice a week to represent all stages of maturity. Reference measurements of sugar levels and spectral reflectance were made in the laboratory. The degree of ripeness and sugar content were assessed individually for each berry using a digital refractometer. Reflectance spectra were acquired with a 11- Channel Spectral Sensor AS7341 (ams AG, Premstaetten, Austria). The spectral response is defined in the wavelengths from approximately 350nm to 1000nm. 8 optical channels cover the visible spectrum, center wavelength (λp) (415 nm, 445 nm, 480nm, 515nm, 555nm, 590nm, 630nm 680nm) and a spectral resolution of 20 nm One channel is used to measure near-infrared light (λp =910 nm) and one channel is a photo diode without filter (“clear”). This sensor is implemented on an Arduino-based development board. The measurements of the individual grape berries were conducted inside a 3D-printed dark box. Two integrated LEDs in the sensor were used to provide controlled light source. Additionally, a hyperspectral camera (Specim IQ, Specim, Finland) was used to take simultaneous measurements on the individual berries and random bunch. This camera features a spectral range from 400 nm to 1000 nm, a spectral resolution of 7 nm and 204 spectral bands.

The creation of a chemical and spectral dataset of Tempranillo grape berries represents a key result of this research, developed using multispectral sensors and hyperspectral imaging for maturity monitoring. This dataset comprises 900 individual berry samples and is accessible from the “Zenodo” open-access repository. It includes the degree of ripeness (ºBrix), corresponding to the reflectance spectra values obtained in the specified spectral bands of the multispectral sensor for each analyzed berry. This dataset is valuable not only for building, testing, and comparing models to predict the sugar content of grapes but also for applications in Machine Learning, including the development and training of Artificial Neural Networks (ANN). Such models can interpret the spectral data of the grapes, aiding in the prediction of ripening phases. In this study, Partial Least Squares Regression (PLSR) was employed to assess the cross-validation results of the prediction model for total sugar content. Preliminary results for cv. Tempranillo indicate an R² value, suggesting a reasonable level of predictive accuracy.

In conclusion, VIS-NIR spectroscopy techniques are valuable tools in viticulture for determining the optimal harvest time. In this study, we propose the development of a prototype that utilizes a low-cost spectral sensor to measure the spectral response of grapes. The ability to measure grape maturity in real-time, without the need for field sampling, can help winegrowers and winemakers improve the quality of their products and optimize the harvest. This approach will enable more accurate decision-making regarding when to harvest, ensuring the best characteristics of both the grapes and the wine. However, it is essential to validate this method under field measurement conditions, including the integration of communication protocols such as 5G, LoRa, and Wi-Fi networks, to ensure its reliability and effectiveness in real-world scenarios.