Assessing and mapping vineyard water status using a ground mobile thermal imaging platform

Gutiérrez, Salvador; Fernández‐Novales, Juan; Diago, María P.; Iñíguez, Rubén; Tardáguila, Javier

doi:10.1007/s00271-021-00735-1

Cited by 13 publications

(3 citation statements)

References 43 publications

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“…In conclusion, our experiments indicate that careful tests for water potential determination must be performed on the plant material used for the experiments in order to improve measurement precision. These tests would be also pivotal in studies aimed at estimating plant water status from multispectral, environmental and thermal imaging data that use Ψ stem as reference method for validation [31,32]. Given the different patterns found among cvs.…”

Section: Discussionmentioning

confidence: 99%

Best Procedures for Leaf and Stem Water Potential Measurements in Grapevine: Cultivar and Water Status Matter

Tomasella

Calderan

Mihelčič

et al. 2023

Plants

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The pressure chamber is the most used tool for plant water status monitoring. However, species/cultivar and seasonal effects on protocols for reliable water potential determination have not been properly tested. In four grapevine cultivars and two times of the season (early season, Es; late season, Ls, under moderate drought), we assessed the maximum sample storage time before leaf water potential (Ψleaf) measurements and the minimum equilibration time for stem water potential (Ψstem) determination, taking 24 h leaf cover as control. In ‘Pinot gris’, Ψleaf already decreased after 1 h leaf storage in both campaigns, dropping by 0.4/0.5 MPa after 3 h, while in ‘Refosk’, it decreased by 0.1 MPa after 1 and 2 h in Es and Ls, respectively. In ‘Merlot’ and ‘Merlot Kanthus’, even 3 h storage did not affect Ψleaf. In Es, the minimum Ψstem equilibration was 1 h for ‘Refošk’ and 10 min for ‘Pinot gris’ and ‘Merlot’. In Ls, ‘Merlot Kanthus’ required more than 2 h equilibration, while 1 h to 10 min was sufficient for the other cultivars. The observed cultivar and seasonal differences indicate that the proposed tests should be routinely performed prior to experiments to define ad hoc procedures for water status determination.

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Section: Discussionmentioning

confidence: 99%

Best Procedures for Leaf and Stem Water Potential Measurements in Grapevine: Cultivar and Water Status Matter

Tomasella

Calderan

Mihelčič

et al. 2023

Plants

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show abstract

“…When applied to vines, this concept is known as precision viticulture (PV), which has a wide range of applications. These include detecting biomass [1] and water content [2], identifying plant diseases, conducting pest surveillance [3], analyzing grape maturity [4], estimation yields [5] and identifying grapevine varieties [6]. This study focuses on the latter, as the authenticity and classification of cultivars significantly impact wine production and are subject to governmental regulations [7].…”

Section: Introductionmentioning

confidence: 99%

Classification of Grapevine Varieties Using UAV Hyperspectral Imaging

López,

Ogayar,

Feito

et al. 2024

Remote Sensing

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Classifying grapevine varieties is crucial in precision viticulture, as it allows for accurate estimation of vineyard row growth for different varieties and ensures authenticity in the wine industry. This task can be performed with time-consuming destructive methods, including data collection and analysis in the laboratory. In contrast, unmanned aerial vehicles (UAVs) offer a markedly more efficient and less restrictive method for gathering hyperspectral data, even though they may yield data with higher levels of noise. Therefore, the first task is the processing of these data to correct and downsample large amounts of data. In addition, the hyperspectral signatures of grape varieties are very similar. In this study, we propose the use of a convolutional neural network (CNN) to classify seventeen different varieties of red and white grape cultivars. Instead of classifying individual samples, our approach involves processing samples alongside their surrounding neighborhood for enhanced accuracy. The extraction of spatial and spectral features is addressed with (1) a spatial attention layer and (2) inception blocks. The pipeline goes from data preparation to dataset elaboration, finishing with the training phase. The fitted model is evaluated in terms of response time, accuracy and data separability and is compared with other state-of-the-art CNNs for classifying hyperspectral data. Our network was proven to be much more lightweight by using a limited number of input bands (40) and a reduced number of trainable weights (560 k parameters). Hence, it reduced training time (1 h on average) over the collected hyperspectral dataset. In contrast, other state-of-the-art research requires large networks with several million parameters that require hours to be trained. Despite this, the evaluated metrics showed much better results for our network (approximately 99% overall accuracy), in comparison with previous works barely achieving 81% OA over UAV imagery. This notable OA was similarly observed over satellite data. These results demonstrate the efficiency and robustness of our proposed method across different hyperspectral data sources.

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“…El impacto del estrés hídrico en la región mediterránea sobre el comportamiento productivo y cualitativo de los viñedos es un tema de estudio de gran relevancia en las últimas décadas (Gutiérrez et al, 2021;Ohana-Levi et al, 2022 ;Razavi, et al, 2008). El cambio climático y la escasez de agua que puede generar amenazan hoy al rendimiento del viñedo y a la calidad del vino a escala mundial (IPCC, 2014).…”

Section: El Déficit Hídricounclassified

Efectos combinados de la clorosis férrica y el estado hídrico del viñedo en la calidad de la uva y el vino cv. Tempranillo

Alonso¹

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Assessing and mapping vineyard water status using a ground mobile thermal imaging platform

Cited by 13 publications

References 43 publications

Best Procedures for Leaf and Stem Water Potential Measurements in Grapevine: Cultivar and Water Status Matter

Best Procedures for Leaf and Stem Water Potential Measurements in Grapevine: Cultivar and Water Status Matter

Classification of Grapevine Varieties Using UAV Hyperspectral Imaging

Efectos combinados de la clorosis férrica y el estado hídrico del viñedo en la calidad de la uva y el vino cv. Tempranillo

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