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Remote Sensing
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Remote Sensing in Viticulture II
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Remote Sensing in Viticulture II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 9470

Special Issue Editors

Dr. Luís Pádua

E-Mail Website
Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
Interests: digital image processing; unmanned aerial vehicles; precision viticulture; precision agriculture; photogrammetric processing; multi-temporal analysis; spectral imaging; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Emanuel Peres

E-Mail Website
Guest Editor
Department of Engineering, School of Sciences and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: remote sensing; precision agriculture; in-field data processing; remote monitoring; UAV; UAS; precision forestry; sensors and data processing; human–computer interfaces; augmented reality; virtual reality; embedded systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Raul Morais dos Santos

E-Mail Website
Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
Interests: sensor interfaces; microelectronics; wireless sensor networks; IoT; precision viticulture; energy harvesting; proximal sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The technological and scientific developments in the last several decades have allowed the emergence of new approaches for data acquisition and the processing of remote sensed data within the context of precision viticulture. Remote sensing contributes to improvements in decision-support systems, making it possible to retrieve a multitude of information. Vineyard mapping, monitoring phytosanitary issues, yield and quality estimation, and water status monitoring are among the viticulture applications that can be performed with remotely sensed data. In fact, current technology and methodologies enable vineyard monitoring at a parcel scale or at an individual plant scale, with the possibility of estimating different biophysical and geometrical plant parameters, including multi-sensor data fusion approaches.

Given the current widespread availability and accessibility for acquiring, processing and analyzing proximal and remotely sensed data, it is possible to employ these data within any specific period, regardless of the location. Constant updates to time series data with different spatial, spectral and temporal resolutions available from satellite systems enable continuous vineyard monitoring at, local, regional and global scales. The emergence of unmanned aerial systems and mobile or stationary proximal sensing platforms has made it possible to acquire huge amounts of data from various sensors. Considering environmental and economic sustainability, the use of tridimensional, multi- or hyperspectral, and thermal data opens new possibilities to promote more sustainable and efficient vineyard management, supporting the preservation of natural resources.

This Special Issue aims to encourage the publication of studies or review articles documenting recent advances in the viticulture sector using remote sensing and intelligent field monitoring. It aims to cover the development of novel methodologies, algorithms, and applications using remotely sensed data including, but not limited to: grapevine vegetation monitoring using unmanned aerial vehicles (UAVs), airborne and satellite data; vigor mapping and site-specific applications; time series and multi-temporal vineyard analysis; digital image processing, computer vision and machine learning methods applied in viticulture; precision viticulture methods; advances in proximal sensing in viticulture, including the use of image sensors; as well as the estimation and mapping of water status, irrigation demands, and phytosanitary issues. 

Dr. Luís Pádua
Dr. Emanuel Peres
Dr. Raul Morais
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precision viticulture
  • vineyard management
  • vineyard mapping and classification
  • machine and deep learning
  • decision support
  • phenological modelling and yield prediction
  • estimation of biophysical and geometrical parameters
  • intelligent monitoring
  • multi-temporal analysis

Published Papers (4 papers)

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Research

24 pages, 28702 KiB  
Article
Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery
by Milan Gavrilović, Dušan Jovanović, Predrag Božović, Pavel Benka and Miro Govedarica
Remote Sens. 2024, 16(3), 584; https://doi.org/10.3390/rs16030584 - 03 Feb 2024
Viewed by 1199
Abstract
Precision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection and vineyard zoning. Vine detection employs the [...] Read more.
Precision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection and vineyard zoning. Vine detection employs the YOLO (You Only Look Once) deep learning algorithm, achieving a remarkable 90% accuracy by analysing UAV imagery with various spectral ranges from various phenological stages. Vineyard zoning, achieved through the application of the K-means algorithm, incorporates geospatial data such as the Normalized Difference Vegetation Index (NDVI) and the assessment of nitrogen, phosphorus, and potassium content in leaf blades and petioles. This approach enables efficient resource management tailored to each zone’s specific needs. The research aims to develop a decision-support model for precision viticulture. The proposed model demonstrates a high vine detection accuracy and defines management zones with variable weighting factors assigned to each variable while preserving location information, revealing significant differences in variables. The model’s advantages lie in its rapid results and minimal data requirements, offering profound insights into the benefits of UAV application for precise vineyard management. This approach has the potential to expedite decision making, allowing for adaptive strategies based on the unique conditions of each zone. Full article
(This article belongs to the Special Issue Remote Sensing in Viticulture II)
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22 pages, 4887 KiB  
Article
Multispectral and Thermal Sensors Onboard UAVs for Heterogeneity in Merlot Vineyard Detection: Contribution to Zoning Maps
by Luz K. Atencia Payares, Ana M. Tarquis, Roberto Hermoso Peralo, Jesús Cano, Joaquín Cámara, Juan Nowack and María Gómez del Campo
Remote Sens. 2023, 15(16), 4024; https://doi.org/10.3390/rs15164024 - 14 Aug 2023
Cited by 2 | Viewed by 924
Abstract
This work evaluated the ability of UAVs to detect field heterogeneity and their influences on vineyard development in Yepes (Spain). Under deficit irrigation, vine growth and yield variability are influenced by soil characteristics such as water holding capacity (WHC). Over two irrigation seasons [...] Read more.
This work evaluated the ability of UAVs to detect field heterogeneity and their influences on vineyard development in Yepes (Spain). Under deficit irrigation, vine growth and yield variability are influenced by soil characteristics such as water holding capacity (WHC). Over two irrigation seasons (2021–2022), several vegetation indices (VIs) and parameters of vegetative growth and yield were evaluated in two field zones. Multispectral and thermal information was obtained from bare soils. The water availability showed annual differences; it was reduced by 49% in 2022 compared to 2021, suggesting that no significant differences were found for the parameters studied. The zone with higher WHC also had the higher vegetative growth and yield in 2021. This agreed with the significant differences among the VIs evaluated, especially the ratio vegetation index (RVI). Soil multispectral and thermal bands showed significant differences between zones in both years. This indicated that the soil spectral and thermal characteristics could provide more reliable information for zoning than vine vegetation itself, as they were less influenced by climatic conditions between years. Consequently, UAVs proved to be valuable for assessing spatial and temporal heterogeneity in the monitoring of vineyards. Soil spectral and thermal information will be essential for zoning applications due to its consistency across different years, enhancing vineyard management practices. Full article
(This article belongs to the Special Issue Remote Sensing in Viticulture II)
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25 pages, 4705 KiB  
Article
Modeling Phenology Combining Data Assimilation Techniques and Bioclimatic Indices in a Cabernet Sauvignon Vineyard (Vitis vinifera L.) in Central Chile
by Víctor García-Gutiérrez and Francisco Meza
Remote Sens. 2023, 15(14), 3537; https://doi.org/10.3390/rs15143537 - 14 Jul 2023
Viewed by 1241
Abstract
Phenology is a science that is fundamental to crop productivity and is especially sensitive to environmental changes. In Mediterranean and semi-arid climates, vineyard phenology is directly affected by changes in temperature and rainfall distribution, being highly vulnerable to climate change. Due to the [...] Read more.
Phenology is a science that is fundamental to crop productivity and is especially sensitive to environmental changes. In Mediterranean and semi-arid climates, vineyard phenology is directly affected by changes in temperature and rainfall distribution, being highly vulnerable to climate change. Due to the significant heterogeneity in soil, climate, and crop variables, we need fast and reliable ways to assess vineyard phenology in large areas. This research aims to evaluate the performance of the phenological data assimilation model (DA-PhenM) and compare it with phenological models based on meteorological data (W-PhenM) and models based on Sentinel-2 NDVI (RS-PhenM). Two W-PhenM approaches were evaluated, one assessing eco- and endo-dormancy, as proposed by Caffarra and Eccel (CaEc) and the widely used BRIN model, and another approach based on the accumulation of heat units proposed by Parker called the Grapevine Flowering Veraison model (GFV). The DA-PhenM evaluated corresponds to the integration between RS-PhenM and CaEc (EKF-CaEC) and between RS-PhenM and GFV (EKF-GFV). Results show that EKF-CaEc and EKF-GFV have lower root mean square error (RMSE) values than CaEc and GFV models. However, based on the number of parameters that models require, EKF-GFV performs better than EKF-CaEc because the latter has a higher Bayesian Index Criterion (BIC) than EKF-GFV. Thus, DA-PhenM improves the performance of both W-PhenM and RS-PhenM, which provides a novel contribution to the phenological modeling of Vitis vinifera L. cv Cabernet Sauvignon. Full article
(This article belongs to the Special Issue Remote Sensing in Viticulture II)
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26 pages, 23625 KiB  
Article
Swin-Transformer-YOLOv5 for Real-Time Wine Grape Bunch Detection
by Shenglian Lu, Xiaoyu Liu, Zixuan He, Xin Zhang, Wenbo Liu and Manoj Karkee
Remote Sens. 2022, 14(22), 5853; https://doi.org/10.3390/rs14225853 - 18 Nov 2022
Cited by 21 | Viewed by 5396
Abstract
Precise canopy management is critical in vineyards for premium wine production because maximum crop load does not guarantee the best economic return for wine producers. The growers keep track of the number of grape bunches during the entire growing season for optimizing crop [...] Read more.
Precise canopy management is critical in vineyards for premium wine production because maximum crop load does not guarantee the best economic return for wine producers. The growers keep track of the number of grape bunches during the entire growing season for optimizing crop load per vine. Manual counting of grape bunches can be highly labor-intensive and error prone. Thus, an integrated, novel detection model, Swin-transformer-YOLOv5, was proposed for real-time wine grape bunch detection. The research was conducted on two varieties of Chardonnay and Merlot from July to September 2019. The performance of Swin-T-YOLOv5 was compared against commonly used detectors. All models were comprehensively tested under different conditions, including two weather conditions, two berry maturity stages, and three sunlight intensities. The proposed Swin-T-YOLOv5 outperformed others for grape bunch detection, with mean average precision (mAP) of up to 97% and F1-score of 0.89 on cloudy days. This mAP was ~44%, 18%, 14%, and 4% greater than Faster R-CNN, YOLOv3, YOLOv4, and YOLOv5, respectively. Swin-T-YOLOv5 achieved an R2 of 0.91 and RMSE of 2.4 (number of grape bunches) compared with the ground truth on Chardonnay. Swin-T-YOLOv5 can serve as a reliable digital tool to help growers perform precision canopy management in vineyards. Full article
(This article belongs to the Special Issue Remote Sensing in Viticulture II)
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