In Mediterranean countries, due to warmer and drier environmental conditions, viticulture faces problems such as drought, short biological cycle, and frequent infestations by pests. Precision Viticulture (PV), through spatially variable rate application of crop inputs, enhances grape yield and quality, while reducing operational costs and environmental impact. PV has been a reality in Mediterranean countries since the end of the last century. Proximal/remote sensors such as LIght Detection And Ranging (LIDAR), soil electrical conductivity proximal sensors, and remote sensing from Unmanned Aerial Vehicles (UAVs) and/or satellites and/or robots can monitor vegetation vigour, thus delineating vineyard Management Zones (MZs), needing specific crop input amounts. MZs enable us to plan either temporally variable vintage in zones having different ripeness periods for producing uniform quality grapes and, therefore, wine, or spatially variable vintage for producing different quality grapes and, therefore, wines. The economic and environmental benefits of PV should be quantified by refining user-friendly data processing software and developing models to better understand and address the within-vineyard spatial variability of crop and soil parameters. Our aim of this review article was to highlight the ways PV can be implemented in Mediterranean countries to face problems such as drought, short biological cycle, and frequent infestations by pests.
Citation: Antonio Comparetti, Evangelos Anastasiou, Aikaterini Kasimati, Spyros Fountas. Precision viticulture in Mediterranean countries: From vegetation vigour and yield maps to spatially and temporally variable vintage[J]. AIMS Agriculture and Food, 2025, 10(2): 390-422. doi: 10.3934/agrfood.2025020
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[10] | Michael S. Badowski, Angela Muise, David T. Harris . Patient use of autologous cryopreserved intact adipose tissue from lipoaspirate. AIMS Cell and Tissue Engineering, 2017, 1(3): 224-235. doi: 10.3934/celltissue.2017.3.224 |
In Mediterranean countries, due to warmer and drier environmental conditions, viticulture faces problems such as drought, short biological cycle, and frequent infestations by pests. Precision Viticulture (PV), through spatially variable rate application of crop inputs, enhances grape yield and quality, while reducing operational costs and environmental impact. PV has been a reality in Mediterranean countries since the end of the last century. Proximal/remote sensors such as LIght Detection And Ranging (LIDAR), soil electrical conductivity proximal sensors, and remote sensing from Unmanned Aerial Vehicles (UAVs) and/or satellites and/or robots can monitor vegetation vigour, thus delineating vineyard Management Zones (MZs), needing specific crop input amounts. MZs enable us to plan either temporally variable vintage in zones having different ripeness periods for producing uniform quality grapes and, therefore, wine, or spatially variable vintage for producing different quality grapes and, therefore, wines. The economic and environmental benefits of PV should be quantified by refining user-friendly data processing software and developing models to better understand and address the within-vineyard spatial variability of crop and soil parameters. Our aim of this review article was to highlight the ways PV can be implemented in Mediterranean countries to face problems such as drought, short biological cycle, and frequent infestations by pests.
Treatment of heart failure remains a major medical challenge in the US and around the globe. Although several treatments modalities exist for patients with heart failure, heart transplant remains the most effective mitigator. However, there exists a concerning deficit of organs for transplantation; the number of adults on the waiting list for heart transplants has increased by 34.2% from 2003 to 2013 [1]. With organ donors in such high demand, it is crucial that alternative treatment paradigms are developed.
Tissue engineering focuses on the development of biological substitutes that serve in restoring, replacing, or improving the function of damaged tissues [2]. More specifically, cardiac tissue engineering examines the development of functional myocardium used to improve the lost functionality of the infarcted myocardium, in addition to modeling the physiology of cardiac development and diseases in vitro [3].
Based on current state of the art, functional cardiac constructs can be fabricated with a myriad of approaches including employing the use of biodegradable gels, polymeric scaffolds, and self-organization organization techniques [4,5,6,7]. In more detail, Radisic et al. used highly porous collagen scaffolds along with neonatal rat ventricular heart cells to develop engineered cardiac tissues [5]. Bursac et al. seeded neonatal rat ventricular cells on polyglycolic acid (PGA) scaffolds to fabricate cardiac muscle constructs [4]. Shimizu et al. utilized a self-organization strategy to construct three-dimensional cardiac constructs by stacking chick embryonic cardiomyocyte cell sheets on top of each other using a thermal sensitive polymer; yielding heart-like tissue constructs [6]. We have also developed heart muscle and other cardiovascular components [8,9,10,11,12,13]. Such progress includes, 3D-AHM that was formed using primary cardiac myocytes utilizing two different methods (layering and embedding) in which both methods showed functional tissue development [11]. In a separate study, cardioid tissue was produced using PDMS and laminin coated plates to culture neonatal cardiomyocytes [8].
As our group and others researchers continue to advance models for heart muscle and other tissues, it becomes vital to create systems with the ability to accurately assess their functionality. It is important to fabricate cardiac constructs that closely resemble native tissue in structure and performance. The intricate ansiotropic structure of the native cardiac muscle drives the electrical activity within the heart [14]. It is these electrical impulses that regulate the cardiac cycle and maintain sphygmic synchronisity. Understanding the electrical characteristics of cardiac constructs developed in our laboratory is crucial to properly replicate the functional attributes of native tissue. Here, we address the need of such characterization by developing a novel 16 electrode non-invasive system to record the electrical potentials of 3D-AHM. The results of this research can lead to improvement in our methodology and therefore in the viability of the constructs.
Several systems have been used in the past by other researchers and within our laboratory to assess the electrical properties of cardiac constructs [4,15,16]. One research group used a custom fabricated cylindrical plexiglass chamber fitted into an electrically grounded brass casing. This system used 8 microelectrodes placed 1.5mm to 5mm away from the site of stimulation [4]. Other groups have used the commercially available MEA1060 amplifier from Multi Channel Systems (Reutigen, Germany). This system is composed of 60 channels that record the EKG signals produced by cardiac constructs [15,17,18]. We developed a 32 channel direct contact system used with 3D-AHM constructs anaolgus to our present study [16]. This 32 channel direct contact system proved efficient in gathering all desired metrics, however, our goal is to move toward less invasive sensing systems as tearing of the constructs was seen at the tissue electrode interface; the result of the affixed gold plated pins penetrating the construct surface and therefore diminishing the viability of our tissues.
For this study, we developed a noninvasive direct contact system to assess the electrophysiological properties of 3D-AHM. First, we fabricated our cardiac constructs by seeding rat neonatal primary cardiac cells on a fibrin gel. Remodeling of the cells and integration at the cell-scaffold level lead to the formation of functional tissues. Second, we fabricated a custom 16 channel noninvasive electrode board compatible with the RHD2000-Series Amplifier Evaluation System (Intan Technologies, Los Angeles, CA). With this system we were able to produce electrical maps of the impulse propagation, calculate CVs, and acquire other metrics associated with the electrical properties of our heart muscle constructs.
The Institutional Animal Care and Use Committee (IACUC) at the University of Houston approved all animal protocols in accordance with the "Guide for the Care and Use of Laboratory Animals" (NIH publication 86–23,1986).
Neonatal primary cardiac cells were isolated from the hearts of 2 to 3 day old Sprague-Dawley rats employing methods previously established [9]. Each heart was divided into 3 to 4 pieces and placed in an ice-cold PBS phosphate buffer comprised of 116 mM NaCl, 20 mM HEPES, 5.5 mM glucose, 5.4 mM KCl, 1 mM Na2HPO4, and 0.8 mM MgSO4. After the blood cells were gently rinsed, the pieces were transferred to a second phosphate buffers solution for additional mincing. Tissues were cut into 1 mm2 fragments and placed in a dissociation solution (DS) consisting of 0.32 mg/mL collagenase type 2-filtered (Worthington Biochemical Corporation, Lakewood, NJ) and 0.6 mg/mL pancreatin in phosphate buffer. The tissue sections and 15mL of DS were placed in an orbital shaker for 30 minutes at 37 ℃ and 60 rpm to carry out serial digestion. After the 1st digestion was completed, the supernatant was collected in 3 mL of horse serum to neutralize the enzyme, and placed in a centrifuge for 5 min at 1000 rpm and 4 ℃. The cell pellet was re-suspended in 5 mL of horse serum and maintained in an incubator at 37 ℃, supplied with 5% CO2. Fresh DS was added to the partially digested tissue and the digestion process was repeated 2 to 3 times. The acquired cells were then pooled, centrifuged and suspended in culture medium (CM). The CM used consists of M199 (Life Technologies, Grand Island, NY) along with 20% F12K (Life Technologies), 10% fetal bovine serum, 5% horse serum, 1% antibiotic-antimycotic, 40 ng/mL hydrocortisone, and 100 ng/mL insulin. Trypan blue (4%) staining, was performed according to the manufacturer's protocol to analyze cell viability.
All 35 mm tissue culture plates were coated with 2 mL of SYLGARD (PDMS, type 184 silicone elastomer) (Dow Chemical Corporation, Midland, MI). The plates were air dried for 2 weeks allowing the silicone coating to form properly. Once the coating had cured completely, the plates were sterilized with 80% ethanol before use. 4 minutien pins (Fine Science Tools, Foster City, CA) 0.1 mm in diameter, were placed into the PDMS coating of the culture plate to serve as anchor points and form a 20 mm × 20 mm square.
The 3D-AHM was fabricated using a fibrin gel and isolated primary cardiac cells. Fibrin gel was formed by adding 1mL of CM containing 10 U/mL thrombin to the PDMS coated surface of each culture plate. Subsequently, 500 μL of saline containing 20 mg/mL fibrinogen was added. Fibrinogen is cleaved and cross-linked by exposure to thrombin, yielding an insoluble fibrin scaffold ideal for construct fabrication [7]. Once both solutions had been added, the culture plates were shaken well to ensure complete mixing and complete plate coverage and placed in the incubator for 45 minutes to stimulate the formation of the gel. Primary cardiac cells, isolated from the entire neonatal rat hearts, were diluted in CM at 2 million cells/mL and 2 mL of the solution were added to each plate with fibrin gel. The cells were cultured in an incubator at 37 ℃ supplied with 5% CO2; media was changed every 2 days. This process resulted in 3D tissue with dimensions of 20 mm × 20 mm and a thickness of ~ 200–300 μm.
After 3D-AHM was formed, contracting synchronously (5–6 days in culture), contractile forces were measured using a highly sensitive TRI202PAD micro-force transducer (Panlab, Barcelona, Spain). In efforts to avoid damage to the construct and minimize the amount of force lost due to vibration, a thin rigid wire was used to attach the tissues to the force transducer. The output signals from the force transducer were processed using the PowerLab16/35 data acquisition system (ADInstruments, Colorado Springs, CO), and then transmitted to LabChart for analysis where the peak analysis feature was used to calculate the maximum twitch force of the tissue constructs.
Heart muscle constructs were directly fixed in ice-cold acetone for 10 minutes. Nonspecific epitope antigens were blocked by using 10% goat serum for 1 hour at room temperature. The tissue segments were then incubated with mouse anti-α-actinin monoclonal antibody (1:200, Sigma, A7811), rabbit anti-collagen type Ⅰ (1:100, Abcam, ab34710), and rabbit anti-connexin 43 (1:100, Abcam, ab11370) for 2 hours at room temperature. Next, the tissue fragments were treated with both goat anti-mouse and goat anti-rabbit secondary antibodies 1:400 (Alexa Fluor 488 and Alexa Fluor 546; Life Technologies, Grand Island, NY) for 1 hour at room temperature. Nuclei were counterstained with 2.5 μg/mL 4, 6-diamidino-2-phenylindole (DAPI) at room temperature for 5 minutes. Lastly, the tissue samples were placed on VWR® Microslides and fluorescent images were produced using a Nikon C2+ confocal laser-scanning microscope (Nikon Instruments Inc., Melville, NY).
The RHD2000-Series amplifier evaluation system (Intan Technologies, Los Angeles, CA) was purchased. This highly customizable system includes an Opal Kelly XEM 6010 USB/TPGA interface module, which is capable of supporting up to 256 low noise amplifier channels with sampling rates that range from 1 to 30 kS/s. For this study we used a sampling rate of 5 kS/s and a bandwidth between 0.09 Hz and 1.00 kHz. Additionally, we designed and fabricated a custom 33 mm diameter circular printed circuit board (PCB) with 16 electrodes (5 mm × 5 mm square size each) (Figure 1). The PBC material used was standard FR-4 (glass reinforced epoxy laminate) at 1.6 mm thick. All 16 electrodes were placed on the top-side of the PBC with thin copper conductive traces connecting them on the bottom side to a standard Omnetics connector that is compatible with our bio-potential amplifiers (Figure 1). The 33 mm diameter PBC fit within a standard 35 mm tissue culture plate. For our initial testing the electrodes were tinned (Figure 1). In the future, an immersion gold process (ENIG) can be used for better biocompatibility if needed.
To aid in assessing the EKG properties of our fabricated constructs, a Cole-Parmer Stable Dry Temp Dry Block Heater (Vernon Hills, IL) was set to 37 ℃ to maintain the tissues during the testing period (Figure 2). The tissue construct was removed from the culture plate and placed on top of the electrode board with the top surface facing down for maximum cell to electrode contact (Figure 2). The electrical potential of each tissue construct was measured for up to 30 minutes, and the data was stored in 1 minute RHD file recordings (Figure 3). We tested 3 tissue constructs using this approach and present representative tracings in the results section.
Once the data collection process had concluded the raw data was run through an open-source m-file provided by Intan Technologies to import the data into MATLAB (MathWorks, Natick, MA) for analysis. Only files that displayed consistent readings as well as minimal artifacts from acoustic or electronic sources were chosen as representative of the desired data set. The chosen files were then run through a custom m-file to retrieve the cross-correlation values between the signals of each channel and the chosen reference channels. This information along with the sampling rate was used to construct time delay tables, which were used to generate the electrical impulse propagations maps. The electrical maps are made by assigning a color range to the values of the time delay tables, making it possible to visually express the impulse propagation from the reference channel to the 15 other channels with respect to time. The electrical potential of each data set was also evaluated using a second custom m-file, which allowed us to obtain the average wave amplitude, time to peak, and relaxation time (Figure 3).
During culture of 3D-AHMs, we observed that the delamination began approximately after 3 days, which led to the formation of the desired shape with the minutien pins as anchor points. The delamination process is directed by the contractions in the cell layer that initially forms on the surface of the fibrin gel. Within 4 to 5 days, the tissues are formed, contracting more synchronously, and ready for testing. Contractile force was measured and found to be approximately 600 μN for tissues that have been in culture from 6 to 7 days after initial cell plating (Figure 4). Furthermore, immunostaining showed the presence of many cardiac markers (Figure 5).
Once the tissues had formed (4 to 5 days in culture), our novel system was used to evaluate the electrophysiological properties of the 3D-AHM. With reference channels at each corner of the electrode board, the chosen representative files for the acquired data were used to retrieve the cross-correlation values between the signals of each channel and the chosen reference channels. Cross-correlation yields a peak where the signals overlap, which corresponds to the number of samples the signals are lagging by with respect to the reference. This information along with the sampling rate was used to determine the time delays between the signals, which we found to be in the range of 0–38 ms.
After time delays were determined, we constructed tables of the values respective to the location of the electrodes on the sampling board (Figure 6). Time delay tables were then used to illustrate the propagation of the electrical potential within the tissues by using electrical maps which also yielded information regarding signal origin within the tissue; channel 10 (Figure 7). With the data obtained from the time delays (Figure 8a), we were able to construct the impulse propagation map shown in Figure 8b.
Once the origin of contraction was determined the localized CVs corresponding to the 3D-AHM with respect to that channel were calculated. The calculations were based on the values acquired for the impulse propagation and the known distances traveled between the 16 channels. With this reference channel we were able to calculate overall CVs radiating from channel 10. We found the CV from the reference to the upper left corner of the board (channel 1) to be 62.4 cm/s, to the upper right corner (channel 4) 44.8 cm/s, to the lower left corner (channel 13) 72.5 cm/s, and to the lower right corner (channel 16) 169.1 cm/s (Figure 8c). We were also able to compute localized CVs in the range of 20–170 cm/s as shown in Figure 8d.
The raw data was processed through a second custom m-file to perform peak analysis of the acquired data. We extracted 10s segments of raw data from all 16 channels (Figure 9a), and we were able to determine the average wave amplitude, time to peak, and relaxation time (Figure 9b). The average wave amplitude was found to be 159.7 ± 22 μV. The average time to peak was found to be 169.5 ± 7.9 ms. Finally, the average relaxation time was 163.6 ± 7.8 ms. Overall duration of the electrical potential cycle was calculated to be 360 ± 0.4 ms.
The mammalian heart lacks the innate ability to self-repair damaged sections after acute myocardial infarction. The development of tissue equivalents that can repair or replace damaged sections of the heart has become an alternative paradigm where other treatments may fail. Cardiac tissue engineering has progressed to a degree where many different strategies may be employed to develop tissue constructs. However, current techniques concomitant with an appreciable deficit of adequate instrumentation have not yielded a facultative match to native heart muscle [3].
Our custom fabricated 16-electrode board, compatible with the RHD2000 Evaluation System from Intan Technologies, is capable of addressing concerns associated with previously devised methods. One such issue is fractionation of the waveforms and low amplitudes recorded by one research group [4]. This problem is avoided in our system due to a higher resolution during data acquisition. Another group using a separate commercially available system was able to obtain a narrow range of data compared to the data acquired by our system [15]. Lastly, a 32 channel direct contact system developed within our laboratory proved efficient in gathering all desired metrics [16], however, the pins in contact with the fibrinogen-cardiomyocyte construct caused some tearing at the tissue interface level. All propagation maps acquired during this study showed a trend of time increasing as distance from the reference increases. However, at specific channels the trend is not seen, leading to the conclusion that the tissue constructs have asynchronous impulse propagation at some points, which may be a result of cell agglomeration during the fabrication process.
Immunohistological assessment of our tissues was also performed to validate the development of cardiac muscle by observing markers commonly seen in 3D-AHM. Positive staining for α-actinin (green) reveals z-lines of cardiac myofibrils. Positive staining for collagen type Ⅰ (red) indirectly shows the presence cardiac fibroblasts within the 3D-AHM. As well, positive staining for connexin 43 (yellow) illustrates evidence of electromechanical coupling within the tissue. Staining for, and demonstrating the presence of these markers verifies the development of heart muscle (Figure 5a–5b). Additionally, acquired z-stack images of the tissues with these specific markers show the three dimensionality of the constructs and some degree of tissue level organization (Figure 5c–5d). Moreover, a study, previously published by our lab on the optimization of our tissues, shows the presence of similar markers analyzed during this study as well as other cardiac markers. This process not only provides further insight into the characterization of our tissues, but also proves the presence of other types of cells that are needed to support the development of 3D-AHM [19].
Understanding the electrical impulse propagation within the myocardium is crucial for proper tissue development. It is these impulses that regulate the cardiac cycle and the synchronized contraction of the heart. The software interface provided by Intan Technologies allowed us to modify the bandwidth and amplifier-sampling rate to obtain the best signal possible. Our system recorded the electrical potentials of the constructs for up to 30 minutes, which were automatically broken up into separate 1-minute RHD files. These files were run through an open source MATLAB file to extract the data and perform any necessary analysis. This allowed us to look at each individual channel to obtain time delays, impulse propagation, CVs, and perform peak analysis.
Computation of the time delay was accomplished by obtaining the maximum value of the cross correlation function, which points to the instant where the signals are best aligned yielding the number of samples the signal is lagging by with respect to the chosen reference. The properties of cross correlation minimize errors in the calculation process, despite the presence of noise. The obtained sample lag from each channel multiplied by the sampling rate used (5.00 kS/s) allowed us to obtain the time delay between each signal and the reference. The acquired time delays were used to construct tables that displayed the values with respect to each reference as can be seen in Figure 6. Next, we constructed electrical maps to illustrate impulse propagation across 3D-AHM with respect to the chosen reference (Figure 7). We discovered that the tissues exhibited a trend in impulse propagation, however asynchronous characteristics were present at certain regions of the constructs, leading to an overall non-uniform electrical dispersion. The incongruity can be explained by the presence of cardiac pacemaker cells, which can cause contraction to start spontaneously at different regions of the 3D-AHM. Additionally, uniform contraction depends on the proportion of fibroblasts, smooth muscle cells and endothelial cells [19]. Seeing that these supporting cells have a faster proliferation rate than cardiomyocytes, this may also justify the discrepant propagation of the electrical impulse. Additionally, the absence of uniformity may be explained by the lack of electrical or cyclical mechanical stimulation, which has previously demonstrated a penchant for increasing membrane polarization of both N-cadherin and connexin 43 junctions leading to a more highly coordinated contraction [14]. Both stimuli paradigms will be implemented in subsequent studies to better condition 3D-AHM during the culturing process. In the next step of the process, we evaluated both the electrical maps constructed and the time delays calculated to determine the approximate origination location of the signal for the construct, which pointed to channel 10 as that origin. A time delay table and electrical map were constructed using this channel as a reference (Figure 8a–8b). From the time delay tables we were also able to obtain the CVs of the 3D-AHM (Figure 8c). The previous analysis of the electrical potential signals for each channel along with known distances between channels were used to compute the overall and localized CVs over the total area of the construct, which could not be achieved by systems used by other research groups. In previous studies, a research group found the neonatal ventricular electrical signal propagation to be 21.84 ± 1.48 and that of adult ventricles to be 31.69 ± 4.44 cm/s [4]. Other researchers reported that their bioengineered cardiac constructs exhibited CVs of 9.35 ± 0.27 cm/s and 11.89 ± 0.46 cm/s when enriched. Another group showed an average impulse propagation of 8.6 ± 2.3 cm/s in their cardiac constructs [5]. We observed overall and localized CVs to be in the range of 20–170 cm/s for 3D-AHM. Our calculated values differ from those found in literature, which may be explained by the usage of different methodology during the fabrication process. Additionally, the majority of studies performed in the past make use of ventricular cells, while we use cells found in both the atria and the ventricles. The contraction rate of atrial cells has been found to be 13–100 bpm and that of ventricular cells to be 14–42 bpm [20]. The wider range seen with atrial cells can be attributed to the presence of cardiac pacemaker cells, which have a contraction rate of 80–100 bpm. It is the presence of these faster beating cells in our tissues that can lead to higher CVs than those found elsewhere. As shown in a previously developed study, cells that aggregate during culture and that beat faster than other cells become the pacemakers and set the rhythm for contraction [21]. Moreover, our previous study [16], determined that mechanical movement of the construct during contraction did not account for the high CVs supporting our belief that distribution of different cell types across our tissues has the greatest influence over the CVs. In order to determine the validity of our hypothesis, studies of the electrophysiological properties of 3D-AHM fabricated with only ventricular cells need to be performed in the future.
Once all possible metrics regarding electrical impulse of the constructs were acquired, the files were processed using a secondary custom MATLAB program to perform peak analysis of the acquired raw data. From our tests we calculated the average wave amplitude to be 159.7 ± 22 μV. Bursac et al. acquired average amplitudes of 260 ± 90 μV for their regular constructs and 430 ± 140 μV for enriched constructs [4]. Our values are lower most likely from inverting the constructs onto the surface of the electrode board, possibly leading to some damage and/or loss of function. We also found the average time to peak (169.5 ± 7.9 ms), average relaxation time (163.6 ± 7.8 ms), and overall duration of the electrical potential (360 ± 0.4 ms). This overall interval duration is longer than the duration of the QRS complex seen in normal rats (23 ± 5 ms) [22]. This discrepancy can be explained by the difference in contractile function between 3D-AHM and native rat heart, which we hope to close over time. Although, 3D-AHM shows the formation of three-dimensional cardiac tissues, further improvements are necessary to better resemble the functional properties of the native heart muscle.
The researchers would like to acknowledge NIH for provision of funding for this research (Grant number: R01-EB011516). We would also like to thank the Department of Biomedical Engineering and the Cullen College of Engineering at the University of Houston for further financial support.
All authors declare no conflicts of interest in this paper.
[1] |
Fountas S, Espejo-Garcia B, Kasimati A, et al. (2020) The future of digital agriculture: technologies and opportunities. IT Prof 22: 24–28. https://doi.org/10.1109/MITP.2019.2963412 doi: 10.1109/MITP.2019.2963412
![]() |
[2] | Paice MER, Day W (1997) Using computer simulation to compare patch spraying strategies. In: Stafford JV (Ed.), Precision Agriculture '97: Spatial Variability in Soil and Crop, Oxford, BIOS Scientific Publishers, 1: 421–428. |
[3] | McGovern EA, Holden NM, Ward SM, et al. (1998) Calibration of Landsat Thematic Mapper imagery for milled peat density prediction. Jyvaskyla, Finlandia. |
[4] | ISPA—International Society of Precision Agriculture. Precision Ag definition. Available from: https://www.ispag.org/about/definition. |
[5] |
Santillán D, Garrote L, Iglesias A, et al. (2020) Climate change risks and adaptation: New indicators for Mediterranean viticulture. Mitig Adapt Strateg Glob Change 25: 881–899. https://doi.org/10.1007/s11027-019-09899-w doi: 10.1007/s11027-019-09899-w
![]() |
[6] | Santesteban LG (2019) Precision viticulture and advanced analytics. A short review. Food Chem 279: 58–62. https://doi.org/10.1016/j.foodchem.2018.11.140 |
[7] |
Tanda G, Chiarabini V (2019) Use of multispectral and thermal imagery in precision viticulture. J Physiol: Conf Ser 1224: 012034. https://doi.org/10.1088/1742-6596/1224/1/012034 doi: 10.1088/1742-6596/1224/1/012034
![]() |
[8] | Arnó J, Martínez Casasnovas JA, Ribes Dasi M, et al. (2009) Review. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management. Span J Agric Res 7: 779. https://doi.org/10.5424/sjar/2009074-1092 |
[9] |
Sishodia RP, Ray RL, Singh SK (2020) Applications of remote sensing in precision agriculture: A review. Remote Sens 12: 3136. https://doi.org/10.3390/rs12193136 doi: 10.3390/rs12193136
![]() |
[10] |
Matese A, Di Gennaro F (2015) Technology in precision viticulture: A state of the art review. Int J Wine Res 5: 69–81. https://doi.org/10.2147/IJWR.S69405 doi: 10.2147/IJWR.S69405
![]() |
[11] | Anderson G, van Aardt J, Bajorski P, et al. (2016) Detection of wine grape nutrient levels using visible and near infrared 1 nm spectral resolution remote sensing. In: Valasek J, Thomasson JA (Eds.), Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 9866: 70–83. |
[12] |
Mashalaba L, Galleguillos M, Seguel O, et al. (2020) Predicting spatial variability of selected soil properties using digital soil mapping in a rainfed vineyard of central Chile. Geoderma Reg 22: e00289. https://doi.org/10.1016/j.geodrs.2020.e00289 doi: 10.1016/j.geodrs.2020.e00289
![]() |
[13] |
Kandylakis Z, Falagas A, Karakizi C, et al. (2020) Water stress estimation in vineyards from aerial SWIR and multispectral UAV data. Remote Sens 12: 2499. https://doi.org/10.3390/rs12152499 doi: 10.3390/rs12152499
![]() |
[14] |
Palacios F, Bueno G, Salido J, et al. (2020) Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions. Comput Electron Agric 178: 105796. https://doi.org/10.1016/j.compag.2020.105796 doi: 10.1016/j.compag.2020.105796
![]() |
[15] |
Brillante L, Martínez-Lüscher J, Yu R, et al. (2020) Carbon isotope discrimination (δ13 C) of grape musts is a reliable tool for zoning and the physiological ground-truthing of sensor maps in precision viticulture. Front Environ Sci 8: 561477. https://doi.org/10.3389/fenvs.2020.561477 doi: 10.3389/fenvs.2020.561477
![]() |
[16] |
Perry EM, Pierce FJ, Davenport JR, et al. (2009) Comparing active optical and airborne measurements of grape canopies. Acta Hortic 824: 75–84. https://doi.org/10.17660/ActaHortic.2009.824.8 doi: 10.17660/ActaHortic.2009.824.8
![]() |
[17] |
Anastasiou E, Balafoutis A, Theocharis S, et al. (2022) Assessment of laser scanner use under different settings in two differently managed vineyards for estimating pruning wood parameters. AgriEngineering 4: 733–746. https://doi.org/10.3390/agriengineering4030047 doi: 10.3390/agriengineering4030047
![]() |
[18] |
Anastasiou E, Castrignanò A, Arvanitis K, et al. (2019) A variability multi-source data fusion approach to assess spatial-temporal and delineate homogeneous zones: A use case in a table grape vineyard in Greece. Sci Total Environ 684: 155–163. https://doi.org/10.1016/j.scitotenv.2019.05.324 doi: 10.1016/j.scitotenv.2019.05.324
![]() |
[19] |
Henry D, Aubert H, Veronese T, et al. (2017) Remote estimation of intra-parcel grape quantity from three-dimensional imagery technique using ground-based microwave FMCW radar. IEEE Instrum Meas Mag 20: 20–24. https://doi.org/10.1109/MIM.2017.7951687 doi: 10.1109/MIM.2017.7951687
![]() |
[20] |
Vanino S, Pulighe G, Nino P, et al. (2015) Estimation of evapotranspiration and crop coefficients of Tendone vineyards using multi-sensor remote sensing data in a Mediterranean environment. Remote Sens 7: 14708–14730. https://doi.org/10.3390/rs71114708127 doi: 10.3390/rs71114708127
![]() |
[21] |
Anastasiou E, Balafoutis AT, Fountas S (2022) Trends in remote sensing technologies in olive cultivation. Smart Agr Technol 3: 100103. https://doi.org/10.1016/j.atech.2022.100103 doi: 10.1016/j.atech.2022.100103
![]() |
[22] |
Cogato A, Meggio F, Collins C, et al. (2020) Medium-resolution multispectral data from sentinel-2 to assess the damage and the recovery time of late frost on vineyards. Remote Sens 12: 1896. https://doi.org/10.3390/rs12111896 doi: 10.3390/rs12111896
![]() |
[23] | Pagay V, Kidman CM (2019) Evaluating remotely-sensed grapevine (Vitis Vinifera L.) water stress responses across a viticultural region. Agronomy 9: 682. https://doi.org/10.3390/agronomy9110682 |
[24] |
Albetis J, Jacquin A, Goulard M, et al. (2019) On the potentiality of UAV multispectral imagery to detect Flavescence dorée and grapevine trunk diseases. Remote Sens 11: 23. https://doi.org/10.3390/rs11010023 doi: 10.3390/rs11010023
![]() |
[25] |
Soliman A, Heck RJ, Brenning A, et al. (2013) Remote sensing of soil moisture in vineyards using airborne and ground-based thermal inertia data. Remote Sens 5: 3729–3748. https://doi.org/10.3390/rs5083729 doi: 10.3390/rs5083729
![]() |
[26] |
Droulia F, Charalampopoulos I (2021) Future climate change impacts on European viticulture: A review on recent scientific advances. Atmosphere 12: 495. https://doi.org/10.3390/atmos12040495 doi: 10.3390/atmos12040495
![]() |
[27] |
Dinis LT, Bernardo S, Yang C, et al. (2022) Mediterranean viticulture in the context of climate change. Ciência y Técnica in Vitivinicoltura 37: 139–158. https://doi.org/10.1051/ctv/ctv20223702139 doi: 10.1051/ctv/ctv20223702139
![]() |
[28] | Bramley RGV (2001) Variation in the yield and quality of winegrapes and the effect of soil property variation in two contrasting Australian vineyards. Final Report on GWRDC—Project No. CSL00/01. CSIRO Land and Water/Grape and Wine Research and Development Corporation. |
[29] | Bramley RGV (2001) Progress in the development of precision viticulture—Variation in yield, quality and soil properties in contrasting Australian vineyards. Final Report on GWRDC—Project No. CSL00/01. CSIRO Land and Water/Grape and Wine Research and Development Corporation. |
[30] | Bramley RGV (2002) Travel to Minneapolis and California USA to attend the 6th International Conference on Precision Agriculture and meet with US wine industry personnel interested in Precision Viticulture. Final Report on GWRDC—Project No. CSL02/01. CSIRO Land and Water/Grape and Wine Research and Development Corporation. |
[31] | Bramley RGV (2002) Precision viticulture—Tools to optimise winegrape production in a difficult landscape. Final Report on GWRDC—Project No. CSL02/01. CSIRO Land and Water/Grape and Wine Research and Development Corporation. |
[32] | Castagnoli A, Dosso P (2001) Viticoltura assistita da satellite (Satellite aided viticulture). L'informatore agrario 18: 77–81 (in Italian). |
[33] | Castagnoli A, Dosso P (2002) Servizi ad alta tecnologia per la viticoltura di precisione (High technology services for precision viticulture). L'informatore agrario (appendix) 13: 57–62 (in Italian). |
[34] | Tagarakis A, Liakos V, Fountas, S, et al. (2011) Using soil and landscape properties to delineate management zones in vines. Tarım Makinaları Bilimi Dergisi 7: 33–38. |
[35] | Tisseyre B, Taylor J (2005) An overview of methodologies and technologies for implementing precision agriculture in viticulture. In: XⅡ Congresso Brasileiro de Viticultura e Enologia Anais, 45354. |
[36] |
Tisseyre B, Ojeda H, Taylor J (2007) New technologies and methodologies for site-specific viticulture. J Int Sci Vigne Vin 41: 63–76. https://doi.org/10.20870/oeno-one.2007.41.2.852 doi: 10.20870/oeno-one.2007.41.2.852
![]() |
[37] | Bramley RGV, Lanyon DM, Panten K (2005) Whole-of-vineyard experimentation—An improved basis for knowledge generation and decision making. Final Report on GWRDC—Project No. CSL04/01. CSIRO Land and Water/Grape and Wine Research and Development Corporation. |
[38] | Bramley RGV, Reid A, Taylor J, et al. (2005) A comparison of the spatial variability of vineyard yield in European and Australian production systems. Final Report on GWRDC—Project No. CSL04/01. CSIRO Land and Water/Grape and Wine Research and Development Corporation. |
[39] | Schueller JK (1997) Technology for precision agriculture. In: Stafford JV (Ed.), Precision Agriculture '97. Spatial Variability in Soil and Crop, Oxford, BIOS Scientific Publishers, 1: 33–34. |
[40] | Senturk S, Sertel E, Kaya S (2013) Vineyards mapping using object based analysis. In: 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 66–70, IEEE. |
[41] | Jurisic M, Stanisavljevic A, Plascak I (2010) Application of Geographic Information System (GIS) in the selection of vineyard sites in Croatia. Bulg J Agric Sci 16: 235–242. |
[42] |
Esteves C, Fangueiro D, Braga RP, et al. (2022) Assessing the contribution of ECa and NDVI in the delineation of management zones in a vineyard. Agronomy 12: 1331. https://doi.org/10.3390/agronomy12061331 doi: 10.3390/agronomy12061331
![]() |
[43] |
Serrano J, Mau V, Rodrigues R, et al. (2023) Definition and validation of vineyard management zones based on soil apparent electrical conductivity and altimetric survey. Environments 10: 117. https://doi.org/10.3390/environments10070117 doi: 10.3390/environments10070117
![]() |
[44] | Comparetti A (2011) Precision agriculture: Past, present and future. In: Agroinzinerija ir energetika, Aleksandras Stulginskis University, 216–230. |
[45] |
Kernecker M, Knierim A, Wurbs A, et al. (2020) Experience versus expectation: Farmers' perceptions of smart farming technologies for cropping systems across Europe. Precis Agric 21: 34–50. https://doi.org/10.1007/s11119-019-09651-zCorpus doi: 10.1007/s11119-019-09651-zCorpus
![]() |
[46] | Say SM, Keskin M, Sehri M, et al. (2017) Adoption of precision agriculture technologies in developed and developing countries. In: International Science and Technology Conference, Cambridge, USA, 41–49. |
[47] |
Sozzi M, Kayad A, Marinello F, et al. (2020) Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform. OENO One 54: 189–197. https://doi.org/10.20870/oeno-one.2020.54.1.2557 doi: 10.20870/oeno-one.2020.54.1.2557
![]() |
[48] | Bălăceanu C, Negoiţă A, Drăgulinescu AM, et al. (2021) The use of IoT technology in smart viticulture. In: 2021 23rd International Conference on Control Systems and Computer Science (CSCS), 362–369. https://doi.org/10.1109/CSCS52396.2021.00066 |
[49] |
Mizik T (2023) How can proximal sensors help decision-making in grape production?. Heliyon 9: e16322. https://doi.org/10.1016/j.heliyon.2023.e16322 doi: 10.1016/j.heliyon.2023.e16322
![]() |
[50] |
Singh AP, Yerudkar A, Mariani V, et al. (2022) A bibliometric review of the use of unmanned aerial vehicles in precision agriculture and precision viticulture for sensing applications. Remote Sens 14: 1604. https://doi.org/10.3390/rs14071604 doi: 10.3390/rs14071604
![]() |
[51] |
Fountas S, Mylonas N, Malounas I, et al. (2020) Agricultural robotics for field operations. Sensors 20: 2672. https://doi.org/10.3390/s20092672 doi: 10.3390/s20092672
![]() |
[52] |
Ammoniaci M, Kartsiotis SP, Perria R, et al. (2021) State of the art of monitoring technologies and data processing for precision viticulture. Agriculture 11: 201. https://doi.org/10.3390/agriculture11030201 doi: 10.3390/agriculture11030201
![]() |
[53] |
Somkuwar RG, Naik S (2024) Precision viticulture: A review. Curr Hortic 12: 23–34. https://doi.org/10.5958/2455-7560.2024.00003.8 doi: 10.5958/2455-7560.2024.00003.8
![]() |
[54] |
Vaudour E, Shaw AB (2005) A worldwide perspective on viticultural zoning. S Afr J Enol Vitic 26: 106–115. https://doi.org/10.21548/26-2-2125 doi: 10.21548/26-2-2125
![]() |
[55] |
Kontogiannis S, Kokkonis G, Ellinidou S, et al. (2017) Proposed fuzzy-NN algorithm with LoRa communication protocol for clustered irrigation systems. Future Internet 9: 78. https://doi.org/10.3390/fi9040078 doi: 10.3390/fi9040078
![]() |
[56] | Will B, Rolfes I (2014) A miniaturized soil moisture sensor based on time domain transmissometry. In: Proceedings of the IEEE Sensors Applications Symposium (SAS), Queenstown, New Zealand, 233–236. |
[57] |
Ferrarezi RS, Dove SK, van Iersel MW (2015) An automated system for monitoring soil moisture and controlling irrigation using low-cost open-source microcontrollers. Hort Technol 2: 110–118. https://doi.org/10.21273/HORTTECH.25.1.110 doi: 10.21273/HORTTECH.25.1.110
![]() |
[58] |
Carrara M, Castrignano A, Comparetti A, et al. (2007) Mapping of penetrometer resistance in relation to tractor traffic using multivariate geostatistics. Geoderma 142: 294–307. https://doi.org/10.1016/j.geoderma.2007.08.020 doi: 10.1016/j.geoderma.2007.08.020
![]() |
[59] |
Adylin IP, Comparetti A, Greco C, et al. (2024) Computing the pressure of agricultural tractors on soil and mapping its compaction. Dokuchaev Soil Bulletin V 120: 136–163. https://doi.org/10.19047/0136-1694-2024-120-136-163 doi: 10.19047/0136-1694-2024-120-136-163
![]() |
[60] |
Kameoka S, Isoda S, Hashimoto A, et al. (2017) A wireless sensor network for growth environment measurement and multi-band optical sensing to diagnose tree vigor. Sensors 17: 966. https://doi.org/10.3390/s17050966 doi: 10.3390/s17050966
![]() |
[61] | Gladstones J (2011) Wine, Terroir and Climate Change: Wakefield Press, Kent Town, CT, USA, ISBN 978-1-86254-924-1. |
[62] |
Jones VG, Duff A, Hall A, et al. (2010) Spatial Analysis of Climate in Wine Grape Growing Regions in the Western United States. Am J Enol Vitic 61: 323–326. https://doi.org/10.5344/ajev.2010.61.3.313 doi: 10.5344/ajev.2010.61.3.313
![]() |
[63] | Blanco-Ward D, Ribeiro A, Barreales D, et al. (2019) Climate change potential effects on grapevine bioclimatic indices: A case study for the Portuguese demarcated Douro Region (Portugal). In: BIO Web Conference 2019. |
[64] | Bulgari R, Cola G, Ferrante A, et al. (2015) Micrometeorological environment in traditional and photovoltaic greenhouses and effects on growth and quality of tomato (Solanum lycopersicum L.). Ital J Agrometeorol 2: 27–38. https://doi.org/10.19199/2015.2.2038-5625.027 |
[65] |
Vivar M, Fuentes M, Norton M, et al. (2014) Estimation of sunshine duration from the global irradiance measured by a photovoltaic silicon solar cell. Renew Sustain Energy Rev 36: 26–33. http://dx.doi.org/10.1016/j.rser.2014.04.045 doi: 10.1016/j.rser.2014.04.045
![]() |
[66] |
Yang H, Li J, Yang J, et al. (2014) Effects of nitrogen application rate and leaf age on the distribution pattern of leaf SPAD readings in the rice canopy. PLoS ONE 9: e88421. https://doi.org/10.1371/journal.pone.0088421 doi: 10.1371/journal.pone.0088421
![]() |
[67] | Osypka P, Rheinfelden H (2009) Apparatus for Examining or Monitoring Plants. U.S. Patent 2009/0278555 A1. |
[68] |
Morari F, Castrignanò A, Pagliarina C (2009) Application of multivariate geostatistics in delineating management zones within a gravelly vineyard using geo-electrical sensors. Comput Electron Agric 68: 97–107. https://dx.doi.org.10.1016/j.compag.2009.05.003 doi: 10.1016/j.compag.2009.05.003
![]() |
[69] |
Andrenelli MC, Magini S, Pellegrini S, et al. (2013) The use of the ARP© system to reduce the costs of soil survey for precision viticulture. J Appl Geophys, 99: 24–34. http://dx.doi.org/10.1016/j.jappgeo.2013.09.012 doi: 10.1016/j.jappgeo.2013.09.012
![]() |
[70] | Meygret A, Baillarin S, Gascon F, Hillairet E, Dechoz C et al. (2009) SENTINEL-2 image quality and level 1 processing. International Society of Optical Engineering. https://doi.org/10.1117/12.826184 |
[71] | Giannaros C, Kotroni V, Lagouvardos K, Giannaros MT, Pikridas C (2020) Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece. Remote Sens 12,383. https://doi.org/10.3390/rs12030383 |
[72] | Haralambous H, Oikonomou C, Pikridas C, et al. (2019) Project-Balkan-Mediterranean real time severe weather service. In: Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 9879–9882. https://doi.org/10.1109/IGARSS.2019.8898121 |
[73] |
Sepúlveda-Reyes D, Ingram B, Bardeen M, et al. (2015) Predicting grapevine water status based on hyperspectral reflectance vegetation indices. Remote Sens 7: 6460–16479. https://doi.org/10.3390/rs71215835 doi: 10.3390/rs71215835
![]() |
[74] |
Sepúlveda-Reyes D, Ingram B, Bardeen M, et al. (2016) Selecting canopy zones and thresholding approaches to assess grapevine water status by using aerial and ground-based thermal imaging. Remote Sens 8: 822. https://doi.org/10.3390/rs8100822 doi: 10.3390/rs8100822
![]() |
[75] |
Rey-Caramés C, Diago MP, Martín MP, et al. (2015) Using RPAS multi-spectral imagery to characterise vigour, leaf development, yield components and berry composition variability within a vineyard. Remote Sens 7: 14458–14481. https://doi.org/10.3390/rs71114458 doi: 10.3390/rs71114458
![]() |
[76] | Turner D, Lucieer A, Watson C (2011) Development of an unmanned aerial vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery. In: Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, Australia. |
[77] |
Karakizi C, Oikonomou M, Karantzalos K (2016) Vineyard detection and vine variety discrimination from very high resolution satellite data. Remote Sens 8: 235. https://doi.org/10.3390/rs8030235 doi: 10.3390/rs8030235
![]() |
[78] |
Giovos R, Tassopoulos D, Kalivas D, et al. (2021) Remote sensing vegetation indices in viticulture: A critical review. Agriculture 11: 457. https://doi.org/10.3390/agriculture11050457 doi: 10.3390/agriculture11050457
![]() |
[79] |
Beeri O, Netzer Y, Munitz S, et al. (2020) Kc and LAI estimations using optical and SAR remote sensing imagery for vineyards plots. Remote Sens 12: 3478. https://doi.org/10.3390/rs12213478 doi: 10.3390/rs12213478
![]() |
[80] | Gerhards M, Schlerf M., Mallick K, et al. (2019) Challenges and future perspectives of multi-/hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sens 11: 1240. https://doi.org/10.3390/rs11101240 |
[81] |
Al-Saddik H, Simon JC, Cointault F (2017) Development of spectral disease indices for 'Flavescence Dorée' grapevine disease identification. Sensors 17: 2772. https://doi.org/10.3390/s17122772 doi: 10.3390/s17122772
![]() |
[82] |
Stuart MB, McGonigle AJS, Willmott JR (2019) Hyperspectral imaging in environmental monitoring: A review of recent developments and technological advances in compact field deployable systems. Sensors 19: 3071. https://doi.org/10.3390/s19143071 doi: 10.3390/s19143071
![]() |
[83] |
Jiang Z, Huete A, Chen J, et al. (2006) Analysis of NVDI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens Environ 101: 366–378. https://doi.org/10.1016/j.rse.2006.01.003 doi: 10.1016/j.rse.2006.01.003
![]() |
[84] |
Matsushita B, Yang W, Chen J, et al. (2007) Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-Density cypress forest. Sensors 7: 2636–2651. https://doi.org/10.3390/s7112636 doi: 10.3390/s7112636
![]() |
[85] |
Xue J, Su B (2017) Significant remote sensing vegetation indices: A review of developments and applications. J Sensors (Hindawi) 2017: 1353691. https://doi.org/10.1155/2017/1353691 doi: 10.1155/2017/1353691
![]() |
[86] |
Gamon J, Serrano L, Surfus J (1997) The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112: 492–501. https://doi.org/10.1007/s004420050337 doi: 10.1007/s004420050337
![]() |
[87] |
Garbulsky MF, Peñuelas J, Gamon J, Inoue Y, Filella I (2011) The Photochemical Reflectance Index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis. Remote Sens Environ 115: 281–297. https://doi.org/10.1016/j.rse.2010.08.023 doi: 10.1016/j.rse.2010.08.023
![]() |
[88] |
Hu R, Yan G, Mu X, Luo J (2014) Indirect measurement of leaf area index on the basis of path length distribution. Remote Sens Environ 155: 239–247. http://dx.doi.org/10.1016/j.rse.2014.08.032 doi: 10.1016/j.rse.2014.08.032
![]() |
[89] |
Anastasiou E, Balafoutis A, Darra N, et al. (2018) Satellite and proximal sensing to estimate the yield and quality of table grapes. Agriculture 8: 94. https://doi.org/10.3390/agriculture8070094 doi: 10.3390/agriculture8070094
![]() |
[90] | Almutairi B, Battay A, Belaid M, et al. (2013) Comparative study of SAVI and NDVI vegetation indices in Sulaibiya Area (Kuwait) using worldview satellite imagery. Int J Geosci Geomatics 1: 50–53. |
[91] |
Jimenez A, Salamance JM, Medina MJQ, et al. (2015) Crops diagnosis using digital image processing and precision agriculture technologies. Inge Cuc 11: 63–71. https://doi.org/10.17981/ingecuc.11.1.2015.06 doi: 10.17981/ingecuc.11.1.2015.06
![]() |
[92] |
Comparetti A, Marques da Silva JR (2022) Use of Sentinel-2 satellite for spatially variable rate fertiliser management in a Sicilian vineyard. Sustainability 14: 1688. https://doi.org/10.3390/su14031688 doi: 10.3390/su14031688
![]() |
[93] |
Fernández-Novales J, Saiz-Rubio V, Barrio I, et al. (2021) Monitoring and mapping vineyard water status using non-invasive technologies by a ground robot. Remote Sens 13: 2830. https://doi.org/10.3390/rs13142830 doi: 10.3390/rs13142830
![]() |
[94] |
Vrochidou E, Tziridis K, Nikolaou A, et al. (2021) An autonomous grape-harvester robot: Integrated system architecture. Electronics 10: 1056. https://doi.org/10.3390/electronics10091056 doi: 10.3390/electronics10091056
![]() |
[95] |
Oberti R, Marchi M, Tirelli P, et al. (2016) Selective spraying of grapevines for disease control using a modular agricultural robot. Biosyst Eng 146: 203–215. https://doi.org/10.1016/j.biosystemseng.2015.12.004 doi: 10.1016/j.biosystemseng.2015.12.004
![]() |
[96] |
Kasimati A, Psiroukis V, Darra N, et al. (2023) Investigation of the similarities between NDVI maps from different proximal and remote sensing platforms in explaining vineyard variability. Precis Agric 24: 1220–1240. https://doi.org/10.1007/s11119-022-09984-2 doi: 10.1007/s11119-022-09984-2
![]() |
[97] |
Paoli JN, Strauss O, Tisseyre B, et al. (2007) Spatial data fusion for qualitative estimation of fuzzy request zones: Application on precision viticulture. Fuzzy Sets Syst 158: 535–554. https://doi.org/10.1016/j.fss.2006.10.019 doi: 10.1016/j.fss.2006.10.019
![]() |
[98] |
Kasimati A, Espejo-Garcia B, Vali E, et al. (2021) Investigating a selection of methods for the prediction of total soluble solids among wine grape quality characteristics using normalized difference vegetation index data from proximal and remote sensing. Front Plant Sci 12: 683078. https://doi.org/10.3389/fpls.2021.683078 doi: 10.3389/fpls.2021.683078
![]() |
[99] |
Kasimati A, Espejo-García B, Darra N, et al. (2022) Predicting grape sugar content under quality attributes using normalized difference vegetation index data and automated machine learning. Sensors 22: 3249. https://doi.org/10.3390/s22093249 doi: 10.3390/s22093249
![]() |
[100] |
Grelier M, Guillaume S, Tisseyre B, et al. (2007) Precision viticulture data analysis using fuzzy inference systems. OENO ONE 41: 19–31. https://doi.org/10.20870/oeno-one.2007.41.1.857 doi: 10.20870/oeno-one.2007.41.1.857
![]() |
[101] | Gutiérrez S, Tardaguila J, Fernández-Novales J, et al. (2015) Data mining and non-invasive proximal sensing for precision viticulture. In: Proceedings 2nd International Electronic Conference Sensors Application. |
[102] | Whalley JL, Shanmuganathan S (2013) Applications of image processing in viticulture: A review. In: Conference MSSANZ-International Congress on Modelling and Simulation, Adelaide, Australia. |
[103] | Liu S, Marden S, Whitty M (2013) Towards automated yield estimation in viticulture. In: Proceedings of the Australasian Conference on Robotics and Automation, Sydney, Australia, 24: 2–6. |
[104] | Liu S, Whitty M, Cossell S (2015) Automatic grape bunch detection in vineyards for precise yield estimation. In: 2015 14th IAPR International Conference on Machine Vision Applications (MVA), IEEE, 238–241. https://doi.org/10.1109/MVA.2015.7153175 |
[105] | De Filippis T, Rocchi L, Fiorillo E, et al. (2012) Smartvineyard: An open source web-GIS application for precision viticulture. In: I International Workshop on Vineyard Mechanization and Grape and Wine Quality, 978: 107–116. https://doi.org/10.17660/ActaHortic.2013.978.11 |
[106] | Shanmuganathan S, Sallis P, Pavesi L, Muñoz, MCJ (2008) Computational intelligence and geo-informatics in viticulture. In: 2008 Second Asia International Conference on Modelling & Simulation (AMS), IEEE, 480–485. https://doi.org/10.1109/AMS.2008.101 |
[107] | Rossi V, Salinari F, Poni S, et al. (2014) Addressing the implementation problem in agricultural decision support systems: The example of vite.net®. Comput Electron Agric 100: 88–99. http://dx.doi.org/10.1016/j.compag.2013.10.011 |
[108] | Feruzzi FM, Gavazzeni L (2024) VINETO: A Decision Support System for Sustainable Wine Production using high-resolution Earth Observation imagery and Machine Learning. Proceedings of 2024 IAF Symposium on Integrated Applications at the 75th International Astronautical Congress, IAC 2024,315–322. https://doi.org/10.52202/078366-0037 |
[109] | Visconti F, de la Fuente M, Buesa I, et al. (2023) Decision support system for selecting the rootstock, irrigation regime and nitrogen fertilization in winemaking vineyards: WANUGRAPE4.0. In: EDP Sciences, BIO Web of Conferences, 68: 01032. https://doi.org/10.1051/bioconf/20236801032 |
[110] |
Pilafidis S, Kosmas E, Livieratos I, et al. (2024). Assessing energy use and greenhouse gas emissions in Cretan vineyards for the development of a crop-specific decision support tool. Environ, Dev Sustainability 26: 24415–24452. https://doi.org/10.1007/s10668-023-03649-4 doi: 10.1007/s10668-023-03649-4
![]() |
[111] |
Dias LC, Marques P, Garcia R, et al. (2025). A multicriteria sustainability assessment for improving integrated pest management for vineyards. J Cleaner Prod 2025: 145489. https://doi.org/10.1016/j.jclepro.2025.145489 doi: 10.1016/j.jclepro.2025.145489
![]() |
[112] |
Tsirogiannis IL, Malamos N, Baltzoi P. (2023) Application of a generic participatory decision support system for irrigation management for the case of a wine grapevine at Epirus, Northwest Greece. Horticulturae 9: 267. https://doi.org/10.3390/horticulturae9020267 doi: 10.3390/horticulturae9020267
![]() |
[113] |
Terribile F, Bonfante A, D'Antonio A, et al. (2017) A geospatial decision support system for supporting quality viticulture at the landscape scale. Compu Electron Agric 140: 88–102. https://doi.org/10.1016/j.compag.2017.05.028 doi: 10.1016/j.compag.2017.05.028
![]() |
[114] |
Bregaglio S, Savian F, Raparelli E, et al. (2022) A public decision support system for the assessment of plant disease infection risk shared by Italian regions. J Environ Manag 317: 115365. https://doi.org/10.1016/j.jenvman.2022.115365 doi: 10.1016/j.jenvman.2022.115365
![]() |
[115] |
Balafoutis A, Koundouras S, Anastasiou E, et al. (2017) Life cycle assessment of two vineyards after the application of precision viticulture techniques: A case study. Sustainability 9: 1997. https://doi.org/10.3390/su9111997 doi: 10.3390/su9111997
![]() |
[116] |
Zanchin A, Lovat L, Marcuzzo P, et al. (2023) Improving the oenological potential of grapes for Prosecco PDO sparkling wine thanks to nitrogen fertigation. Agronomy 13: 1369. https://doi.org/10.3390/agronomy13051369 doi: 10.3390/agronomy13051369
![]() |
[117] |
Casson A, Ortuani B, Giovenzana V, et al. (2022) A multidisciplinary approach to assess environmental and economic impact of conventional and innovative vineyards management systems in Northern Italy. Sci Total Environ 838: 156–181. http://dx.doi.org/10.1016/j.scitotenv.2022.156181 doi: 10.1016/j.scitotenv.2022.156181
![]() |
[118] |
González M-R, Hailemichael G, Catalina A, et al. (2019) Combined effects of water status and iron deficiency chlorosis on grape composition in non-irrigated vineyards. Sci Agric 76: 473–480. http://dx.doi.org/10.1590/1678-992X-2018-0084 doi: 10.1590/1678-992X-2018-0084
![]() |
[119] |
Sánchez R, González García MR, Vilanova M, et al. (2019) Aroma composition of Tempranillo grapes as affected by iron deficiency chlorosis and vine water status. Sci Agric 78: e20190112. http://dx.doi.org/10.1590/1678-992X-2019-0112 doi: 10.1590/1678-992X-2019-0112
![]() |
[120] |
Garcia-Tejero IF, Costa JM, Egipto R, et al. (2016) Thermal data to monitor crop-water status in irrigated Mediterranean viticulture. Agric Water Manag 176: 80–90. https://doi.org/10.1016/j.agwat.2016.05.008 doi: 10.1016/j.agwat.2016.05.008
![]() |
[121] |
Kontogiannis S, Asiminidis C (2020) A proposed low-cost viticulture stress framework for table grape varieties. IoT 1: 337–359. https://doi.org/10.3390/iot102002 doi: 10.3390/iot102002
![]() |
[122] |
Ortuani B, Facchi A, Mayer A, et al. (2019). Assessing the effectiveness of variable-rate drip irrigation on water use efficiency in a vineyard in Northern Italy. Water 11: 1964. https://doi.org/10.3390/w11101964 doi: 10.3390/w11101964
![]() |
[123] | Cameron W, Petrie PR, Bonada M (2024) Effects of vineyard management practices on winegrape composition. A review using meta-analysis. Am J Enol Vitic 75: 0750022. https://doi.org/10.5344/ajev.2024.24018 |
[124] |
Tagarakis AC, Koundouras S, Fountas S, et al. (2018) Evaluation of the use of LIDAR laser scanner to map pruning wood in vineyards and its potential for management zones delineation. Precis Agric 19: 334–347. https://doi.org/10.1007/s11119-017-9519-4 doi: 10.1007/s11119-017-9519-4
![]() |
[125] |
Vallejo A, Millán L, Abrego Z, et al. (2019) Fungicide distribution in vitiviniculture ecosystems according to different application strategies to reduce environmental impact. Sci Total Environ 687: 319–329. https://doi.org/10.1016/j.scitotenv.2019.06.112 doi: 10.1016/j.scitotenv.2019.06.112
![]() |
[126] |
Tsalidis GA (2022) Human health and ecosystem quality benefits with life cycle assessment due to fungicides elimination in agriculture. Sustainability 14: 846. https://doi.org/10.3390/su14020846 doi: 10.3390/su14020846
![]() |
[127] |
Chen M, Brun F, Raynal M, et al. (2020) Delaying the first grapevine fungicide application reduces exposure on operators by half. Sci Rep 10: 6404. https://doi.org/10.1038/s41598-020-62954-4 doi: 10.1038/s41598-020-62954-4
![]() |
[128] |
Anastasiou E, Fountas S, Voulgaraki M, et al. (2023) Precision farming technologies for crop protection: A meta-analysis. Smart Agr Technol 2023: 100323. https://doi.org/10.1016/j.atech.2023.100323 doi: 10.1016/j.atech.2023.100323
![]() |
[129] |
Pérez-Expósito JP, Fernández-Caramés TM, Fraga-Lamas P, et al. (2017) VineSens: An eco-smart decision-support viticulture system. Sensors 17: 465. https://doi.org/10.3390/s17030465 doi: 10.3390/s17030465
![]() |
[130] |
Gil E, Arnó J, Llorens J, et al. (2014) Advanced technologies for the improvement of spray application techniques in Spanish viticulture: An overview. Sensors 14: 691–708. https://doi.org/10.3390/s140100691 doi: 10.3390/s140100691
![]() |
[131] |
Gil E, Llorens J, Llop J, et al. (2013) Use of a terrestrial LIDAR sensor for drift detection in vineyard spraying. Sensors 13: 516–534. https://doi.org/10.3390/s130100516 doi: 10.3390/s130100516
![]() |
[132] |
Garcia-Ruiz F, Campos J, Llop-Casamada J, Gil E (2023) Assessment of map based variable rate strategies for copper reduction in hedge vineyards. Comput Electron Agric 207: 107753. https://doi.org/10.1016/j.compag.2023.107753 doi: 10.1016/j.compag.2023.107753
![]() |
[133] | Marques da Silva JR, Correia M, Dias A, et al. (2017) CARTS—Canopy Adjusted Real Time Spraying, 11th International AⅡA Conference, Bari, Italy, 484–487. |
[134] | Gil E, Llorens J, Llop J, et al. (2013) Variable rate sprayer. Part 2—Vineyard prototype: Design, implementation, and validation. Comput Electron Agric 95: 136–150. https://doi.org/10.1016/j.compag.2013.02.01 |
[135] |
Román C, Llorens J, Uribeetxebarria A, et al. (2020) Spatially variable pesticide application in vineyards: Part Ⅱ, field comparison of uniform and map-based variable dose treatments. Biosyst Eng 195: 42–53. https://doi.org/10.1016/j.biosystemseng.2020.04.013 doi: 10.1016/j.biosystemseng.2020.04.013
![]() |
[136] |
Martinez-Casasnovas JA, Agelet-Fernandez J, Arno J, et al. (2012) Analysis of vineyard differential management zones and relation to vine development, grape maturity and quality. Span J Agric Res 10: 326-337. Available from: www.inia.es/sjar. http://dx.doi.org/10.5424/sjar/2012102-370-11. doi: 10.5424/sjar/2012102-370-11
![]() |
[137] |
Serrano L, González-Flor C, Gorchs G (2012) Assessment of grape yield and composition using the reflectance based Water Index in Mediterranean rainfed vineyards. Remote Sens Environ 118: 249–258. https://doi.org/10.1016/j.rse.2011.11.021 doi: 10.1016/j.rse.2011.11.021
![]() |
[138] | Bramley RGV (2022) 12—Precision viticulture: Managing vineyard variability for improved quality outcomes. In: Reynolds AG (Ed.), Woodhead Publishing Series in Food Science, Technology and Nutrition, Managing Wine Quality, 2nd ed.: Woodhead Publishing Limited, Cambridge, UK, 541–586. |
[139] | Bonilla I, de Toda FM, Martínez-Casasnovas JA (2015) Vine vigor, yield and grape quality assessment by airborne remote sensing over three years: Analysis of unexpected relationships in cv. Tempranillo. Span J Agric Res 13: e0903. https://doi.org/10.5424/sjar/2015132-7809 |
[140] |
Priori S, Martini E, Andrenelli MC, et al. (2013) Improving wine quality through harvest zoning and combined use of remote and soil proximal sensing. Soil Sci Soc Am J 77: 1338–1348. https://doi.org/10.2136/sssaj2012.0376 doi: 10.2136/sssaj2012.0376
![]() |
[141] |
Booltink HWG, van Alphen BJ, Batchelor WD, et al. (2001) Tools for optimizing management of spatially variable fields. Agric Syst 70: 445–476. https://doi.org/10.1016/S0308-521X(01)00055-5 doi: 10.1016/S0308-521X(01)00055-5
![]() |
[142] | Dampney PMR, Moore M (1999) Precision agriculture in England - current practice and research-based advice to farmers. In: Robert PC, Rust RH, Larson WE (Eds.), Proceedings of the 4th International Conference on Precision Agriculture, St. Paul, MN, US. Madison, WI, American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 661–674. |
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