Terroir 2020 banner
IVES 9 IVES Conference Series 9 International Terroir Conferences 9 Terroir 2020 9 History and innovation of terroir 9 Can the satellite image resolution be improved to support precision agriculture in the vineyard through vegetation indices?

Can the satellite image resolution be improved to support precision agriculture in the vineyard through vegetation indices?

Abstract

Aim: This study aims to show the application of a new methodological approach to improve the resolution of Sentinel-2A images and derived vegetation indices through the results from different vineyards. 

Methods and Results: A multiscale fully-connected Convolutional Neural Network (CNN) was constructed and applied for the pan-sharpening of Sentinel-2A images by high resolution UAS-based orthophoto. The reconstructed data was validated by independent high resolution multispectral UAS-based imagery and in-situ spectral measurements. The reconstructed Sentinel-2A images provided a temporal evaluation of plant responses to environmental factors using selected vegetation indices. The proposed methodology has been applied on different vineyards in southern Italy. Here, the outputs of CNN were compared with morpho-physiological data, both collected in-vivo and reconstructed through the retrospective analysis of vine trunk wood (tree-rings). The functional anatomical traits and isotopic signals were measured and used to derive indices such as water use efficiency. The obtained results showed a valuable agreement between the vegetation indices derived from reconstructed Sentinel-2A data and plant hydraulic traits obtained from tree-ring based reconstruction of vine eco-physiological behavior.

Conclusions: 

The multiscale CNN architecture for remote sensing imagery pan-sharpening and reconstruction can overcome the constraints in use of satellite images in precision agriculture, by creating new fused data valid for applications that could not be supported by the original Sentinel multispectral or UVS data. The validation of such an approach on different and real vineyard systems, with data collected in-vivo and through retrospective analyses on tree-ring chronologies has shown great potential to extend the approach to other woody crop systems. 

Significance and Impact of the Study: The integration between knowledge from different scientific domains represents a powerful approach to support the farmer in the field management and, at the same time, a valuable opportunity to study the plant adaptation to variable pedo-climatic conditions. This represents the base for understanding the vine adaptive capability and planning the actions for vineyard management under different climatic scenarios. Finally, emerging CNN methodologies can be implemented in DSS to support real-time monitoring of several parameters related to plant health status, to better follow plant growth in the field and evaluate its performance under changing environmental conditions.

DOI:

Publication date: March 23, 2021

Issue: Terroir 2020

Type: Video

Authors

A. Bonfante1*, A. Brook2, G. Battipaglia3, A. Erbaggio4, M. Buonanno1, E. Monaco1, C. Cirillo5, V. De Micco5

1Institute for Mediterranean Agricultural and Forest Systems -CNR-ISAFOM, National Research Council, Ercolano-NA, Italy
2Spectroscopy & Remote Sensing Laboratory, Department of Geography and Environmental Studies, University of Haifa, Mount Carmel, Israel
3Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “L. Vanvitelli”, Caserta, Italy
4Freelance
5Department of Agricultural Sciences, University of Naples Federico II, Portici – NA, Italy

Contact the author

Keywords

Precision agriculture, satellite image resolution, CNN, grapevine hydraulics, KTB group approach

Tags

IVES Conference Series | Terroir 2020

Citation

Related articles…

What are the optimal ranges and thresholds for berry solar radiation for flavonoid biosynthesis?

In wine grape production, canopy management practices are applied to control the source-sink balance and improve the cluster microclimate to enhance berry composition. The aim of this study was to identify the optimal ranges of berry solar radiation exposure (exposure) for upregulation of flavonoid biosynthesis and thresholds for their degradation, to evaluate how canopy management practices such as leaf removal, shoot thinning, and a combination of both affect the grapevine (Vitis vinifera L. cv. Cabernet Sauvignon) yield components, berry composition, and flavonoid profile under context of climate change. First experiment assessed changes in the grape flavonoid content driven by four degrees of exposure. In the second experiment, individual grape berries subjected to different exposures were collected from two cultivars (Cabernet Sauvignon and Petit Verdot). The third experiment consisted of an experiment with three canopy management treatments (i) LR (removal of 5 to 6 basal leaves), (ii) ST (thinned to 24 shoots per vine), and (iii) LRST (a combination of LR and ST) and an untreated control (UNT). Berry composition, flavonoid content and profiles, and 3-isobutyl 2-methoxypyrazine were monitored during berry ripening. Although increasing canopy porosity through canopy management practices can be helpful for other purposes, this may not be the case of flavonoid compounds when a certain proportion of kaempferol was achieved. Our results revealed different sensitivities to degradation within the flavonoid groups, flavonols being the only monitored group that was upregulated by solar radiation. Within different canopy management practices, the main effects were due to the ST. Under environmental conditions given in this trial, ST and LRST hastened fruit maturity; however, a clear improvement of the flavonoid compounds (i.e., greater anthocyanin) was not observed at harvest. Methoxypyrazine berry content decreased with canopy management practices studied. Although some berry traits were improved (i.e. 2.5° Brix increase in berry total soluble solids) due to canopy management practices (ST), this resulted in a four-fold increase in labor operations cost, two-fold decrease in yield with a 10-fold increase in anthocyanin production cost per hectare that should be assessed together as the climate continues to get hot.

Optimizing stomatal traits for future climates

Stomatal traits determine grapevine water use, carbon supply, and water stress, which directly impact yield and berry chemistry. Breeding for stomatal traits has the strong potential to improve grapevine performance under future, drier conditions, but the trait values that breeders should target are unknown. We used a functional-structural plant model developed for grapevine (HydroShoot) to determine how stomatal traits impact canopy gas exchange, water potential, and temperature under historical and future conditions in high-quality and hot-climate California wine regions (Napa and the Central Valley). Historical climate (1990-2010) was collected from weather stations and future climate (2079-99) was projected from 4 representative climate models for California, assuming medium- and high-emissions (RCP 4.5 and 8.5). Five trait parameterizations, representing mean and extreme values for the maximum stomatal conductance (gmax) and leaf water potential threshold for stomatal closure (Ψsc), were defined from meta-analyses. Compared to mean trait values, the water-spending extremes (highest gmax or most negative Ysc) had negligible benefits for carbon gain and canopy cooling, but exacerbated vine water use and stress, for both sites and climate scenarios. These traits increased cumulative transpiration by 8 – 17%, changed cumulative carbon gain by -4 – 3%, and reduced minimum water potentials by 10 – 18%. Conversely, the water-saving extremes (lowest gmax or least negative Ψsc) strongly reduced water use and stress, but potentially compromised the carbon supply for ripening. Under RCP 8.5 conditions, these traits reduced transpiration by 22 – 35% and carbon gain by 9 – 16% and increased minimum water potentials by 20 – 28%, compared to mean values. Overall, selecting for more water-saving stomatal traits could improve water-use efficiency and avoid the detrimental effects of highly negative canopy water potentials on yield and quality, but more work is needed to evaluate whether these benefits outweigh the consequences of minor declines in carbon gain for fruit production.

VINIoT: Precision viticulture service for SMEs based on IoT sensors network

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.

Soil quality in Beaujolais vineyard. Importance of pedology and cultural practices

A pedological study was carried out from 2009 to 2017 in Beaujolais vineyard, to improve physical and chemical knowledge of soils. It was completed in 2016 and 2017 by the current study, dealing with microbial aspects, in order to build a reference frame for improved advice in soil management. Microbial biomass was measured on representative plots of the six most common soil types identified in Beaujolais and, for each soil type, on plots with different levels of the main impacting parameters: total organic carbon, pH, cation exchange capacity, extractable copper. A total of 59 soil samples were collected. Confirming the results of various trials carried out in Beaujolais over the past 20 years, the results of the present study showed that the soils were still alive, but exhibited a large variability of biological parameters, which appeared dependant on both pedological and anthropic factors. Therefore, a good interpretation of biological parameters and advice for vine growers must rely on a pedologically-based referential with differentiated main driving factors. For example, the control of pH is of primary importance in granitic soils and in no way organic matter addition can improve soil quality if pH is too low. Conversely, in calcareous soils, biological parameters are more directly affected by direct or indirect (cover crops for example) inputs of organic matter. The use of biological parameters, such as microbial biomass, is of great potential value to improve advice on agro-viticultural practices (soil management, fertilization, liming, etc.), basis of a sustainable wine production on fragile soils.

Modeling the suitability of Pinot Noir in Oregon’s Willamette Valley in a changing climate

Air temperature is the key driver of grapevine phenology and a significant environmental factor impacting yield and quality for a winegrape growing region. In this study the optimal downscaled CMIP5 ensemble for computing thegrowing season average temperature (GST) viticulture climate classification index was determined to spatially compute on a decadal basis predictions of the GST climate index and the grapevine sugar ripeness (GSR) model for Pinot Noir throughout the Willamette Valley (WV) American Viticultural Area (AVA). Forecasts for average temperature and a 220 g/L target sugar concentration level were computed using daily Localized Constructed Analogs (LOCA) downscaled CMIP5 historic and Representative Concentration Pathways (RCP) future climate projections of minimum and maximum daily temperature. We explore spatiotemporal trends of the GST climate classification index and Pinot Noir specific applications of the GSR phenology model for the WV AVA. Spatiotemporal computations of the GST climate index and Pinot Noir specific applications of the GSR model enable the opportunity to explore relationships between their computed values with one intent being to provide updated GST ranges that better align with current temperature-based modeling understanding of Pinot Noir grapevine phenology and the viticultural application of LOCA CMIP5 climate projections for the WV AVA. The Pinot Noir specific applications of the GSR model or the GST index with updated bounds indicate that the percent of the WV AVA area suitable for Pinot Noir production is currently at or near its peak value in the upper 80s to lower 90s of this century.