Browsing by Author "Barnard, Yolandi"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    The use of technology to improve current precision viticulture practices: predicting vineyard performance
    (Stellenbosch : Stellenbosch University, 2018-12) Barnard, Yolandi; Poblete-Echeverría, Carlos A; Strever, A. E.; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Viticulture & Oenology & Institute for Wine Biotechnology.
    ENGLISH ABSTRACT: Producing high quality grapes is difficult due to intra-vineyard spatial variability in vineyards. Variability leads to differences in grape quality and quantity. This poses a problem for producers, as homogeneous growth is nearly non-existent in vineyards. Remote sensing provides information of vineyard variability resulting in better knowledge of the distribution and occurrence thereof, leading to improved management practices. Remote sensing has been studied and implemented in several fields of research and industry, such as monitoring forest growth, pollution, population growth, etc. The potential to implement remote sensing technology is endless. Generating variability maps introduces the possibility of plant specific management practices, to alleviate problems occurring from variability. Aerial and satellite remote sensing provide new methods of variability monitoring, through spatial variability mapping of soil and plant biomass. Advances in geo-referencing and geolocations provide high accuracy precision tools for producers and researchers. New technology introduces possible means of vigour classification and stress monitoring on a plant scale, relieving the uncertainty caused by the distribution and extent of variability in vineyards. Vineyards are more difficult to analyse with remote sensing technology, due to the discontinuous canopies resulting in objects, other than plant biomass, to be monitored with the plant biomass. These objects can be soil and inter-row plant growth, along with trees close to or adjacent to the vineyards. This provides a dilemma through diluting biomass estimations and resulting in misinterpretation of the vineyard variability. These problems could be solved with the use of high-resolution multispectral imaging, providing clear classification and information of plant growth and health status. These sensing technologies have only been studied in some industries and have yet to be implemented to provide plant specific information. Introducing high accuracy plant specific information along with geolocation information will provide the producer with enough information to implement specific management practices alleviating heterogeneous plant growth and promoting homogeneous growth and yield, resulting in improved economic status through limiting input costs and environmental impact, providing better living conditions for plants along with increased plant longevity. The aim of the study was to evaluate the accuracy of leaf area index (LAI) estimations from selected remote sensing technologies with three different sensor resolutions. Imaging of the experimental site with natural variability were taken with the remote sensors. Targeted vines in the vineyard were selected as ground control points for ground truth measurements. The data acquired from the ground truth measurements were compared to the normalised difference vegetation index (NDVI) values generated from the remote sensing technologies. Grid analysis was performed on the unmanned aerial vehicle (UAV) multispectral images, mapping the LAI of individual plants. Significant differences in LAI predictions were obtained with good correlations between the ground truth data and the UAV multispectral image NDVI, r2 = 0.69. Climatic conditions proved problematic for the satellite images, where resolution also posed a problem. Variability is often caused by environmental factors, although management practices influence variability of vineyards. Management practices can be beneficial to plant growth, such as tillage promotes soil aeration and biodiversity through mixing the soil layers and providing more homogeneous soil conditions in the vineyard, or detrimental, for example saline irrigation water can lead to toxic saline concentrations in the soil and result in plant degradation over time as the symptoms are only visual when toxicity has occurred. Salinity also provides improved soil moisture conditions through reducing the rate of soil drying. Other factors result in zonal variability, such as patchy growth from nutrient deficiencies or irregular growth patterns from pests or diseases. Remote sensing technology provides several sensing methods to determine the extent and distribution of variability. These methods involve various sensors, such as multispectral, light distance and ranging (LiDAR), etc., providing enough information to make informed decisions on management practices to limit variability or improve the extent thereof. These sensors are attached to aerial, satellite or ground platforms depending on the resolution needed and the extent of the study site. Field measurements of the selected ground truth sampling points showed the presence of natural variability and the distribution thereof in the vineyard. Analysis of the UAV multispectral images revealed a good correlation between the ground truth data and the NDVI values. Soil and other objects were removed from the multispectral images, resulting in increased accuracy of biomass estimations and limited the NDVI blending effect observed in low-resolution images. Pixel based NDVI values of each plant, generated from the UAV multispectral device, were averaged to provide the NDVI per plant. Satellite images generated resolution-based area averages and blended pixel values of the soil and other objects adjacent to the vines limiting the plant-based information. Satellite images were affected by climatic conditions, especially cloud cover, along with limited image acquisitions revealed restricted image usability. UAV multispectral images provided plant-based LAI maps based on information generated through grid analysis, revealing the distribution of variability with accurate vine locations. This study provided methods of autonomous image analysis for high- and low-resolution remote sensing technology. Models with accurate plant-based estimations to monitor and evaluate management practices will improve grape production and optimise quality resulting in improved wine quality. Selective harvesting and management practices will lead to optimised yield quality for targeted wine production, feeding the consumer driven industry. This study paved the way for future research in variability estimations from remote sensing technology with emphasis on the causes of within-vineyard variability.