Newly Bred Varieties of Phylloxera Tolerant Rootstocks

Becker, Andreas; Schartl, Angelika; Herrmann, J.

doi:10.17660/actahortic.2003.603.105

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It was found that in the graft and rootstock vines, on average, for varieties and years, the lengths of internodes differed. This corresponds to the fact that in the methods for determining grape varieties for varietal compliance, the biometric indicators of the vine can change depending on the conditions of the place and year of cultivation [2,[4][5][6][7][8][9][10][11].…”

Section: Plant Materialsmentioning

confidence: 99%

“…A grafted plant consists of two parts that are different in their biological properties, each of which reacts in its own way to certain environmental conditions by changing the metabolic rate. With the same type of reaction of both parts, i.e., while maintaining a similar nature of their metabolism, the grafted plant retains high viability and productivity [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Use of Computer Vision to Improve the Affinity of Rootstock-Graft Combinations and Identify Diseases of Grape Seedlings

et al. 2023

View full text Add to dashboard Cite

This study explores the application of computer vision for enhancing the selection of rootstock-graft combinations and detecting diseases in grape seedlings. Computer vision has various applications in viticulture, but publications and research have not reported the use of computer vision in rootstock-graft selection, which defines the novelty of this research. This paper presents elements of the technology for applying computer vision to rootstock-graft combinations and includes an analysis of grape seedling cuttings. This analysis allows for a more accurate determination of the compatibility between rootstock and graft, as well as the detection of potential seedling diseases. The utilization of computer vision to automate the grafting process of grape cuttings offers significant benefits in terms of increased efficiency, improved quality, and reduced costs. This technology can replace manual labor and ensure economic efficiency and reliability, among other advantages. It also facilitates monitoring the development of seedlings to determine the appropriate planting time. Image processing algorithms play a vital role in automatically determining seedling characteristics such as trunk diameter and the presence of any damage. Furthermore, computer vision can aid in the identification of diseases and defects in seedlings, which is crucial for assessing their overall quality. The automation of these processes offers several advantages, including increased efficiency, improved quality, and reduced costs through the reduction of manual labor and waste. To fulfill these objectives, a unique robotic assembly line is planned for the grafting of grape cuttings. This line will be equipped with two conveyor belts, a delta robot, and a computer vision system. The use of computer vision in automating the grafting process for grape cuttings offers significant benefits in terms of efficiency, quality improvement, and cost reduction. By incorporating image processing algorithms and advanced robotics, this technology has the potential to revolutionize the viticulture industry. Thanks to training a computer vision system to analyze data on rootstock and graft grape varieties, it is possible to reduce the number of defects by half. The implementation of a semi-automated computer vision system can improve crossbreeding efficiency by 90%. Reducing the time spent on pairing selection is also a significant advantage. While manual selection takes between 1 and 2 min, reducing the time to 30 s using the semi-automated system, and the prospect of further automation reducing the time to 10–15 s, will significantly increase the productivity and efficiency of the process. In addition to the aforementioned benefits, the integration of computer vision technology in grape grafting processes brings several other advantages. One notable advantage is the increased accuracy and precision in pairing selection. Computer vision algorithms can analyze a wide range of factors, including size, shape, color, and structural characteristics, to make more informed decisions when matching rootstock and graft varieties. This can lead to better compatibility and improved overall grafting success rates.

show abstract

Section: Plant Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Use of Computer Vision to Improve the Affinity of Rootstock-Graft Combinations and Identify Diseases of Grape Seedlings

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Проявление вегетативной изменчивости растений, ведущей к их ухудшению, возможно убрать только целенаправленным отбором. Кроме того, в обеспечении высокой и стабильной урожайности сорта важная роль принадлежит подвою, который, снижая или повышая адаптационные возможности сортоподвойных комбинаций к условиям среды, определяет урожайность и качество урожая [5][6][7][8][9][10][11]. Поэтому подбор плодоносящих насаждений (участков первичного отбора) и оценка имеющихся сортопод-'Kokur White' grapevine cultivar on various rootstocks ... ... достаточно много исследований посвящено вопросу изучения сорта Кокур белый [12,13].…”

unclassified

Изучение Увологических И Агробиологических Показателей Сорта Винограда Кокур Белый На Различных Подвоях Для Проведения Клоновой Селекции

Studennikova¹,

Котоловець²,

Tikhomirova³

2019

Magarach. Vinogradstvo I Vinodelie

View full text Add to dashboard Cite

Представлены результаты работы по изучению влияния филлоксероустойчивых подвоев Берландиери × Рипариа Кобер 5ББ и Берландиери × Рипариа Телеки 4Б (СО4) на агробиологические и увологические показатели крымского технического белоягодного аборигенного сорта винограда Кокур белый в условиях Алуштинской долины (филиал «Алушта» ФГУП «ПАО «Массандра»). Для проведения исследований были выбраны: участок № 361, площадью 4,49 га, на котором произрастают кусты винограда Кокур белый, привитые на подвое Берландиери × Рипариа Кобер 5 ББ (2010 год посадки, схема 3 м ×1,25 м) и участок № 391, площадью 3,06 га, на котором возделываются растения сорта Кокур белый, привитые на подвое Берландиери × Рипариа Телеки 4Б (СО4) - 2011 год посадки, схема 3 м × 1 м). На обоих участках система ведения кустов - вертикальная трехпроволочная шпалера, формировка АЗОС-1, высота штамба - 1,3 м. Показана целесообразность проведения работ по улучшению сорта Кокур белый методом клоновой селекции на сортоподвойной комбинации Кокур белый подвой Кобер 5 ББ: у растений отмечен высокий уровень коэффициента плодоношения (0,92), довольно крупные грозди (365 г), урожай с куста в среднем составил 4,74 кг/куст, очень высокая продуктивность побега по сырой массе грозди - 336,6 г/побег, большой процент выхода мякоти и сока - 91,3%. Оценка имеющихся сортоподвойных комбинаций в производственных условиях позволила провести индивидуальный отбор генотипически ценных в биолого-хозяйственном отношении форм растений.The paper reports on the findings of a study on the influence of phylloxera-resistant rootstocks ‘Berlandieri’×‘Riparia Kober 5BB’ and ‘Berlandieri’×‘Riparia Teleki 4B (СО4)’ on the agro-biological and uvological indicators of Crimean aboriginal grapevine variety used in winemaking ‘Kokur Beliy in the conditions of Alushta valley (branch of Alushta FGUP PAO Massandra). The following were selected for research: plot № 361 with an area of 4.49 hectares with bushes of ‘Kokur Beliy’ grafted on rootstock of ‘Berlandieri’ × Riparia Kobera 5 BB’ (planting year - 2010, planting scheme 3 m ×1.25 m) and plot № 391 covering 3.06 hectares with ‘Kokur Beliy’ vines grafted on ‘Berlandieri’ × ‘Riparia Teleki 4B (СО4)’ rootstock - planting year - 2011, planting scheme 3 m×1 m). On both plots, the training system used was vertical three-wire trellis, vines shaped as AZOS-1, the trunk height 1.3 m. The paper demonstrates the relevance of the work on improvement of ‘Kokur Beliy’ cultivar with the help of clonal breeding on rootstock cultivar combination ‘Kokur Beliy’ rootstock ‘Kober 5 BB’: the plants demonstrated high relative productivity (0.92), quite large bunches (365 g), yield per bush made on average 4.74 kg/bush, very high shoot productivity as per raw bunch weight 336.6 g/shoot, a large percentage of pulp and juice output - 91.3 %. Assessment of the cultivar-rootstock combinations under production conditions allowed making individual selection of genotypically valuable, from the biological and economic points of view, plants.

show abstract