Computer Vision and Deep Learning for Precision Viticulture

Mohimont, Lucas; Alin, François; Rondeau, Marine; Gaveau, Nathalie; Steffenel, Luiz Angelo

doi:10.3390/agronomy12102463

Cited by 34 publications

(15 citation statements)

References 161 publications

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“…PV can only be effective if the right quantity and quality of data are available and are properly processed and interpreted. Although machine learning and artificial intelligence are rapidly evolving [ 75 ], the role of the human factor in the decision-making process is still significant. The quality of wine particularly depends on the human factor [ 76 ].…”

Section: Discussionmentioning

confidence: 99%

How can proximal sensors help decision-making in grape production?

Mizik

2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

How can proximal sensors help decision-making in grape production?

Mizik

2023

Heliyon

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“…However, this study may have some intersection with Chen et al [24], which aimed to review studies that used deep learning for plant image identification. Besides, Mohimont et al [25] reviewed studies that used computer vision and DL for yield-related precision viticulture tasks, e.g. flower counting, grape detection, berry counting and yield estimation, while Ferro and Catania [26] surveyed the technologies employed in precision viticulture, covering topics ranging from sensors to computer vision algorithms for data processing.…”

Section: Of 34mentioning

confidence: 99%

“…Flower counting, grape detection, berry counting, yield estimation, disease identification and variety identification are examples of such tasks. Mohimont et al [25] analysed recent precision viticulture studies that applied DL techniques to all of the above yield-related tasks, excluding variety identification and disease detection. The number of studies the authors found for each task individually does not differ from the number of studies found in the present study.…”

Section: Comparison With Other Subfields Of Precision Viticulturementioning

confidence: 99%

Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review

Carneiro,

Cunha,

Sousa

2024

Preprint

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The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

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“…We must also not forget that the visible color is the result of an interaction between the spectrum of the emitted light and the surface. If a white sheet is illuminated by the light of a red bulb, then the sheet will also appear red [29].…”

Section: Search Subject By Bloommentioning

confidence: 99%

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

et al. 2023

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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.

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Computer Vision and Deep Learning for Precision Viticulture

Cited by 34 publications

References 161 publications

How can proximal sensors help decision-making in grape production?

How can proximal sensors help decision-making in grape production?

Deep Learning and Machine Learning for Automatic Grapevine Varieties Identification: A Brief Review

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

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