Predicting the site-specific distribution of agrochemical spray deposition in vineyards at multiple phenological stages using 2D LiDAR-based primary canopy attributes

Cheraiet, A.; Naud, Olivier; Carra, Mathilde; Codis, Sébastien; Lebeau, Frédéric; Taylor, James A.

doi:10.1016/j.compag.2021.106402

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…The 2D laser scanner surveys revealed a significant relationship with pruning weight (r = 0.80), yield and vigour indices, demonstrating the potential of using laser scanner measurements to assess the variability of vine vigour within vineyards [81]. The evaluation of canopy attributes (height, width and density), assessed with laser scanning technology provide information to improve agrochemical spray treatments [82,83].…”

Section: Technologies and Sensors For Vineyard Monitoringmentioning

confidence: 84%

“…Through surveys performed with UAVs, very detailed spatial resolutions can be achieved [216,217], unlike satellite images, in which the resolution is often not sufficient to perform these surveys [218]. Data for implementing 2D maps or 3D modelling can be provided by laser scanners such as LiDAR [82], or derived from RGB, multispectral imagery [17,176]. LiDAR, however, is still considered an expensive solution with some operational limitations for winegrowers, a low-cost solution consists of UAVs equipped with consumer RGB cameras [219].…”

Section: Vineyard Canopy Geometry Based On the Point Cloudmentioning

confidence: 99%

“…elling can be provided by laser scanners such as LiDAR [82], or derived from RGB, multispectral imagery [17,176]. LiDAR, however, is still considered an expensive solution with some operational limitations for winegrowers, a low-cost solution consists of UAVs equipped with consumer RGB cameras [219].…”

Section: Vineyard Canopy Geometry Based On the Point Cloudmentioning

confidence: 99%

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Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Ferro

Catania

2023

Horticulturae

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The potential of precision viticulture has been highlighted since the first studies performed in the context of viticulture, but especially in the last decade there have been excellent results have been achieved in terms of innovation and simple application. The deployment of new sensors for vineyard monitoring is set to increase in the coming years, enabling large amounts of information to be obtained. However, the large number of sensors developed and the great amount of data that can be collected are not always easy to manage, as it requires cross-sectoral expertise. The preliminary section of the review presents the scenario of precision viticulture, highlighting its potential and possible applications. This review illustrates the types of sensors and their operating principles. Remote platforms such as satellites, unmanned aerial vehicles (UAV) and proximal platforms are also presented. Some supervised and unsupervised algorithms used for object-based image segmentation and classification (OBIA) are then discussed, as well as a description of some vegetation indices (VI) used in viticulture. Photogrammetric algorithms for 3D canopy modelling using dense point clouds are illustrated. Finally, some machine learning and deep learning algorithms are illustrated for processing and interpreting big data to understand the vineyard agronomic and physiological status. This review shows that to perform accurate vineyard surveys and evaluations, it is important to select the appropriate sensor or platform, so the algorithms used in post-processing depend on the type of data collected. Several aspects discussed are fundamental to the understanding and implementation of vineyard variability monitoring techniques. However, it is evident that in the future, artificial intelligence and new equipment will become increasingly relevant for the detection and management of spatial variability through an autonomous approach.

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Section: Technologies and Sensors For Vineyard Monitoringmentioning

confidence: 84%

Section: Vineyard Canopy Geometry Based On the Point Cloudmentioning

confidence: 99%

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Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Ferro

Catania

2023

Horticulturae

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“…To date, it has been established that the use of computer vision, thermography, spectroscopy, chlorophyll fluorescence, multispectral and hyperspectral imaging provides objective and rapid diagnosis of mildew, oidium, esca, "golden yellowing" phytoplasmosis, and the viral grape leaf roll disease [5], [7][8][9][10][11]. The first positive results were obtained on the creation IOP Publishing doi:10.1088/1755-1315/1206/1/012021 2 of data sets and deep machine learning using convolutional neural networks for the recognition of the above diseases, as well as on the development of web applications for creating decision support systems and monitoring the parameters of introducing protective equipment based on the use of sensors and mathematical modeling [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Prospects for digitalization of monitoring of Lepidoptera phytophages of grapes

Radionovskaya

Galkina

Андреев

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The effectiveness of phytophagous insect control systems depends on the accuracy of the chosen method of monitoring their numbers and the available information on the dynamics of the population number. As part of the automation of the processes of collecting and analyzing data on the biological activity of pheromone preparations of the phytophage of grapes Lobesia botrana Den. et Schiff., the method of counting the number of pest butterflies on the glue inserts of pheromone traps has been improved by the use of digital cameras and MCView software. According to the validation results, it was found that, relative to the actual data, the improved method ensured the accuracy of counting butterflies at a density of 45-321 imago/insert at an average of 99.1%, which exceeded the results of counting by the traditional visual method by an average of 5%. It is shown that partial method automation made it possible to reduce the influence of a particular researcher, as a subjective factor, on the experiment results. An increase in the time required for counting butterflies by the improved method relative to the visual method by an average of 30% was noted. The presented method of quantitative evaluation of the results of pheromone monitoring in vineyards is a transitional stage to the full automation of this information collection and analysis. For this purpose, to date, photosets have been collected containing more than 1000 photos of glue inserts of pheromone traps with captured butterflies and training of a convolutional neural network has begun designed to detect and quantify such grape lepidopteran phytophages as Lobesia botrana, Helicoverpa armigera Hbn., Theresimima ampelophaga Bayl. (Insecta: Lepidoptera).

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“…To achieve the goal of sustainable protection and to produce a high quality and quantity of grapes, a mix of complementary approaches is needed. These include the use of more resistant varieties, spraying with biological PPPs, improving the application methodology, incorporating novel dosing algorithms and spraying techniques tailored to individual plants, and other methods and practices [1,2]. A review of measurement and spraying methods in permanent crops can be found in [3].…”

Section: Introductionmentioning

confidence: 99%

Vine Canopy Reconstruction and Assessment with Terrestrial Lidar and Aerial Imaging

et al. 2022

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For successful dosing of plant protection products, the characteristics of the vine canopies should be known, based on which the spray amount should be dosed. In the field experiment, we compared two optical experimental methods, terrestrial lidar and aerial photogrammetry, with manual defoliation of some selected vines. Like those of other authors, our results show that both terrestrial lidar and aerial photogrammetry were able to represent the canopy well with correlation coefficients around 0.9 between the measured variables and the number of leaves. We found that in the case of aerial photogrammetry, significantly more points were found in the point cloud, but this depended on the choice of the ground sampling distance. Our results show that in the case of aerial UAS photogrammetry, subdividing the vine canopy segments to 5 × 5 cm gives the best representation of the volume of vine canopies.

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Predicting the site-specific distribution of agrochemical spray deposition in vineyards at multiple phenological stages using 2D LiDAR-based primary canopy attributes

Cited by 11 publications

References 24 publications

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Prospects for digitalization of monitoring of Lepidoptera phytophages of grapes

Vine Canopy Reconstruction and Assessment with Terrestrial Lidar and Aerial Imaging

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