GrapesNet: Indian RGB &amp; RGB-D vineyard image datasets for deep learning applications

Barbole, Ms. Dhanashree K.; Jadhav, Parul

doi:10.1016/j.dib.2023.109100

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…The wGrapeUNIPD-DL dataset [46], available since 2022, comprises 373 images of various grape varieties captured at different phenological stages across six Italian vineyard locations. GrapesNet [47], published in 2023, offers four datasets containing RGB and RGB-D images of grape bunches, facilitating tasks such as grape segmentation and weight prediction. Representative examples of images appearing in the aforementioned datasets are presented in Figure 1.…”

Section: Available Datasetsmentioning

confidence: 99%

A Grape Dataset for Instance Segmentation and Maturity Estimation

Blekos,

Chatzis,

Kotaidou

et al. 2023

Agronomy

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Grape maturity estimation is vital in precise agriculture as it enables informed decision making for disease control, harvest timing, grape quality, and quantity assurance. Despite its importance, there are few large publicly available datasets that can be used to train accurate and robust grape segmentation and maturity estimation algorithms. To this end, this work proposes the CERTH grape dataset, a new sizeable dataset that is designed explicitly for evaluating deep learning algorithms in grape segmentation and maturity estimation. The proposed dataset is one of the largest currently available grape datasets in the literature, consisting of around 2500 images and almost 10 k grape bunches, annotated with masks and maturity levels. The images in the dataset were captured under various illumination conditions and viewing angles and with significant occlusions between grape bunches and leaves, making it a valuable resource for the research community. Thorough experiments were conducted using a plethora of general object detection methods to provide a baseline for the future development of accurate and robust grape segmentation and maturity estimation algorithms that can significantly advance research in the field of viticulture.

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Section: Available Datasetsmentioning

confidence: 99%

A Grape Dataset for Instance Segmentation and Maturity Estimation

Blekos,

Chatzis,

Kotaidou

et al. 2023

Agronomy

View full text Add to dashboard Cite

show abstract

“…They help in the identification and classification of diseases [8] , as well as in the detailed analysis of yield factors [9] . Following the idea of [10] , where they introduced the concept of different angles with a handheld camera to avoid occlusions and provided 11000+ images, this dataset offers Unmanned Aerial Vehicles (UAV) videos with grape bunch annotations recorded in a commercial vineyard under challenging conditions, such as occlusion. This endeavour aims not just at enriching the repository of data available for precision agriculture but also at overcoming specific hurdles not only for object detection within viticulture, similar to [11] where they provided instances to locate the bunches in the images but including tracking, by adding the same ID of each grape bunch along frames.…”

Section: Introductionmentioning

confidence: 99%