@article{
 title = {An annotated image dataset of vegetable crops at an early stage of growth for proximal sensing applications},
 type = {article},
 year = {2022},
 keywords = {Deep learning,Neural network,Object detection,Precision agriculture,Tracking algorithm},
 pages = {108035},
 volume = {42},
 month = {1},
 publisher = {Elsevier B.V.},
 day = {1},
 id = {d8dcbddd-1461-33fd-984c-127f3836acc2},
 created = {2022-07-15T12:46:39.056Z},
 file_attached = {true},
 profile_id = {bfbbf840-4c42-3914-a463-19024f50b30c},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2022-11-21T11:38:10.013Z},
 read = {true},
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 confirmed = {false},
 hidden = {false},
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 abstract = {This article introduces a dataset of 2 801 images of vegetable crops. Maize ( Zea mays ), bean ( Phaseolus vulgaris ) and leek ( Allium ampeloprasum ) crops at an early stage of develop- ment (between 2 and 5 weeks from seeding of transplanting) are supported. Two kinds of annotations are provided: (i) bounding boxes enclosing the crops of interest or their stems, weeds being left apart, and (ii) crop structures in the form of star graphs whose vertices are the plant organs (stems and leaves) and whose edges represent the connections be- tween them. The images have been captured in various pro- duction and experimentation plots in France using an acqui- sition module which controls light conditions. They present a wide variety of soil conditions, weed infestation and growth stages. This dataset can benefit precision hoeing and in-field crop monitoring applications that are based on proximal im- agery.},
 bibtype = {article},
 author = {Lac, Louis and Keresztes, Barna and Louargant, Marine and Donias, Marc and Da Costa, Jean-Pierre},
 doi = {10.1016/j.compag.2021.106606},
 journal = {Data in Brief},
 number = {2352-3409}
}

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