@article{
 title = {Deep Learning for Post-Harvest Grape Diseases Detection},
 type = {article},
 year = {2023},
 keywords = {classification,deep learning,fruit grading,grape disease,segmentation},
 volume = {0},
 id = {5c818afd-6b85-37db-85a3-09b23a3d471c},
 created = {2023-10-27T07:38:16.458Z},
 file_attached = {true},
 profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2023-11-22T17:08:42.431Z},
 read = {false},
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 abstract = {Post-harvest fruit grading is a necessary step to avoid disease related loss in quality. This is relevant in the context of the Champagne industry where grapes can not be manipulated by machines to avoid crushing. Our team have been developing a computer vision based solution to automate this process. In this paper, our main contribution is the usage of a PSPnet segmentation model for real-time visible symptoms detection with a IoU score of 58%. The associated classification score reach 95%, which improved our previous work. We also study a MobileNet-V2 model’s ability to discriminate between different grape diseases in ideal condition.},
 bibtype = {article},
 author = {Mohimont, Lucas},
 doi = {10.3233/aise230025}
}

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