@article{
 title = {Meteorological Data and UAV Images for the Detection and Identification of Grapevine Disease Using Deep Learning},
 type = {article},
 year = {2022},
 keywords = {ouhami2022meteorologicaldatauav},
 publisher = {Institute of Electrical and Electronics Engineers Inc.},
 id = {9b6bb1a0-04fb-3d64-a3fe-5d81b4ab86b4},
 created = {2023-11-06T11:27:14.937Z},
 accessed = {2023-11-06},
 file_attached = {true},
 profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2023-11-23T18:12:04.333Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {6b565182-74c4-44fd-98cc-10618152e2ae,4dd63b9a-2e78-4e16-b45d-b692d8e0c5c3,bd3c6f2e-3514-47cf-bc42-12db8b9abe45},
 private_publication = {false},
 abstract = {Vine disease is a serious issue in viticulture that causes an increase in the quantity and quantity of grapes. The early detection of vine disease can significantly improve their control and avoid the spread of pathogens, viruses, or fungi. One considerable solution is to use remote sensing as it is one of the important conventional eco-friendly methods for disease detection. Together with Deep learning models, remote sensing is used to improve the prediction quality and address effectively disease detection within semantic segmentation and disease identification based on meteorological history analysis. In this paper, we present our approach for downy mildew in vineyards identification and localization using a deep learning approach on multimodal decision fusion. We have reached an overall prediction segmentation accuracy and recall of 85,14% and 84,91% respectively.},
 bibtype = {article},
 author = {Ouhami, Maryam and Es-Saady, Youssef and El Hajj, Mohammed and Canals, Raphael and Hafiane, Adel},
 doi = {10.1109/EHB55594.2022.9991443},
 journal = {2022 10th E-Health and Bioengineering Conference, EHB 2022}
}

{"_id":"Zf7ii9q72yxMjZ5Ki","bibbaseid":"ouhami-essaady-elhajj-canals-hafiane-meteorologicaldataanduavimagesforthedetectionandidentificationofgrapevinediseaseusingdeeplearning-2022","author_short":["Ouhami, M.","Es-Saady, Y.","El Hajj, M.","Canals, R.","Hafiane, A."],"bibdata":{"title":"Meteorological Data and UAV Images for the Detection and Identification of Grapevine Disease Using Deep Learning","type":"article","year":"2022","keywords":"ouhami2022meteorologicaldatauav","publisher":"Institute of Electrical and Electronics Engineers Inc.","id":"9b6bb1a0-04fb-3d64-a3fe-5d81b4ab86b4","created":"2023-11-06T11:27:14.937Z","accessed":"2023-11-06","file_attached":"true","profile_id":"f1f70cad-e32d-3de2-a3c0-be1736cb88be","group_id":"5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1","last_modified":"2023-11-23T18:12:04.333Z","read":"true","starred":false,"authored":false,"confirmed":false,"hidden":false,"folder_uuids":"6b565182-74c4-44fd-98cc-10618152e2ae,4dd63b9a-2e78-4e16-b45d-b692d8e0c5c3,bd3c6f2e-3514-47cf-bc42-12db8b9abe45","private_publication":false,"abstract":"Vine disease is a serious issue in viticulture that causes an increase in the quantity and quantity of grapes. The early detection of vine disease can significantly improve their control and avoid the spread of pathogens, viruses, or fungi. One considerable solution is to use remote sensing as it is one of the important conventional eco-friendly methods for disease detection. Together with Deep learning models, remote sensing is used to improve the prediction quality and address effectively disease detection within semantic segmentation and disease identification based on meteorological history analysis. In this paper, we present our approach for downy mildew in vineyards identification and localization using a deep learning approach on multimodal decision fusion. We have reached an overall prediction segmentation accuracy and recall of 85,14% and 84,91% respectively.","bibtype":"article","author":"Ouhami, Maryam and Es-Saady, Youssef and El Hajj, Mohammed and Canals, Raphael and Hafiane, Adel","doi":"10.1109/EHB55594.2022.9991443","journal":"2022 10th E-Health and Bioengineering Conference, EHB 2022","bibtex":"@article{\n title = {Meteorological Data and UAV Images for the Detection and Identification of Grapevine Disease Using Deep Learning},\n type = {article},\n year = {2022},\n keywords = {ouhami2022meteorologicaldatauav},\n publisher = {Institute of Electrical and Electronics Engineers Inc.},\n id = {9b6bb1a0-04fb-3d64-a3fe-5d81b4ab86b4},\n created = {2023-11-06T11:27:14.937Z},\n accessed = {2023-11-06},\n file_attached = {true},\n profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},\n group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},\n last_modified = {2023-11-23T18:12:04.333Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {6b565182-74c4-44fd-98cc-10618152e2ae,4dd63b9a-2e78-4e16-b45d-b692d8e0c5c3,bd3c6f2e-3514-47cf-bc42-12db8b9abe45},\n private_publication = {false},\n abstract = {Vine disease is a serious issue in viticulture that causes an increase in the quantity and quantity of grapes. The early detection of vine disease can significantly improve their control and avoid the spread of pathogens, viruses, or fungi. One considerable solution is to use remote sensing as it is one of the important conventional eco-friendly methods for disease detection. Together with Deep learning models, remote sensing is used to improve the prediction quality and address effectively disease detection within semantic segmentation and disease identification based on meteorological history analysis. In this paper, we present our approach for downy mildew in vineyards identification and localization using a deep learning approach on multimodal decision fusion. We have reached an overall prediction segmentation accuracy and recall of 85,14% and 84,91% respectively.},\n bibtype = {article},\n author = {Ouhami, Maryam and Es-Saady, Youssef and El Hajj, Mohammed and Canals, Raphael and Hafiane, Adel},\n doi = {10.1109/EHB55594.2022.9991443},\n journal = {2022 10th E-Health and Bioengineering Conference, EHB 2022}\n}","author_short":["Ouhami, M.","Es-Saady, Y.","El Hajj, M.","Canals, R.","Hafiane, A."],"urls":{"Paper":"https://bibbase.org/service/mendeley/bfbbf840-4c42-3914-a463-19024f50b30c/file/6112261b-be4e-8f9b-013d-afbb5fdfd14a/full_text.pdf.pdf"},"biburl":"https://bibbase.org/service/mendeley/bfbbf840-4c42-3914-a463-19024f50b30c","bibbaseid":"ouhami-essaady-elhajj-canals-hafiane-meteorologicaldataanduavimagesforthedetectionandidentificationofgrapevinediseaseusingdeeplearning-2022","role":"author","keyword":["ouhami2022meteorologicaldatauav"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/bfbbf840-4c42-3914-a463-19024f50b30c","dataSources":["2252seNhipfTmjEBQ"],"keywords":["ouhami2022meteorologicaldatauav"],"search_terms":["meteorological","data","uav","images","detection","identification","grapevine","disease","using","deep","learning","ouhami","es-saady","el hajj","canals","hafiane"],"title":"Meteorological Data and UAV Images for the Detection and Identification of Grapevine Disease Using Deep Learning","year":2022}