Deep Transfer Learning Models for Tomato Disease Detection
| Autor: | Maryam Ouhami, Adel Hafiane, Mohamed El Hajji, Mostafa El Yassa, Raphael Canals, Youssef Es-Saady |
|---|---|
| Přispěvatelé: | Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
2. Zero hunger
010504 meteorology & atmospheric sciences Disease detection business.industry media_common.quotation_subject fungi food and beverages 02 engineering and technology Agricultural engineering Vegetable crops 01 natural sciences Crop protection Agriculture [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Precision agriculture Business Agricultural productivity Transfer of learning ComputingMilieux_MISCELLANEOUS 0105 earth and related environmental sciences media_common |
| Zdroj: | Image and Signal Processing Springer Image and Signal Processing Springer, pp.65-73, 2020, ⟨10.1007/978-3-030-51935-3_7⟩ Lecture Notes in Computer Science ISBN: 9783030519346 ICISP |
| DOI: | 10.1007/978-3-030-51935-3_7⟩ |
| Popis: | Vegetable crops in Morocco and especially in the Sous-Massa region are exposed to parasitic diseases and pest attacks which affect the quantity and the quality of agricultural production. Precision farming is introduced as one of the biggest revolutions in agriculture, which is committed to improving crop protection by identifying, analyzing and managing variability delivering effective treatment in the right place, at the right time, and with the right rate. |
| Databáze: | OpenAIRE |
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