Deep Learning Based Classification System for Identifying Weeds Using High-Resolution UAV Imagery
Autor: | Raphael Canals, Adel Hafiane, Eric Dericquebourg, M. Dian Bah |
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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), Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique (PRISME), Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges (ENSI Bourges) |
Rok vydání: | 2018 |
Předmět: |
2. Zero hunger
Computer science business.industry Classification procedure Deep learning [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] High resolution 04 agricultural and veterinary sciences 02 engineering and technology Agricultural engineering 15. Life on land Convolutional neural network Field (computer science) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 040103 agronomy & agriculture 0202 electrical engineering electronic engineering information engineering 0401 agriculture forestry and fisheries 020201 artificial intelligence & image processing Precision agriculture Artificial intelligence business ComputingMilieux_MISCELLANEOUS |
Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030011765 Computing Conference Computing Conference, Jul 2018, Londres, United Kingdom. pp.176-187, ⟨10.1007/978-3-030-01177-2_13⟩ |
DOI: | 10.1007/978-3-030-01177-2_13 |
Popis: | In recent years, weeds is responsible for most of the agricultural yield losses. To deal with this problem Omega, farmers resort to spraying pesticides throughout the field. Such method not only requires huge quantities of herbicides but impact environment and humans health. In this paper, we propose a new vision-based classification system for identifying weeds in vegetable fields such as spinach, beet and bean by applying convolutional neural networks (CNNs) and crop lines information. In this study, we combine deep learning with line detection to enforce the classification procedure. The proposed method is applied to high-resolution Unmanned Aerial Vehicles (UAV) images of vegetables taken about 20 m above the soil. We have performed an extensive evaluation of the method with real data. The results showed that the proposed method of weeds detection was effective in different crop fields. The overall precision for the beet, spinach and bean is respectively of 93%, 81% and 69%. |
Databáze: | OpenAIRE |
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