Open Access
| Issue |
E3S Web of Conf.
Volume 537, 2024
International Scientific and Practical Conference “Sustainable Development of the Environment and Agriculture: Green and Environmental Technologies” (SDEA 2024)
|
|
|---|---|---|
| Article Number | 10023 | |
| Number of page(s) | 9 | |
| Section | Innovative Technologies in Food Industry and Public Catering | |
| DOI | https://doi.org/10.1051/e3sconf/202453710023 | |
| Published online | 13 June 2024 | |
- M. E. Perdomo, M. Cardona, D.M. Castro, W.M. Mejia, Literature Review on Artificial Intelligence Implementation in the Honduran Agricultural Sector. 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), Soyapango, El Salvador, 1–5 (2022) [Google Scholar]
- M.E. Perdomo, I. Magomedov, O. Anokhina, Literary Review: Coffee Technologies. BIO Web Conf., 57, 05003 (2023) [CrossRef] [EDP Sciences] [Google Scholar]
- J.L. Ordoñez-Avila, M.G. Martínez-Rangel, Proposed Open Systems Model and Intelligent Agriculture in Coffee Farms within the Harvesting Process, en Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 2022): “Exponential Technologies and Global Challenges: Moving toward a new culture of entrepreneurship and innovation for sustainable development”, Latin American and Caribbean Consortium of Engineering Institutions (2022) [Google Scholar]
- J.L. Ordoñez-Avila, M.E. Perdomo, D. Barahona, H. Perdomo, G. Sosa, M.G. Martínez-Rangel, Proposed Model for Coffee Cooperative in Santa Bárbara, Honduras. en Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 2022): “Exponential Technologies and Global Challenges: Moving toward a new culture of entrepreneurship and innovation for sustainable development”, Latin American and Caribbean Consortium of Engineering Institutions (2022) [Google Scholar]
- M.E. Perdomo, Relationship between coffee quality methods and the coffee price in Honduras. en Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 2022): “Exponential Technologies and Global Challenges: Moving toward a new culture of entrepreneurship and innovation for sustainable development”, Latin American and Caribbean Consortium of Engineering Institutions (2022) [Google Scholar]
- T.A. Heryanto, I.G.B.B. Nugraha, Classification of Coffee Beans Defect Using Mask Region-based Convolutional Neural Network. 2022 International Conference on Information Technology Systems and Innovation (ICITSI), 333–339 (2022) [Google Scholar]
- M.S. Fuentes, N.A.L. Zelaya, J.L.O. Avila, Coffee Fruit Recognition Using Artificial Vision and neural NETWORKS. 2020 5th International Conference on Control and Robotics Engineering (ICCRE), 224–228 (2020) [Google Scholar]
- J.R. Sanz-Uribe, P.J. Ramos-Giraldo, C.E. Oliveros-Tascon, Algorithm to Identify Maturation Stages of Coffee Fruits. Advances in Electrical and Electronics Engineering - IAENG Special Edition of the World Congress on Engineering and Computer Science 2008, 167–174 (2008) [Google Scholar]
- A.G. Alharbi, M. Arif, Detection And Classification Of Apple Diseases using Convolutional Neural Networks. 2020 2nd International Conference on Computer and Information Sciences (ICCIS), 1–6 (2020) [Google Scholar]
- I. Grijalva, B.J. Spiesman, B. McCornack, Computer vision model for sorghum aphid detection using deep learning. Journal of Agriculture and Food Research, 13, 100652 (2023) [CrossRef] [Google Scholar]
- A. Chaitanya, J. Shetty, P. Chiplunkar, Food Image Classification and Data Extraction Using Convolutional Neural Network and Web Crawlers. Procedia Computer Science, 218, 143–152 (2023) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.