Proceedings :: International Society of Precision Agriculture

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Furlan Maggi, M

Figueiredo, G

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Authors

Torres Avila, E

Bazzi, C.L

Moro Lumertz, S

CELY BONILLA, E

Furlan Maggi, M

Medeiros, T.A

Perez, D

Schenatto, K

Sobjak, R

Chaves, C

Quicaña, A.

Chimello, L

Hermes, M

Andreoli, A

Albuquerque, M

Figueiredo, G

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Digital Solutions for Soil Health, Water Quality, and Conservation Practices

Type

Poster

Year

2026

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Filter results2 paper(s) found.
1. Automated Detection of Melons (Cucumis melo L.) via Multispectral UAV and Deep Learning in Honduras Accurate agricultural production estimation is vital for the logistical and financial efficiency of agribusiness. This study proposes an automated melon detection pipeline using a multispectral Unmanned Aerial Vehicle (UAV) and deep learning architectures. The experiment was conducted in Apacilagua, Honduras, during the 2023–2024 season, covering an area of 32.17 ha. Data collection took place between 90 and 100 days after sowing (DAS)—a critical maturation phase—using a DJI... E. Torres Avila, C.L. Bazzi, S. Moro Lumertz, E. Cely Bonilla, M. Furlan Maggi, T.A. Medeiros, D. Perez, K. Schenatto, R. Sobjak Download paper
2. Use of Digital Permeameter for the Functional Characterization of Geoenvironments Characterizing agricultural geoenvironments with precision is inherently a complex task. Historically, this process has relied on quasi-static edaphic attributes, such as soil texture and apparent electrical conductivity. However, a critical problem exists, as these parameters exhibit low sensitivity to ephemeral structural changes resulting from soil management systems. Texture conditions the productive potential, yet it fails to reflect modifications in pore geometry induced by mechanical pressures... C. Chaves, A. . Quicaña, L. Chimello, M. Hermes, A. Andreoli, M. Albuquerque, G. Figueiredo, M. Hermes

Filter results2 paper(s) found.

1. Automated Detection of Melons (Cucumis melo L.) via Multispectral UAV and Deep Learning in Honduras

Accurate agricultural production estimation is vital for the logistical and financial efficiency of agribusiness. This study proposes an automated melon detection pipeline using a multispectral Unmanned Aerial Vehicle (UAV) and deep learning architectures. The experiment was conducted in Apacilagua, Honduras, during the 2023–2024 season, covering an area of 32.17 ha. Data collection took place between 90 and 100 days after sowing (DAS)—a critical maturation phase—using a DJI... E. Torres Avila, C.L. Bazzi, S. Moro Lumertz, E. Cely Bonilla, M. Furlan Maggi, T.A. Medeiros, D. Perez, K. Schenatto, R. Sobjak

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2. Use of Digital Permeameter for the Functional Characterization of Geoenvironments

Characterizing agricultural geoenvironments with precision is inherently a complex task. Historically, this process has relied on quasi-static edaphic attributes, such as soil texture and apparent electrical conductivity. However, a critical problem exists, as these parameters exhibit low sensitivity to ephemeral structural changes resulting from soil management systems. Texture conditions the productive potential, yet it fails to reflect modifications in pore geometry induced by mechanical pressures... C. Chaves, A. . Quicaña, L. Chimello, M. Hermes, A. Andreoli, M. Albuquerque, G. Figueiredo, M. Hermes