Proceedings :: International Society of Precision Agriculture

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Alves de Araújo, G

Ata-Ul-Karim, S

Amstalden, F

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Authors

Cammarano, D

Ata-Ul-Karim, S

Canicatti, M

Abalos, D

Zhou, Y

Tanaka, T.S

Butterbach-Bahl, K

Amaro, R.P

Amstalden, F

Berro Filho, C

Duft, D.G

Alves de Araújo, G

Costa Souza, J.B

Freire de Oliveira, M

Ortiz, B.V

Luns Hatum de Almeida, S

Felipe dos Santos, A

Pereira da Silva, R.P

Topics

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

Remote and Proximal Sensing of Soils and Crops

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Oral

Year

2026

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Filter results3 paper(s) found.
1. System-based Precision Agriculture for Sustainable Crop Production The major challenge addressed is the systemic mismanagement of nitrogen (N) fertilizer in agricultural fields leading to problems such as leaching of nitrates into groundwater and emission of harmful greenhouse gases. Digital technologies are commercialized in agriculture (available from the early 1990s) but have failed with N fertilization. Despite agriculture is the least digitized sector (as highlighted at the last World Economic Forum) to make a reliable recommendation, researchers need to... D. Cammarano, S. Ata-ul-karim, M. Canicatti, D. Abalos, Y. Zhou, T.S. Tanaka, K. Butterbach-bahl Download paper
2. Detection of Weed-Related Anomalies in Sugarcane Fields Using Sentinel-2 Imagery Weed infestation is one of the main causes of yield losses in agricultural systems, particularly in large-scale crops such as sugarcane. Conventional weed management, based on uniform herbicide application, often ignores the spatial variability of infestations, resulting in higher production costs and environmental impacts. In this context, remote sensing and machine learning techniques are recently being used as a solution for automation and precision in crop monitoring. In this study,... R.P. Amaro, F. Amstalden, C. Berro filho, D.G. Duft Download paper
3. Integrating Management Zones, Artificial Neural Networks and Remote Sensing for Smart Peanut Harvesting The integration of technologies contributes significantly to agricultural development, especially regarding the rational and more sustainable use of soil. Thus, the use of remote sensing and artificial intelligence techniques combined with precision agriculture can maximize smart harvesting for peanut crops, which face several challenges such as limited harvesting technology, indeterminate growth, and the development of pods below the soil surface. Therefore, this study aimed to develop a peanut...

Filter results3 paper(s) found.

1. System-based Precision Agriculture for Sustainable Crop Production

The major challenge addressed is the systemic mismanagement of nitrogen (N) fertilizer in agricultural fields leading to problems such as leaching of nitrates into groundwater and emission of harmful greenhouse gases. Digital technologies are commercialized in agriculture (available from the early 1990s) but have failed with N fertilization. Despite agriculture is the least digitized sector (as highlighted at the last World Economic Forum) to make a reliable recommendation, researchers need to... D. Cammarano, S. Ata-ul-karim, M. Canicatti, D. Abalos, Y. Zhou, T.S. Tanaka, K. Butterbach-bahl

Download paper

2. Detection of Weed-Related Anomalies in Sugarcane Fields Using Sentinel-2 Imagery

Weed infestation is one of the main causes of yield losses in agricultural systems, particularly in large-scale crops such as sugarcane. Conventional weed management, based on uniform herbicide application, often ignores the spatial variability of infestations, resulting in higher production costs and environmental impacts. In this context, remote sensing and machine learning techniques are recently being used as a solution for automation and precision in crop monitoring. In this study,... R.P. Amaro, F. Amstalden, C. Berro filho, D.G. Duft

Download paper

3. Integrating Management Zones, Artificial Neural Networks and Remote Sensing for Smart Peanut Harvesting

The integration of technologies contributes significantly to agricultural development, especially regarding the rational and more sustainable use of soil. Thus, the use of remote sensing and artificial intelligence techniques combined with precision agriculture can maximize smart harvesting for peanut crops, which face several challenges such as limited harvesting technology, indeterminate growth, and the development of pods below the soil surface. Therefore, this study aimed to develop a peanut...