Proceedings :: International Society of Precision Agriculture

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Martini, D

Souza, J.B

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Authors

Kleinhenz, B

Röhrig, M

Scheiber, M

Feldhaus, J

Hartmann, B

Golla, B

Federle , C

Martini, D

Freire de Oliveira, M.F

Ortiz, B.V

Souza, J.B

Bao, Y

Hanyabui, E

Cardoso, P.C

Pereira da Silva, R.P

da Silva, T.R

de Oliveira, M.F

Souza, J.B

de Almeida, S.L

Luns, S

Souza, J.B

, B

Conceicao da Silva, L

Pereira da Silva, R.P

Carreira, V

Topics

Precision Crop Protection

Artificial Intelligence (AI) in Agriculture

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Oral

Poster

Year

2014

2024

2026

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Filter results4 paper(s) found.
1. Pesticide Application Manager (PAM) - Decision Support In Crop Protection Based On Terrain-, Machine-, Business- And Public Data Introduction Pesticide Application Manager (PAM) is a project, co-financed by the German Federal Office for Agriculture and Food (BLE) that aims to develop solutions for automating important processes in crop protection. Due to a series of rules and legal requirements for planning, implementation and documentation, crop protection is one of the most... B. Kleinhenz, M. Röhrig, M. Scheiber, J. Feldhaus, B. Hartmann, B. Golla, C. Federle , D. Martini Download paper
2. Towards a Digital Peanut Profile Board: a Deep Learning Approach Artificial intelligence techniques, particularly deep learning, offer promising avenues for revolutionizing object detection and counting algorithms in the context of digital agriculture. The challenges faced by peanut farmers, particularly the precise determination of optimal maturity for digging, have prompted innovative solutions. Traditionally, peanut maturity assessment has relied on the Peanut Maturity Index (PMI), employing a manual classification process with the aid of a peanut profile... M.F. Freire De Oliveira, B.V. Ortiz, J.B. Souza, Y. Bao, E. Hanyabui Download paper
3. High-resolution Orbital Imagery and Neural Networks to Predict Brix and Purity in Sugarcane Integrating artificial neural networks with high-resolution satellite remote sensing data can provide non-destructive indicators for assessing sugarcane quality at field scale. Conventional laboratory methods for sucrose-related quality assessment are costly, labor-intensive, and operationally demanding, particularly when applied continuously over large commercial areas. This study evaluated the potential of multispectral imagery from the PlanetScope CubeSat platform, vegetation indices, and accumulated... P. Cardoso, R.P. Silva, T.R. Da Silva, M.F. De Oliveira, J.B. Souza, S.L. De Almeida
4. Integration of Spectral Phenological Markers and Artificial Neural Networks for Modeling the Yield of Potato Cultivars The growing demand for food underscores the importance of essential crops such as potato. In this context, understanding yield dynamics is critical, and digital agriculture emerges as a key tool, enabling more efficient estimation of this variable without the need for destructive sampling. Accordingly, this study aimed to use orbital remote sensing combined with artificial intelligence algorithms to develop more accurate and precise models for potato yield prediction. Field data collection was... S. Luns, J.B. Souza, B. , L. Conceicao Da Silva, R.P. Silva, V. Carreira

Filter results4 paper(s) found.

1. Pesticide Application Manager (PAM) - Decision Support In Crop Protection Based On Terrain-, Machine-, Business- And Public Data

Introduction Pesticide Application Manager (PAM) is a project, co-financed by the German Federal Office for Agriculture and Food (BLE) that aims to develop solutions for automating important processes in crop protection. Due to a series of rules and legal requirements for planning, implementation and documentation, crop protection is one of the most... B. Kleinhenz, M. Röhrig, M. Scheiber, J. Feldhaus, B. Hartmann, B. Golla, C. Federle , D. Martini

Download paper

2. Towards a Digital Peanut Profile Board: a Deep Learning Approach

Artificial intelligence techniques, particularly deep learning, offer promising avenues for revolutionizing object detection and counting algorithms in the context of digital agriculture. The challenges faced by peanut farmers, particularly the precise determination of optimal maturity for digging, have prompted innovative solutions. Traditionally, peanut maturity assessment has relied on the Peanut Maturity Index (PMI), employing a manual classification process with the aid of a peanut profile... M.F. Freire De Oliveira, B.V. Ortiz, J.B. Souza, Y. Bao, E. Hanyabui

Download paper

3. High-resolution Orbital Imagery and Neural Networks to Predict Brix and Purity in Sugarcane

Integrating artificial neural networks with high-resolution satellite remote sensing data can provide non-destructive indicators for assessing sugarcane quality at field scale. Conventional laboratory methods for sucrose-related quality assessment are costly, labor-intensive, and operationally demanding, particularly when applied continuously over large commercial areas. This study evaluated the potential of multispectral imagery from the PlanetScope CubeSat platform, vegetation indices, and accumulated... P. Cardoso, R.P. Silva, T.R. Da Silva, M.F. De Oliveira, J.B. Souza, S.L. De Almeida

4. Integration of Spectral Phenological Markers and Artificial Neural Networks for Modeling the Yield of Potato Cultivars

The growing demand for food underscores the importance of essential crops such as potato. In this context, understanding yield dynamics is critical, and digital agriculture emerges as a key tool, enabling more efficient estimation of this variable without the need for destructive sampling. Accordingly, this study aimed to use orbital remote sensing combined with artificial intelligence algorithms to develop more accurate and precise models for potato yield prediction. Field data collection was... S. Luns, J.B. Souza, B. , L. Conceicao Da Silva, R.P. Silva, V. Carreira