Proceedings :: International Society of Precision Agriculture

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Jensen, S.K

Bodanese, M

Oliveira, M.F

Valarares, S.V

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Authors

Salvador, I

Coelho, A.L

Valarares, S.V

Queiroz, D

Canciani, M

Han, E

Jørgensen, U

Ikeda, Y

Hansen, N.P

Jensen, S.K

Weisbjerg, M.R

Didion, T

Silva, V.S

Silva, E.S

Silva, D.O

Oliveira, M.F

Tavares, A.C

Negrini, R.P

Mendes, L.A

Topics

Remote and Proximal Sensing of Soils and Crops

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Poster

Oral

Year

2026

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Filter results3 paper(s) found.
1. Prediction of Soil Potassium Concentration Using Electrical Impedance Spectroscopy The increasing global dependence on potassium fertilizers and instabilities in international trade, intensified by recent geopolitical conflicts, have raised concerns regarding potassium (K) availability and costs in 2026. Since variable-rate fertilizer application enables the optimization of agricultural inputs, the delineation of management zones based on the spatial variability of soil potassium concentration becomes essential. This study aimed to evaluate the use of electrical impedance spectroscopy... I. Salvador, A.L. Coelho, S.V. Valarares, D. Queiroz Download paper
2. UAV-Based Multispectral Modelling of Biomass and Crude Protein Yield for Green Biorefinery Applications In animal production systems, protein demand is steadily increasing due to global population growth. This rising demand has highlighted the need to identify alternative and sustainable protein sources. Green biorefinery systems can efficiently extract protein from plant biomass. Previous studies confirmed that perennial grass crops such as Perennial Ryegrass, Festulolium, and Tall Fescue can produce high-quality biomass suitable for protein extraction. An estimation model of biomass yield and... M. Canciani, E. Han, U. Jørgensen, Y. Ikeda, N.P. Hansen, S.K. Jensen, M.R. Weisbjerg, T. Didion Download paper
3. Satellite Embedding-Based Corn Yield Prediction Using AutoML and Explainable AI Accurate, spatially explicit yield mapping underpins many precision agriculture decisions (e.g., variable-rate inputs and zone management), yet reliable yield monitor data are not always available and can be difficult to standardize across operations. Satellite-based yield models are often built from hand-crafted vegetation indices or phenology metrics, which may limit transferability across fields and years. Here, we evaluated a pixel-level corn yield prediction workflow that uses Satellite Embedding... V.S. Silva, E.S. Silva, D.O. Silva, M.F. Oliveira, A.C. Tavares, R.P. Negrini, L.A. Mendes Download paper

Filter results3 paper(s) found.

1. Prediction of Soil Potassium Concentration Using Electrical Impedance Spectroscopy

The increasing global dependence on potassium fertilizers and instabilities in international trade, intensified by recent geopolitical conflicts, have raised concerns regarding potassium (K) availability and costs in 2026. Since variable-rate fertilizer application enables the optimization of agricultural inputs, the delineation of management zones based on the spatial variability of soil potassium concentration becomes essential. This study aimed to evaluate the use of electrical impedance spectroscopy... I. Salvador, A.L. Coelho, S.V. Valarares, D. Queiroz

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2. UAV-Based Multispectral Modelling of Biomass and Crude Protein Yield for Green Biorefinery Applications

In animal production systems, protein demand is steadily increasing due to global population growth. This rising demand has highlighted the need to identify alternative and sustainable protein sources. Green biorefinery systems can efficiently extract protein from plant biomass. Previous studies confirmed that perennial grass crops such as Perennial Ryegrass, Festulolium, and Tall Fescue can produce high-quality biomass suitable for protein extraction. An estimation model of biomass yield and... M. Canciani, E. Han, U. Jørgensen, Y. Ikeda, N.P. Hansen, S.K. Jensen, M.R. Weisbjerg, T. Didion

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3. Satellite Embedding-Based Corn Yield Prediction Using AutoML and Explainable AI

Accurate, spatially explicit yield mapping underpins many precision agriculture decisions (e.g., variable-rate inputs and zone management), yet reliable yield monitor data are not always available and can be difficult to standardize across operations. Satellite-based yield models are often built from hand-crafted vegetation indices or phenology metrics, which may limit transferability across fields and years. Here, we evaluated a pixel-level corn yield prediction workflow that uses Satellite Embedding... V.S. Silva, E.S. Silva, D.O. Silva, M.F. Oliveira, A.C. Tavares, R.P. Negrini, L.A. Mendes

Download paper