Proceedings :: International Society of Precision Agriculture

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Yu, Y

Yatskul, A

Yablonski, D

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Authors

Zakhary, A

Henkler, S

Ehteshami-Bejnordi, A

Yablonski, D

Sorokina, V

Urbina Salazar, D

Yatskul, A

Pinet, F

Dujany, A

Ugarte, C

Bishop, T

Yu, Y

Tilse, M.J

Filippi, P

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Poster

Oral

Year

2026

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Authors

Filter results3 paper(s) found.
1. Multi-modal Auto-labelling Pipeline for Sugar Beet Crop-Weed Classification The deployment of robust machine learning models in precision agriculture is frequently bottlenecked by the scarcity of high-quality annotated data. Despite recent developments in ML-based crop–weed detection systems, the availability of large-scale, high-quality labelled datasets remains a major limitation. State-of-the-art models require extensive data that captures diverse growth stages, variable lighting and weather conditions, and a wide range of weed species. Creating such datasets... A. Zakhary, S. Henkler, A. Ehteshami-bejnordi, D. Yablonski, V. Sorokina Download paper
2. Integrating Tractor-tire-tool Adjustable Parameters and UAV‑derived Soil Indices to Predict Fuel Consumption and Crop Emergence in Spring Barley Sowing The optimization of energy use and agronomic performance in agricultural operations has become a central challenge in modern agriculture. To achieve this dual objective, farmers could adjust the machinery settings of a tractor-tire-tool system to ensure efficient resource utilization while maintaining optimal agronomic outcomes. This study was conducted as a part of the AgrEnOp project, which aims to predict fuel consumption (l/ha) and crop emergence (plant/m2) based on operator-adjustable... D. Urbina Salazar, A. Yatskul, F. Pinet, A. Dujany, C. Ugarte Download paper
3. Soil Water Nowcasting for Site-specific Yield Potential Estimation Knowing how much plant available water (PAW) is stored across a field at key decision points in the growing season is fundamental to precision agriculture. Spatial variability in soil water translates directly into variability in water-limited yield potential, yet most growers lack the tools to quantify this at the within-field scale. Here we present a Soil Water-Energy Balance (SWEB) model that offers a framework to deliver daily, 30 m resolution estimates of PAW across any dryland paddock... T. Bishop, Y. Yu, M.J. Tilse, P. Filippi Download paper

Filter results3 paper(s) found.

1. Multi-modal Auto-labelling Pipeline for Sugar Beet Crop-Weed Classification

The deployment of robust machine learning models in precision agriculture is frequently bottlenecked by the scarcity of high-quality annotated data. Despite recent developments in ML-based crop–weed detection systems, the availability of large-scale, high-quality labelled datasets remains a major limitation. State-of-the-art models require extensive data that captures diverse growth stages, variable lighting and weather conditions, and a wide range of weed species. Creating such datasets... A. Zakhary, S. Henkler, A. Ehteshami-bejnordi, D. Yablonski, V. Sorokina

Download paper

2. Integrating Tractor-tire-tool Adjustable Parameters and UAV‑derived Soil Indices to Predict Fuel Consumption and Crop Emergence in Spring Barley Sowing

The optimization of energy use and agronomic performance in agricultural operations has become a central challenge in modern agriculture. To achieve this dual objective, farmers could adjust the machinery settings of a tractor-tire-tool system to ensure efficient resource utilization while maintaining optimal agronomic outcomes. This study was conducted as a part of the AgrEnOp project, which aims to predict fuel consumption (l/ha) and crop emergence (plant/m2) based on operator-adjustable... D. Urbina Salazar, A. Yatskul, F. Pinet, A. Dujany, C. Ugarte

Download paper

3. Soil Water Nowcasting for Site-specific Yield Potential Estimation

Knowing how much plant available water (PAW) is stored across a field at key decision points in the growing season is fundamental to precision agriculture. Spatial variability in soil water translates directly into variability in water-limited yield potential, yet most growers lack the tools to quantify this at the within-field scale. Here we present a Soil Water-Energy Balance (SWEB) model that offers a framework to deliver daily, 30 m resolution estimates of PAW across any dryland paddock... T. Bishop, Y. Yu, M.J. Tilse, P. Filippi

Download paper