Proceedings :: International Society of Precision Agriculture

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Ribeiro, M

Romaneckas, K

Raucci, A.R

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Authors

Teixeira, S.A

Valdivino, R

Tsukahara, R

Ribeiro, M

Kriauciuniene, Z

Kazlauskas, M

Romaneckas, K

Buragiene, S

Bručienė, I

Šarauskis, E

GUERRA, P

Raucci, A.R

Gutierrez , S.A

Botero, J.F

Kamienski, C

Campos de Oliveira, F.M

Topics

Precision Crop Protection, Pest, and Plant Health

Site-Specific Nutrient, Lime and Seed Management

Variable-Rate Irrigation, Drainage Optimization, and Water Management

Type

Oral

Year

2026

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Filter results3 paper(s) found.
1. Machine Learning Pipeline to Estimate Soybean Rust Severity Using UAV-derived Multispectral Indices Asian Soybean Rust is one of the most destructive diseases affecting soybean crops worldwide and can result in yield losses of up to 90% when control measures are not implemented in a timely manner. Conventional disease monitoring based on field scouting is time-consuming, labor-intensive, and inherently subjective, often failing to adequately represent the spatial variability of disease across production fields. These limitations highlight the need for automated, objective, and high throughput... S.A. Teixeira, R. Valdivino, R. Tsukahara, M. Ribeiro Download paper
2. The Agronomic and Bioeconomic Aspects of Site-Specific Seeding Rates and Depths for Winter Wheat in Lithuania Precision seeding is one of the most important agrotechnological solutions for smart agriculture. It exploits the variability of soil properties in the field to increase the agronomic and economic efficiency of crops. This study investigated the impact of site-specific seeding (SSS) on the yield and productivity parameters of winter wheat in Lithuania, as well as its economic benefits, compared with conventional uniform rate seeding (URS). Experiments were conducted in a field divided into five... Z. Kriauciuniene, M. Kazlauskas, K. Romaneckas, S. Buragiene, I. Bručienė, E. Šarauskis Download paper
3. Soil-Sensing-Based Irrigation Decision Modeling for Greenhouse Tomato Crops Using Machine Learning Global agriculture faces increasing pressure to optimize water-use efficiency, particularly for high-demand crops like tomato (Solanum lycopersicum). Tomato is among the most widely consumed vegetables worldwide, playing a central role in global food systems. From an agronomic perspective, tomato crops are highly sensitive to water availability and distribution, requiring precise irrigation management to ensure sustainable production and high-quality yields. In controlled environments such as... P. Guerra, A.R. Raucci, S.A. Gutierrez , J.F. Botero, C. Kamienski, F.M. Campos De Oliveira Download paper

Filter results3 paper(s) found.

1. Machine Learning Pipeline to Estimate Soybean Rust Severity Using UAV-derived Multispectral Indices

Asian Soybean Rust is one of the most destructive diseases affecting soybean crops worldwide and can result in yield losses of up to 90% when control measures are not implemented in a timely manner. Conventional disease monitoring based on field scouting is time-consuming, labor-intensive, and inherently subjective, often failing to adequately represent the spatial variability of disease across production fields. These limitations highlight the need for automated, objective, and high throughput... S.A. Teixeira, R. Valdivino, R. Tsukahara, M. Ribeiro

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2. The Agronomic and Bioeconomic Aspects of Site-Specific Seeding Rates and Depths for Winter Wheat in Lithuania

Precision seeding is one of the most important agrotechnological solutions for smart agriculture. It exploits the variability of soil properties in the field to increase the agronomic and economic efficiency of crops. This study investigated the impact of site-specific seeding (SSS) on the yield and productivity parameters of winter wheat in Lithuania, as well as its economic benefits, compared with conventional uniform rate seeding (URS). Experiments were conducted in a field divided into five... Z. Kriauciuniene, M. Kazlauskas, K. Romaneckas, S. Buragiene, I. Bručienė, E. Šarauskis

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3. Soil-Sensing-Based Irrigation Decision Modeling for Greenhouse Tomato Crops Using Machine Learning

Global agriculture faces increasing pressure to optimize water-use efficiency, particularly for high-demand crops like tomato (Solanum lycopersicum). Tomato is among the most widely consumed vegetables worldwide, playing a central role in global food systems. From an agronomic perspective, tomato crops are highly sensitive to water availability and distribution, requiring precise irrigation management to ensure sustainable production and high-quality yields. In controlled environments such as... P. Guerra, A.R. Raucci, S.A. Gutierrez , J.F. Botero, C. Kamienski, F.M. Campos De Oliveira

Download paper