Proceedings :: International Society of Precision Agriculture

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Rubio, J.F

Rodolfo, T.A

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Authors

Rodolfo, T.A

Gonzalez Zarate, O.J

Gonzalez Aguilera, C

González Zarate, O.J

Macea Zabaleta, L

Castillo Ojeda, N

Flórez Olivera, A.F

Rodolfo, T.A

Gonzalez Aguilera, C

Rodolfo, T.A

Schneider, P.S

Perez, M.A

Mantovan, F.D

Bressan, H.R

Reginatto, A.C

Rubio, G.F

Rubio, J.F

Pereira, L.E

Marques, J.R

Tech, A.R

Logli, M.F

Rubio, G.F

Rubio, J.F

Pereira, L.E

Brandi, R.A

Tech, A.R

Irene, V

Silveira, D.M

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Wireless Sensor Networks, Edge Computing, and Farm Connectivity

UAV-Based Scouting, Imaging, and Targeted Applications

Type

Oral

Poster

Year

2026

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Filter results5 paper(s) found.
1. An Interpretable Machine Learning Framework for Soil Nutrient Assessment Based on pH and Electrical Conductivity Understanding how the physical and chemical properties of soil influence nutrient availability is fundamental for advancing precision agriculture, as these properties directly affect the efficiency of macro- and micronutrient absorption by plants. In recent years, the increasing availability of open agricultural datasets has created new opportunities for developing data-driven frameworks capable of supporting large-scale soil assessment and decision-making. However, the effective integration of... T.A. Rodolfo, O.J. Gonzalez Zarate, C. Gonzalez Aguilera Download paper
2. Detection of Maize Foliar Diseases Using AI Optimized for Deployment on Edge Devices Maize is a strategic crop for both regional and global food security. Its productivity is significantly affected by several foliar diseases, among which—common rust, gray leaf spot, and blight—are some of the most prevalent and damaging. These pathologies can cause substantial yield losses if not detected and treated in a timely manner, making early diagnosis a fundamental factor to ensure healthy and sustainable crop development. However, traditional diagnostic methods based on manual... O.J. González Zarate, L. Macea Zabaleta, N. Castillo Ojeda, A.F. Flórez Olivera, T.A. Rodolfo, C. Gonzalez Aguilera Download paper
3. An Integrated Water–Energy Vulnerability Index for Irrigated Agricultural Regions The growing interdependence between water availability and energy infrastructure has significantly increased the vulnerability of irrigated agricultural regions, particularly under conditions of climate variability, hydrological uncertainty, and seasonal demand peaks. Irrigated production systems simultaneously depend on reliable water supply and stable energy provision, making them particularly sensitive to disruptions in either domain. Although the water–energy nexus literature has advanced... T.A. Rodolfo, P.S. Schneider, M.A. Perez, F.D. Mantovan, H.R. Bressan, A.C. Reginatto Download paper
4. Application for Pixel-level Segmentation and Quantification of Lignified Fibers (Sclerenchyma) and Parenchyma in Microscopic Images of Sugarcane Culms Quantifying lignified tissues in sugarcane culms (Saccharum spp.) is essential for anatomical characterizations and for inferences related to biomass quality and the potential uses of plant material. Conventional methods may require specific laboratory procedures and manual steps in digital analysis, increasing processing time and reliance on skilled operators. In this context, computer vision techniques applied to microscopic images constitute an accessible and reproducible alternative,... G.F. Rubio, J.F. Rubio, L.E. Pereira, J.R. Marques, A.R. Tech, M.F. Logli Download paper
5. Detection of latrine areas in equine paddocks using drones and computer vision Equines can exhibit behaviors that are harmful to the soil, such as spatial segregation, which is caused by their selective grazing pattern. This species may choose its feeding areas based on vegetation structural characteristics, such as forage density, leaf availability, and stage of maturity (which are perceived through their tactile receptors). When present daily, this natural behavior can impair soil health, as spatial segregation within paddocks intensifies and latrine (dung) areas form....

Filter results5 paper(s) found.

1. An Interpretable Machine Learning Framework for Soil Nutrient Assessment Based on pH and Electrical Conductivity

Understanding how the physical and chemical properties of soil influence nutrient availability is fundamental for advancing precision agriculture, as these properties directly affect the efficiency of macro- and micronutrient absorption by plants. In recent years, the increasing availability of open agricultural datasets has created new opportunities for developing data-driven frameworks capable of supporting large-scale soil assessment and decision-making. However, the effective integration of... T.A. Rodolfo, O.J. Gonzalez Zarate, C. Gonzalez Aguilera

Download paper

2. Detection of Maize Foliar Diseases Using AI Optimized for Deployment on Edge Devices

Maize is a strategic crop for both regional and global food security. Its productivity is significantly affected by several foliar diseases, among which—common rust, gray leaf spot, and blight—are some of the most prevalent and damaging. These pathologies can cause substantial yield losses if not detected and treated in a timely manner, making early diagnosis a fundamental factor to ensure healthy and sustainable crop development. However, traditional diagnostic methods based on manual... O.J. González Zarate, L. Macea Zabaleta, N. Castillo Ojeda, A.F. Flórez Olivera, T.A. Rodolfo, C. Gonzalez Aguilera

Download paper

3. An Integrated Water–Energy Vulnerability Index for Irrigated Agricultural Regions

The growing interdependence between water availability and energy infrastructure has significantly increased the vulnerability of irrigated agricultural regions, particularly under conditions of climate variability, hydrological uncertainty, and seasonal demand peaks. Irrigated production systems simultaneously depend on reliable water supply and stable energy provision, making them particularly sensitive to disruptions in either domain. Although the water–energy nexus literature has advanced... T.A. Rodolfo, P.S. Schneider, M.A. Perez, F.D. Mantovan, H.R. Bressan, A.C. Reginatto

Download paper

4. Application for Pixel-level Segmentation and Quantification of Lignified Fibers (Sclerenchyma) and Parenchyma in Microscopic Images of Sugarcane Culms

Quantifying lignified tissues in sugarcane culms (Saccharum spp.) is essential for anatomical characterizations and for inferences related to biomass quality and the potential uses of plant material. Conventional methods may require specific laboratory procedures and manual steps in digital analysis, increasing processing time and reliance on skilled operators. In this context, computer vision techniques applied to microscopic images constitute an accessible and reproducible alternative,... G.F. Rubio, J.F. Rubio, L.E. Pereira, J.R. Marques, A.R. Tech, M.F. Logli

Download paper

5. Detection of latrine areas in equine paddocks using drones and computer vision

Equines can exhibit behaviors that are harmful to the soil, such as spatial segregation, which is caused by their selective grazing pattern. This species may choose its feeding areas based on vegetation structural characteristics, such as forage density, leaf availability, and stage of maturity (which are perceived through their tactile receptors). When present daily, this natural behavior can impair soil health, as spatial segregation within paddocks intensifies and latrine (dung) areas form....