Proceedings :: International Society of Precision Agriculture

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Castillo Ojeda, N

Conejo Rodriguez, D.F

Cassol, V.M

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Authors

González Zarate, O.J

Macea Zabaleta, L

Castillo Ojeda, N

Flórez Olivera, A.F

Rodolfo, T.A

Gonzalez Aguilera, C

Figueiró, A.C

Nogueira, B

Bender, E

SILVA, R

Cassol, V.M

Silveira , S.J

Bredemeier, C

Vian, A.L

Rayo Álvarez, D

Murillo Sandoval, P.J

Darghan Contreras, A.E

Conejo Rodriguez, D.F

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Remote and Proximal Sensing of Soils and Crops

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Oral

Poster

Year

2026

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Authors

Filter results3 paper(s) found.
1. 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
2. Importance of Irradiance Correction for UAV-based Vegetation Indices in the Prediction of Shoot Biomass in Wheat The extraction of vegetation indices from multispectral images obtained with UAV-bsed sensors for biomass estimation has proven to be a useful tool for designing site-specific interventions on wheat. The reflectance values from monochromatic bands collected with optical sensors for calculating vegetation indices should have high reliability and repeatability in the case of successive assessments under different illumination conditions. In this context, the objective of the study was to characterize... A.C. Figueiró, B. Nogueira, E. Bender, R. Silva, V.M. Cassol, S.J. Silveira , C. Bredemeier, A.L. Vian Download paper
3. Early Yield Estimation in Hass Avocado Using Ecophysiological Variables and Machine Learning This study evaluated the ability of machine learning models to estimate yield in mature Hass avocado trees (>5 years), using ecophysiological variables measured with MultispeQ v2.0 (RIDES 2.1 protocol) and electrical capacitance (1 Hz). The study was conducted at Pan de Azúcar farm (Villahermosa, Tolima, Colombia; 1,565 m a.s.l., Andisols) on 60 trees, with data collected across four phenological stages (fruit development, fruit maturation, leaf and shoot growth, and pre-flowering) and... D. Rayo Álvarez, P.J. Murillo Sandoval, A.E. Darghan Contreras, D.F. Conejo Rodriguez

Filter results3 paper(s) found.

1. 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

2. Importance of Irradiance Correction for UAV-based Vegetation Indices in the Prediction of Shoot Biomass in Wheat

The extraction of vegetation indices from multispectral images obtained with UAV-bsed sensors for biomass estimation has proven to be a useful tool for designing site-specific interventions on wheat. The reflectance values from monochromatic bands collected with optical sensors for calculating vegetation indices should have high reliability and repeatability in the case of successive assessments under different illumination conditions. In this context, the objective of the study was to characterize... A.C. Figueiró, B. Nogueira, E. Bender, R. Silva, V.M. Cassol, S.J. Silveira , C. Bredemeier, A.L. Vian

Download paper

3. Early Yield Estimation in Hass Avocado Using Ecophysiological Variables and Machine Learning

This study evaluated the ability of machine learning models to estimate yield in mature Hass avocado trees (>5 years), using ecophysiological variables measured with MultispeQ v2.0 (RIDES 2.1 protocol) and electrical capacitance (1 Hz). The study was conducted at Pan de Azúcar farm (Villahermosa, Tolima, Colombia; 1,565 m a.s.l., Andisols) on 60 trees, with data collected across four phenological stages (fruit development, fruit maturation, leaf and shoot growth, and pre-flowering) and... D. Rayo Álvarez, P.J. Murillo Sandoval, A.E. Darghan Contreras, D.F. Conejo Rodriguez