Proceedings :: International Society of Precision Agriculture

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Bezerra, A.C

Bastos, L

Bender, E

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Authors

Zolin, P.

Peranzoni Deponti, L

Bastos, L

Amaral, L.R

Guerra Martins, C

Grando, D.L

Moura Bueno, J

Brunetto, G

Kokkonen, A.A

Peranzoni Deponti, L

Bastos, L

De Oliveira Vieira, I

Luns, S

Mendes, R.C

Bastos, L

Pereira da Silva, R.P

Melo, D.D

Amaral, L.R

Bastos, L

Virk, S

Figueiró, A.C

Nogueira, B

Bender, E

SILVA, R

Cassol, V.M

Silveira , S.J

Bredemeier, C

Vian, A.L

Fonseca, A

dos Anjos, J.F

da Silva, E.F

Moura, G.B

Figueirôa , E.D

Coelho, G.P

de Andrade, J.P

Lopes, E

Bezerra, A.C

Fonseca, A

da Silva, E.F

Bezerra, A.C

Moura, G.B

dos Anjos, J.F

Figueirôa , E.D

Coelho, G.P

de Andrade, J.P

Lopes, E

Silva, J.I

Nogueira, B

Figueiró, A.C

, A

Bender, E

Lima, C.D

Vian, A.L

Bredemeier, C

Jakhar, A

Bastos, L

Bhattarai, A

Poudel, K

Dhaliwal, A

Rorato, A

Zolin, P.

Scarpin, G.J

Deponti, L.P

Echer, F.R

Bastos, L

Jakhar, A

Bastos, L

Roth, R

Virk, S

Bhattarai, A

Poudel, K

Dhaliwal, A

Poudel, K

Bhattarai, A

Jakhar, A

Bastos, L

Dhaliwal, A

Bastos, L

SV, K

Bhattarai, A

Jakhar, A

Poudel, K

McCallister, D.M

Jaconis , S.Y

Dhaliwal, A

Bastos, L

SV, K

Bhattarai, A

Jakhar , A

Poudel, K

Scarpin , G.J

Puntel, L.A

Bastos, L

Topics

UAV-Based Scouting, Imaging, and Targeted Applications

Remote and Proximal Sensing of Soils and Crops

Weather, Climate Models, and Smart Forecasting for Agriculture

Site-Specific Nutrient, Lime and Seed Management

Decision Support Systems, Cloud Platforms, and Open Data Solutions

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Meetings

Type

Poster

Oral

Year

2026

Home » Authors » Results

Authors

Filter results15 paper(s) found.
1. Correcting LiDAR-based Plant Height Estimation Errors in Dense Cotton Canopies Cotton is a perennial plant grown as an annual crop and, if not properly managed, excessive vegetative growth may reduce yield, making the use of plant growth regulators (PGR) essential. In precision agriculture, spatial representation of PGR requirements depends on plant height measurements, which are typically labor-intensive. LiDAR sensors mounted on drones have been widely used to estimate plant height. However, under certain conditions, cotton plants can become highly vigorous, resulting... P. Zolin, L. Peranzoni Deponti, L. Bastos, L.R. Amaral
2. Evaluation of Diffuse Reflectance Spectroscopy and Machine Learning Methods for Soil Available Phosphorus and Potassium Prediction Phosphorus (P) and potassium (K) are essential elements for plants. Accurate evaluation of soil available P and K contents is fundamental for precision agriculture and site-specific nutrient management. However, traditional chemical analyses are time-consuming, labor-intensive, and costly. In this context, diffuse reflectance spectroscopy (DRS) has been introduced as a cheaper and rapid alternative; however, its accuracy in estimating soil P and K contents has not been fully proven, particularly... C. Guerra Martins, D.L. Grando, J. Moura Bueno, G. Brunetto, A.A. Kokkonen, L. Peranzoni Deponti, L. Bastos
3. Agroclimatic and Topographic Zoning for the Sustainable Expansion of Peanut Production in the State of Georgia, USA Sustainable agricultural production depends on a detailed analysis of environmental conditions to support decision-making. This study aimed to develop a topoclimatic zoning for peanut production in Georgia, USA, using climatic data from the PRISM Climate Group and topographic data from OpenTopography. The water deficit was calculated using the Thornthwaite and Mather methodology. The methodology included the reclassification of variables into three suitability classes for cultivation, based on... I. De Oliveira Vieira, S. Luns, R.C. Mendes, L. Bastos, R.P. Silva
4. Effect of Post-processing on the Performance of Clustering Algorithms for Delineating Management Zones for Precision Soil Sampling Soil nutrient variability directly influences crop yield; therefore, site-specific management requires maps that can depict variability within the fields. Here, management zone (MZ) approaches have been used, typically derived from clustering analyses applied to low-cost environmental variables. There is still no consensus on zone delineation strategies that maximize the reduction of soil variability, nor on the relative performance of clustering algorithms. Moreover, post-processing of... D.D. Melo, L.R. Amaral, L. Bastos, S. Virk
5. 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
6. Influence of Meteorological Variables on Bean Yield in the Semi-Arid Region: A Data-Driven Approach for Agricultural Decision Support Common bean is a strategic crop for the Brazilian semi-arid region, predominantly cultivated under rainfed systems that are highly dependent on climate variability. In regions characterized by irregular rainfall patterns, high temperatures, and extreme weather events, incorporating temporal analyses based on meteorological data becomes essential for evidence-based agricultural planning. Within the context of precision agriculture, the integration of historical climate series and productivity indicators... A. Fonseca, J.F. Dos Anjos, E.F. Da Silva, G.B. Moura, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, A.C. Bezerra Download paper
7. Nonlinear Modeling of Vegetation Response to Rainfall Variability in the Brazilian Semi-Arid Region Using Sentinel-2 and CHIRPS Data High climate variability in the Brazilian semi-arid region poses significant challenges to agriculture and the sustainable management of Caatinga ecosystems, requiring monitoring tools capable of anticipating vegetation responses to rainfall fluctuations. However, the spectral response of vegetation to precipitation does not always follow linear patterns and may reflect ecohydrological thresholds and water saturation effects. Sentinel-2 data were used to derive the Soil Adjusted Vegetation Index... A. Fonseca, E.F. Da Silva, A.C. Bezerra, G.B. Moura, J.F. Dos Anjos, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, J.I. Silva Download paper
8. Estimation of Agronomic Parameters in Maize Using UAV-based Vegetation Indices Obtained by a Multispectral Sensor Precision agriculture emerges as a response to optimize input use and to monitor crop spatial variability over time. In this context, the use of vegetation indices obtained by optical sensors embedded in drones or satellites has become a useful tool for predicting agronomic parameters in maize fields, such as aboveground dry biomass, leaf chlorophyll content, and grain yield. Thus, the objective of this study was to correlate field agronomic parameters with vegetation indexes obtained by a multispectral... B. Nogueira, A.C. Figueiró, A. , E. Bender, C.D. Lima, A.L. Vian, C. Bredemeier
9. Sensor-based Variable Rate Nitrogen Recommendations: Comparing Proximal, Drone, and Satellite Sensors in Corn Nitrogen (N) represents 20–25% of corn (Zea mays L.) production costs, yet 15–65% is lost through volatilization and leaching. Conventional uniform-rate application ignores spatial variability and seasonal demand. Sensor-based variable rate nitrogen (VRN) addresses this by using real-time reflectance data, but the influence of sensing platform proximal, drone, or satellite on economic outcomes under varying N stress remains under-researched. The objective of this study at Iron Horse,... A. Jakhar, L. Bastos, A. Bhattarai, K. Poudel, A. Dhaliwal
10. Sensor-based plant growth regulator management in cotton: plot-level and within-plant yield distribution Cotton yield is distributed among canopy thirds, and the use of plant growth regulators (PGRs) modulate this balance, affecting fruiting and yield. Drone-mounted sensors can be used to estimate plant growth and generate maps for variable rate PGR applications to support management. This study compared traditional PGR management with fixed timing and rate to sensor-based management by evaluating PGR application rate and timing. Within-plant yield distribution... A. Rorato, P. . Zolin, G.J. Scarpin, L.P. Deponti, F.R. Echer, L. Bastos Download paper
11. A Canopy-based Decision Framework for Selecting Sensor Platform and Vegetation Index in Variable-rate Nitrogen Management of Irrigated Corn Sensor-based variable-rate nitrogen (VRN) management promises field-specific N optimization, yet the choice of sensing platform fundamentally alters N recommendations. At early growth stages, a "double penalty" emerges: nitrogen-deficient plants produce smaller canopies, exposing more bare soil, which deflates vegetation index (VI) values and inflates N recommendations where accuracy matters most. This study developed a canopy coverage-based decision framework for selecting optimal sensor... A. Jakhar, L. Bastos, R. Roth, S. Virk, A. Bhattarai, K. Poudel, A. Dhaliwal
12. Predicting Yield Stability Classes Using Satellite Imagery in the Absence of Yield Monitor Data Site-specific management is essential for improving agricultural productivity while reducing input costs and minimizing environmental impacts. Although yield monitor data are commonly used to characterize within-field yield variability, their availability is often limited by technological and economic constraints. The primary objective of this study was to compare spatial–temporal stability classes derived from yield monitor data and satellite imagery in cotton production... K. Poudel, A. Bhattarai, A. Jakhar, L. Bastos, A. Dhaliwal
13. Main Environmental and Variety Drivers of Cotton Seed Quality: Historical Insights from the United States Cotton Belt  Cotton seed quality traits including oil content, nitrogen (protein), and gossypol significantly influence seed value and end-use applications, yet their predictability based on environmental conditions across varied U.S. growing regions remains poorly understood. This study aimed to: (i) identify critical environmental predictors of seed composition; (ii) build machine learning models to predict seed quality as a function of seasonal weather patterns; and (iii) assess differences... A. Dhaliwal, L. Bastos, K. Sv, A. Bhattarai, A. Jakhar, K. Poudel, D.M. Mccallister, S.Y. Jaconis
14. Predicting Pre-harvest Cotton Fiber Quality: An Open-data and Machine Learning Framework Intra-field variability in soil properties and topography, and inter-field variability in weather patterns often leads to inconsistent cotton fiber quality and yield outcomes, posing challenges for growers. This study aims to: (i) predict within-field cotton fiber quality traits based on environmental and soil variables using machine learning models; (ii) identify the most influential environmental drivers (weather, vegetation indices, soil properties, terrain characteristics) affecting cotton...
15. ISPA Nitrogen Management Community ... L.A. Puntel, L. Bastos

Filter results15 paper(s) found.

1. Correcting LiDAR-based Plant Height Estimation Errors in Dense Cotton Canopies

Cotton is a perennial plant grown as an annual crop and, if not properly managed, excessive vegetative growth may reduce yield, making the use of plant growth regulators (PGR) essential. In precision agriculture, spatial representation of PGR requirements depends on plant height measurements, which are typically labor-intensive. LiDAR sensors mounted on drones have been widely used to estimate plant height. However, under certain conditions, cotton plants can become highly vigorous, resulting... P. Zolin, L. Peranzoni Deponti, L. Bastos, L.R. Amaral

2. Evaluation of Diffuse Reflectance Spectroscopy and Machine Learning Methods for Soil Available Phosphorus and Potassium Prediction

Phosphorus (P) and potassium (K) are essential elements for plants. Accurate evaluation of soil available P and K contents is fundamental for precision agriculture and site-specific nutrient management. However, traditional chemical analyses are time-consuming, labor-intensive, and costly. In this context, diffuse reflectance spectroscopy (DRS) has been introduced as a cheaper and rapid alternative; however, its accuracy in estimating soil P and K contents has not been fully proven, particularly... C. Guerra Martins, D.L. Grando, J. Moura Bueno, G. Brunetto, A.A. Kokkonen, L. Peranzoni Deponti, L. Bastos

3. Agroclimatic and Topographic Zoning for the Sustainable Expansion of Peanut Production in the State of Georgia, USA

Sustainable agricultural production depends on a detailed analysis of environmental conditions to support decision-making. This study aimed to develop a topoclimatic zoning for peanut production in Georgia, USA, using climatic data from the PRISM Climate Group and topographic data from OpenTopography. The water deficit was calculated using the Thornthwaite and Mather methodology. The methodology included the reclassification of variables into three suitability classes for cultivation, based on... I. De Oliveira Vieira, S. Luns, R.C. Mendes, L. Bastos, R.P. Silva

4. Effect of Post-processing on the Performance of Clustering Algorithms for Delineating Management Zones for Precision Soil Sampling

Soil nutrient variability directly influences crop yield; therefore, site-specific management requires maps that can depict variability within the fields. Here, management zone (MZ) approaches have been used, typically derived from clustering analyses applied to low-cost environmental variables. There is still no consensus on zone delineation strategies that maximize the reduction of soil variability, nor on the relative performance of clustering algorithms. Moreover, post-processing of... D.D. Melo, L.R. Amaral, L. Bastos, S. Virk

5. 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

6. Influence of Meteorological Variables on Bean Yield in the Semi-Arid Region: A Data-Driven Approach for Agricultural Decision Support

Common bean is a strategic crop for the Brazilian semi-arid region, predominantly cultivated under rainfed systems that are highly dependent on climate variability. In regions characterized by irregular rainfall patterns, high temperatures, and extreme weather events, incorporating temporal analyses based on meteorological data becomes essential for evidence-based agricultural planning. Within the context of precision agriculture, the integration of historical climate series and productivity indicators... A. Fonseca, J.F. Dos Anjos, E.F. Da Silva, G.B. Moura, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, A.C. Bezerra

Download paper

7. Nonlinear Modeling of Vegetation Response to Rainfall Variability in the Brazilian Semi-Arid Region Using Sentinel-2 and CHIRPS Data

High climate variability in the Brazilian semi-arid region poses significant challenges to agriculture and the sustainable management of Caatinga ecosystems, requiring monitoring tools capable of anticipating vegetation responses to rainfall fluctuations. However, the spectral response of vegetation to precipitation does not always follow linear patterns and may reflect ecohydrological thresholds and water saturation effects. Sentinel-2 data were used to derive the Soil Adjusted Vegetation Index... A. Fonseca, E.F. Da Silva, A.C. Bezerra, G.B. Moura, J.F. Dos Anjos, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, J.I. Silva

Download paper

8. Estimation of Agronomic Parameters in Maize Using UAV-based Vegetation Indices Obtained by a Multispectral Sensor

Precision agriculture emerges as a response to optimize input use and to monitor crop spatial variability over time. In this context, the use of vegetation indices obtained by optical sensors embedded in drones or satellites has become a useful tool for predicting agronomic parameters in maize fields, such as aboveground dry biomass, leaf chlorophyll content, and grain yield. Thus, the objective of this study was to correlate field agronomic parameters with vegetation indexes obtained by a multispectral... B. Nogueira, A.C. Figueiró, A. , E. Bender, C.D. Lima, A.L. Vian, C. Bredemeier

9. Sensor-based Variable Rate Nitrogen Recommendations: Comparing Proximal, Drone, and Satellite Sensors in Corn

Nitrogen (N) represents 20–25% of corn (Zea mays L.) production costs, yet 15–65% is lost through volatilization and leaching. Conventional uniform-rate application ignores spatial variability and seasonal demand. Sensor-based variable rate nitrogen (VRN) addresses this by using real-time reflectance data, but the influence of sensing platform proximal, drone, or satellite on economic outcomes under varying N stress remains under-researched. The objective of this study at Iron Horse,... A. Jakhar, L. Bastos, A. Bhattarai, K. Poudel, A. Dhaliwal

10. Sensor-based plant growth regulator management in cotton: plot-level and within-plant yield distribution

Cotton yield is distributed among canopy thirds, and the use of plant growth regulators (PGRs) modulate this balance, affecting fruiting and yield. Drone-mounted sensors can be used to estimate plant growth and generate maps for variable rate PGR applications to support management. This study compared traditional PGR management with fixed timing and rate to sensor-based management by evaluating PGR application rate and timing. Within-plant yield distribution... A. Rorato, P. . Zolin, G.J. Scarpin, L.P. Deponti, F.R. Echer, L. Bastos

Download paper

11. A Canopy-based Decision Framework for Selecting Sensor Platform and Vegetation Index in Variable-rate Nitrogen Management of Irrigated Corn

Sensor-based variable-rate nitrogen (VRN) management promises field-specific N optimization, yet the choice of sensing platform fundamentally alters N recommendations. At early growth stages, a "double penalty" emerges: nitrogen-deficient plants produce smaller canopies, exposing more bare soil, which deflates vegetation index (VI) values and inflates N recommendations where accuracy matters most. This study developed a canopy coverage-based decision framework for selecting optimal sensor... A. Jakhar, L. Bastos, R. Roth, S. Virk, A. Bhattarai, K. Poudel, A. Dhaliwal

12. Predicting Yield Stability Classes Using Satellite Imagery in the Absence of Yield Monitor Data

Site-specific management is essential for improving agricultural productivity while reducing input costs and minimizing environmental impacts. Although yield monitor data are commonly used to characterize within-field yield variability, their availability is often limited by technological and economic constraints. The primary objective of this study was to compare spatial–temporal stability classes derived from yield monitor data and satellite imagery in cotton production... K. Poudel, A. Bhattarai, A. Jakhar, L. Bastos, A. Dhaliwal

13. Main Environmental and Variety Drivers of Cotton Seed Quality: Historical Insights from the United States Cotton Belt 

Cotton seed quality traits including oil content, nitrogen (protein), and gossypol significantly influence seed value and end-use applications, yet their predictability based on environmental conditions across varied U.S. growing regions remains poorly understood. This study aimed to: (i) identify critical environmental predictors of seed composition; (ii) build machine learning models to predict seed quality as a function of seasonal weather patterns; and (iii) assess differences... A. Dhaliwal, L. Bastos, K. Sv, A. Bhattarai, A. Jakhar, K. Poudel, D.M. Mccallister, S.Y. Jaconis

14. Predicting Pre-harvest Cotton Fiber Quality: An Open-data and Machine Learning Framework

Intra-field variability in soil properties and topography, and inter-field variability in weather patterns often leads to inconsistent cotton fiber quality and yield outcomes, posing challenges for growers. This study aims to: (i) predict within-field cotton fiber quality traits based on environmental and soil variables using machine learning models; (ii) identify the most influential environmental drivers (weather, vegetation indices, soil properties, terrain characteristics) affecting cotton...

15. ISPA Nitrogen Management Community

... L.A. Puntel, L. Bastos