Proceedings :: International Society of Precision Agriculture

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Maess, W

M. Santos, A

Novaes da Silva, A

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Authors

Delgado Bejarano, L

, B

Novaes da Silva, A

Amaral, L.R

H. S. Sousa , F

S. Maciel, T

M. dos Reis, M

Santos, R.D

M. Santos, A

M. S. de Souza, A

K. F. Veras, A

G. Ferreira, G

P. M. Nunes, M

C. R. Seruffo, M

C. C. Daher, L

G.M. Silva, A

Maess, W

Shirtliffe, S

Nketia, K

Topics

Site-Specific Nutrient, Lime and Seed Management

UAV-Based Scouting, Imaging, and Targeted Applications

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Oral

Poster

Year

2026

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Filter results3 paper(s) found.
1. Economic Assessment of Soil Sampling Densities for Variable-Rate Fertilizer Prescription Soil sampling and laboratory analysis represent a substantial share of operational costs in precision agriculture, making sampling density a crucial management parameter. Sampling density defines the resolution at which soil spatial variability is captured and directly influences the accuracy of spatial interpolation and fertilizer prescription maps. Due to high operational costs, reduced sampling densities are commonly adopted in practice, despite their known effects on map quality. This study... L. Delgado Bejarano, B. , A. Novaes Da Silva, L.R. Amaral
2. Development and Field Validation of a Scalable UAV-Based Framework for Automated Cattle Counting and Herd Management in Extensive Production Systems Brazil holds the largest commercial cattle herd in the world, with more than 230 million head, representing approximately 20% of the global population. In this context, technologies capable of optimizing herd monitoring are strategic for increasing production efficiency, reducing operational costs, and promoting sustainability in livestock systems. Among these technologies, computer vision–based systems have emerged as a promising alternative for automated animal detection and counting in... F. H. S. Sousa , T. S. Maciel, M. M. Dos Reis, R.D. Santos, A. M. Santos, A. M. S. De Souza, A. K. F. Veras, G. G. Ferreira, M. P. M. Nunes, M. C. R. Seruffo, L. C. C. Daher, A. G.m. Silva
3. Upscaling UAV Image-Trained Machine Learning Models from Research Plots to Commercially Cropped Land High-throughput plant phenotyping (HTPP) leverages the advancement of unmanned aerial vehicles (UAVs) technology, paired with improvement in spectral sensing technology to allow for the derivation of plant phenotypic traits from image analysis. Crop breeding programs continue to increase incorporation of HTTP methods into their pipelines to enhance their efficiency of selecting for varieties. Machine learning (ML) models, often used hand in hand with HTTP methods, generate phenotypic trait predictions... W. Maess, S. Shirtliffe, K. Nketia Download paper

Filter results3 paper(s) found.

1. Economic Assessment of Soil Sampling Densities for Variable-Rate Fertilizer Prescription

Soil sampling and laboratory analysis represent a substantial share of operational costs in precision agriculture, making sampling density a crucial management parameter. Sampling density defines the resolution at which soil spatial variability is captured and directly influences the accuracy of spatial interpolation and fertilizer prescription maps. Due to high operational costs, reduced sampling densities are commonly adopted in practice, despite their known effects on map quality. This study... L. Delgado Bejarano, B. , A. Novaes Da Silva, L.R. Amaral

2. Development and Field Validation of a Scalable UAV-Based Framework for Automated Cattle Counting and Herd Management in Extensive Production Systems

Brazil holds the largest commercial cattle herd in the world, with more than 230 million head, representing approximately 20% of the global population. In this context, technologies capable of optimizing herd monitoring are strategic for increasing production efficiency, reducing operational costs, and promoting sustainability in livestock systems. Among these technologies, computer vision–based systems have emerged as a promising alternative for automated animal detection and counting in... F. H. S. Sousa , T. S. Maciel, M. M. Dos Reis, R.D. Santos, A. M. Santos, A. M. S. De Souza, A. K. F. Veras, G. G. Ferreira, M. P. M. Nunes, M. C. R. Seruffo, L. C. C. Daher, A. G.m. Silva

3. Upscaling UAV Image-Trained Machine Learning Models from Research Plots to Commercially Cropped Land

High-throughput plant phenotyping (HTPP) leverages the advancement of unmanned aerial vehicles (UAVs) technology, paired with improvement in spectral sensing technology to allow for the derivation of plant phenotypic traits from image analysis. Crop breeding programs continue to increase incorporation of HTTP methods into their pipelines to enhance their efficiency of selecting for varieties. Machine learning (ML) models, often used hand in hand with HTTP methods, generate phenotypic trait predictions... W. Maess, S. Shirtliffe, K. Nketia

Download paper