Proceedings :: International Society of Precision Agriculture

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Langemeier, M

Leite, D.H

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Authors

Colussi, J

Erickson, B

Malone, T

Langemeier, M

Fiechter, C

Leite, D.H

Valente, D.S

Arruda, P.M

Tancredi, F.D

Queiroz, D

Dumbá Monteiro de Castro, G

Queiroz, D

Leite, D.H

Sárvio Valente, D

Dumbá Monteiro de Castro, G

Marin, D.B

Topics

Drivers and Barriers to Adoption of Precision and Digital Technologies

Decision Support Systems, Cloud Platforms, and Open Data Solutions

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Oral

Poster

Year

2026

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Filter results3 paper(s) found.
1. Benchmarking Precision Agriculture Adoption in the United States and Brazil The United States has long been regarded as a global leader in agricultural technology and productivity. However, rapid advancements in other major producing countries are challenging this position. Brazil, in particular, has paired large-scale crop expansion with accelerated digital transformation, raising important questions about where the United States continues to lead and where it risks losing its competitive edge. Understanding how precision agriculture technologies are being adopted and... J. Colussi, B. Erickson, T. Malone, M. Langemeier, C. Fiechter Download paper
2. Web Application Based on CNN for Classification of Biotic and Abiotic Stresses in Coffee Leaves The use of digital systems can assist coffee growers and professionals in diagnosing stresses that affect coffee plantations, ensuring that crop management is carried out correctly and efficiently. Therefore, the aim of this study was to develop a web application based on a pre-trained Convolutional Neural Network to classify coffee leaf images exhibiting symptoms of biotic and abiotic stresses. Initially, a dataset consisting of coffee leaf images affected by biotic and abiotic stresses was constructed.... D.H. Leite, D.S. Valente, P.M. Arruda, F.D. Tancredi, D. Queiroz, G. Dumbá Monteiro De Castro Download paper
3. Plant-Level Coffee Production Estimation Based on Morphological Indices Production estimation in coffee farming is traditionally conducted at aggregated spatial scales, which often limits the characterization of variability among individual plants and constrains its applicability for precision-oriented management. In production systems where within-field heterogeneity affects decisions related to harvesting, logistics, and crop management, approaches capable of representing plant-level variability become particularly relevant. Within this context, this study proposes... D. Queiroz, D.H. Leite, D. Sárvio Valente, G. Dumbá Monteiro De Castro, D.B. Marin Download paper

Filter results3 paper(s) found.

1. Benchmarking Precision Agriculture Adoption in the United States and Brazil

The United States has long been regarded as a global leader in agricultural technology and productivity. However, rapid advancements in other major producing countries are challenging this position. Brazil, in particular, has paired large-scale crop expansion with accelerated digital transformation, raising important questions about where the United States continues to lead and where it risks losing its competitive edge. Understanding how precision agriculture technologies are being adopted and... J. Colussi, B. Erickson, T. Malone, M. Langemeier, C. Fiechter

Download paper

2. Web Application Based on CNN for Classification of Biotic and Abiotic Stresses in Coffee Leaves

The use of digital systems can assist coffee growers and professionals in diagnosing stresses that affect coffee plantations, ensuring that crop management is carried out correctly and efficiently. Therefore, the aim of this study was to develop a web application based on a pre-trained Convolutional Neural Network to classify coffee leaf images exhibiting symptoms of biotic and abiotic stresses. Initially, a dataset consisting of coffee leaf images affected by biotic and abiotic stresses was constructed.... D.H. Leite, D.S. Valente, P.M. Arruda, F.D. Tancredi, D. Queiroz, G. Dumbá Monteiro De Castro

Download paper

3. Plant-Level Coffee Production Estimation Based on Morphological Indices

Production estimation in coffee farming is traditionally conducted at aggregated spatial scales, which often limits the characterization of variability among individual plants and constrains its applicability for precision-oriented management. In production systems where within-field heterogeneity affects decisions related to harvesting, logistics, and crop management, approaches capable of representing plant-level variability become particularly relevant. Within this context, this study proposes... D. Queiroz, D.H. Leite, D. Sárvio Valente, G. Dumbá Monteiro De Castro, D.B. Marin

Download paper