Proceedings :: International Society of Precision Agriculture

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Anand, A

Alkmim, A

Amado, T

Alegre, J.

Alves, S

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Authors

Sundaravadivel, P

Manjunatha, H

Borah, S

Anand, A

Price, A

Torbert, H

Tamil, L

Stroud, T

Favan , J.R

Faulin, G.D

Kasita Kashima, F.M

Alegre, J.

Gonçalves, L.S

Alves, S

da Silva, E.C

Marchioretto, L.D

Gebler, L

Topics

UAV-Based Scouting, Imaging, and Targeted Applications

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Year

2026

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Filter results3 paper(s) found.
1. Agronomist-in-the-Loop Semantic 3D Reconstruction of Cotton Boll Morphology from UAV Imagery for Precision Agriculture Standard aerial photogrammetry is failing precision agriculture in one specific area: the detailed morphological assessment of complex, cluttered canopies. While creating a field-level map is trivial, recovering the geometry of a single cotton boll from a drone altitude of 30 meters is often mathematically intractable for standard Structure-from-Motion (SfM) solvers. These traditional pipelines depend on pixel-perfect consistency, which breaks down amidst... P. Sundaravadivel, H. Manjunatha, S. Borah, A. Anand, A. Price, H. Torbert, L. Tamil, T. Stroud Download paper
2. Soil Texture Classification by Image: Deep Feature Learning vs. Handcrafted Methods for Precision Agriculture Accurate soil texture classification is fundamental for precision agriculture, as it enables site-specific crop management that optimizes the utilization of agricultural resources and enhances overall crop productivity. This study presents a comparative analysis between features automatically extracted by a pre-trained SqueezeNet convolutional neural network (CNN) and three classical methods for manual feature extraction: Fast Fourier Transform (FFT), Gabor Filters, and Local Binary Patterns (LBP),... J.R. Favan , G.D. Faulin, F.M. kasita kashima, J. . Alegre, L.S. Gonçalves Download paper
3. Automated Detection of European Canker (Neonectria ditissima) in Apple Trees via Multispectral Sensors and Computer Vision European canker, caused by the fungus Neonectria ditissima, represents one of the major economic challenges for Brazilian pomiculture, severely affecting ‘Gala’ and ‘Fuji’ cultivars. The disease manifests primarily in woody tissues, such as trunks and branches, although it can also cause fruit rot during the pre-harvest stage. Infection occurs obligatorily through wounds, whether natural (leaf scars) or resulting from management practices (pruning and harvesting).... S. Alves, E.C. Da silva, L.D. Marchioretto, L. Gebler Download paper

Filter results3 paper(s) found.

1. Agronomist-in-the-Loop Semantic 3D Reconstruction of Cotton Boll Morphology from UAV Imagery for Precision Agriculture

Standard aerial photogrammetry is failing precision agriculture in one specific area: the detailed morphological assessment of complex, cluttered canopies. While creating a field-level map is trivial, recovering the geometry of a single cotton boll from a drone altitude of 30 meters is often mathematically intractable for standard Structure-from-Motion (SfM) solvers. These traditional pipelines depend on pixel-perfect consistency, which breaks down amidst... P. Sundaravadivel, H. Manjunatha, S. Borah, A. Anand, A. Price, H. Torbert, L. Tamil, T. Stroud

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2. Soil Texture Classification by Image: Deep Feature Learning vs. Handcrafted Methods for Precision Agriculture

Accurate soil texture classification is fundamental for precision agriculture, as it enables site-specific crop management that optimizes the utilization of agricultural resources and enhances overall crop productivity. This study presents a comparative analysis between features automatically extracted by a pre-trained SqueezeNet convolutional neural network (CNN) and three classical methods for manual feature extraction: Fast Fourier Transform (FFT), Gabor Filters, and Local Binary Patterns (LBP),... J.R. Favan , G.D. Faulin, F.M. kasita kashima, J. . Alegre, L.S. Gonçalves

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3. Automated Detection of European Canker (Neonectria ditissima) in Apple Trees via Multispectral Sensors and Computer Vision

European canker, caused by the fungus Neonectria ditissima, represents one of the major economic challenges for Brazilian pomiculture, severely affecting ‘Gala’ and ‘Fuji’ cultivars. The disease manifests primarily in woody tissues, such as trunks and branches, although it can also cause fruit rot during the pre-harvest stage. Infection occurs obligatorily through wounds, whether natural (leaf scars) or resulting from management practices (pruning and harvesting).... S. Alves, E.C. Da silva, L.D. Marchioretto, L. Gebler

Download paper