Proceedings :: International Society of Precision Agriculture

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Guimarães, C

Gonzalez Zarate, O.J

Gnyp, M

Genkin, K

GUERRA, P

Gentili, M

Gonçalves, L.S

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Authors

Katz, L

Genkin, K

Landau, A

Ness, Y

Crane, O

Golan, R

Rotbart, N

Nevo, E

Shapira, O

Fereres, E

Reichmann, O

Brook, A

Katz, L

Snir, N

Genkin, K

Rotbart, N

Nevo, E

Ronen, N

Reichmann, O

Rodolfo, T.A

Gonzalez Zarate, O.J

Gonzalez Aguilera, C

GUERRA, P

Raucci, A.R

Gutierrez , S.A

Botero, J.F

Kamienski, C

Campos de Oliveira, F.M

Favan , J.R

Faulin, G.D

Kasita Kashima, F.M

Alegre, J.

Gonçalves, L.S

Fontena, V

Wagner, N.K

Teixeira, C

Lopes, J

Moura, L

Guimarães, C

Topics

Remote and Proximal Sensing of Soils and Crops

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Variable-Rate Irrigation, Drainage Optimization, and Water Management

Agricultural Robotics, Automation, and Mechanization

Type

Oral

Poster

Year

2026

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Authors

Filter results6 paper(s) found.
1. From Pocket to Pixels: Smartphone-Based Proximal Sensing for Quantitative Monitoring of Peach Canopy Dynamics Monitoring peach canopy dynamics is essential for optimizing irrigation scheduling and promoting sustainable water management in intensively managed orchards. In well-watered systems, conventional irrigation practices often overlook spatial and temporal variability in canopy size and leaf area index (LAI), potentially leading to inefficient water use. Canopy size, as reflected by LAI—the ratio of total leaf area to ground area—serves as a key biophysical indicator linked to crop transpiration... L. Katz, K. Genkin, A. Landau, Y. Ness, O. Crane, R. Golan, N. Rotbart, E. Nevo, O. Shapira, E. Fereres, O. Reichmann, A. Brook Download paper
2. Smartphone-Based RGB Phenotyping of Hydroponic Lettuce Growth Dynamics Hydroponic lettuce production requires frequent, accurate growth assessment to optimize yield, nutrient efficiency, and crop uniformity within short, nutrient‑sensitive cycles. Existing imaging systems can deliver such precision but are often costly or technically demanding, limiting adoption in smaller hydroponic operations. Smartphone‑based imaging offers a practical alternative for scalable proximal phenotyping, yet its cross‑device quantitative accuracy and performance under operational... L. Katz, N. Snir, K. Genkin, N. Rotbart, E. Nevo, N. Ronen, O. Reichmann Download paper
3. An Interpretable Machine Learning Framework for Soil Nutrient Assessment Based on pH and Electrical Conductivity Understanding how the physical and chemical properties of soil influence nutrient availability is fundamental for advancing precision agriculture, as these properties directly affect the efficiency of macro- and micronutrient absorption by plants. In recent years, the increasing availability of open agricultural datasets has created new opportunities for developing data-driven frameworks capable of supporting large-scale soil assessment and decision-making. However, the effective integration of... T.A. Rodolfo, O.J. Gonzalez Zarate, C. Gonzalez Aguilera Download paper
4. Soil-Sensing-Based Irrigation Decision Modeling for Greenhouse Tomato Crops Using Machine Learning Global agriculture faces increasing pressure to optimize water-use efficiency, particularly for high-demand crops like tomato (Solanum lycopersicum). Tomato is among the most widely consumed vegetables worldwide, playing a central role in global food systems. From an agronomic perspective, tomato crops are highly sensitive to water availability and distribution, requiring precise irrigation management to ensure sustainable production and high-quality yields. In controlled environments such as... P. Guerra, A.R. Raucci, S.A. Gutierrez , J.F. Botero, C. Kamienski, F.M. Campos De Oliveira Download paper
5. 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
6. Orchestration of Missions for Coordination between Autonomous Agents in Agriculture. Due to technological advancement in agriculture, various autonomous agents, such as drones, mobile robots, and intelligent agricultural vehicles, are being used to automate repetitive tasks and increase agricultural production. In addition, these agents, often developed by different manufacturers and endowed with different capabilities, form a highly heterogeneous environment, imposing a central challenge to be solved: the need to manage these agents so that they can act in a coordinated and effective... V. Fontena, N.K. Wagner, C. Teixeira, J. Lopes, L. Moura, C. Guimarães Download paper

Filter results6 paper(s) found.

1. From Pocket to Pixels: Smartphone-Based Proximal Sensing for Quantitative Monitoring of Peach Canopy Dynamics

Monitoring peach canopy dynamics is essential for optimizing irrigation scheduling and promoting sustainable water management in intensively managed orchards. In well-watered systems, conventional irrigation practices often overlook spatial and temporal variability in canopy size and leaf area index (LAI), potentially leading to inefficient water use. Canopy size, as reflected by LAI—the ratio of total leaf area to ground area—serves as a key biophysical indicator linked to crop transpiration... L. Katz, K. Genkin, A. Landau, Y. Ness, O. Crane, R. Golan, N. Rotbart, E. Nevo, O. Shapira, E. Fereres, O. Reichmann, A. Brook

Download paper

2. Smartphone-Based RGB Phenotyping of Hydroponic Lettuce Growth Dynamics

Hydroponic lettuce production requires frequent, accurate growth assessment to optimize yield, nutrient efficiency, and crop uniformity within short, nutrient‑sensitive cycles. Existing imaging systems can deliver such precision but are often costly or technically demanding, limiting adoption in smaller hydroponic operations. Smartphone‑based imaging offers a practical alternative for scalable proximal phenotyping, yet its cross‑device quantitative accuracy and performance under operational... L. Katz, N. Snir, K. Genkin, N. Rotbart, E. Nevo, N. Ronen, O. Reichmann

Download paper

3. An Interpretable Machine Learning Framework for Soil Nutrient Assessment Based on pH and Electrical Conductivity

Understanding how the physical and chemical properties of soil influence nutrient availability is fundamental for advancing precision agriculture, as these properties directly affect the efficiency of macro- and micronutrient absorption by plants. In recent years, the increasing availability of open agricultural datasets has created new opportunities for developing data-driven frameworks capable of supporting large-scale soil assessment and decision-making. However, the effective integration of... T.A. Rodolfo, O.J. Gonzalez Zarate, C. Gonzalez Aguilera

Download paper

4. Soil-Sensing-Based Irrigation Decision Modeling for Greenhouse Tomato Crops Using Machine Learning

Global agriculture faces increasing pressure to optimize water-use efficiency, particularly for high-demand crops like tomato (Solanum lycopersicum). Tomato is among the most widely consumed vegetables worldwide, playing a central role in global food systems. From an agronomic perspective, tomato crops are highly sensitive to water availability and distribution, requiring precise irrigation management to ensure sustainable production and high-quality yields. In controlled environments such as... P. Guerra, A.R. Raucci, S.A. Gutierrez , J.F. Botero, C. Kamienski, F.M. Campos De Oliveira

Download paper

5. 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

6. Orchestration of Missions for Coordination between Autonomous Agents in Agriculture.

Due to technological advancement in agriculture, various autonomous agents, such as drones, mobile robots, and intelligent agricultural vehicles, are being used to automate repetitive tasks and increase agricultural production. In addition, these agents, often developed by different manufacturers and endowed with different capabilities, form a highly heterogeneous environment, imposing a central challenge to be solved: the need to manage these agents so that they can act in a coordinated and effective... V. Fontena, N.K. Wagner, C. Teixeira, J. Lopes, L. Moura, C. Guimarães

Download paper