Proceedings :: International Society of Precision Agriculture

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Kamienski, C

Kaur Dhaliwal, A

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Authors

Heideker, A

Speranza, E.A

Ferreira, E

Silva, D

Kamienski, C

Bianchi, R

Rivera, F.P

Kamienski, C

Silva, D

Heideker, A

Bianchi, R

Kamienski, C

Bastos, L

Fuhrer, L

Porter, W

Scarpin, G.J

Kaur Dhaliwal, A

Bhattarai, A

Jakhar, A

Topics

Remote and Proximal Sensing of Soils and Crops

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Wireless Sensor Networks, Edge Computing, and Farm Connectivity

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Oral

Poster

Year

2026

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Filter results4 paper(s) found.
1. Satellite Imagery to Machine Learning Datasets: An Automated System for Soil Water Stress Monitoring in Agriculture Satellite remote sensing has become a key data source for precision agriculture, particularly for monitoring vegetation dynamics and soil water stress over large areas. Multispectral satellite imagery enables the computation of vegetation indices, including NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), which are commonly employed to quantify vegetation health, vigor, and canopy development. However, the practical use of satellite imagery in data-driven agricultural... A. Heideker, E.A. Speranza, E. Ferreira, D. Silva, C. Kamienski, R. Bianchi Download paper
2. Automated Leak Classification in Drip Irrigation Systems using Deep Learning and RGB Cameras The increasing demand for water efficiency in agriculture has driven the development of intelligent irrigation systems. Among them, drip irrigation is widely adopted due to its efficiency; however, these systems are susceptible to leaks caused by mechanical wear, animal interference, and adverse environmental conditions. The manual detection of leaks by human workers in drip irrigation systems is a time-consuming task, difficult to scale, ... F.P. Rivera, C. Kamienski Download paper
3. An AI-Ready Smart Adapter Architecture for Integrating Heterogeneous Agricultural IoT Systems Across the Edge–Cloud Continuum The increasing adoption of Internet of Things (IoT) technologies in smart agriculture has resulted in highly heterogeneous environments composed of diverse sensors, communication protocols, and distributed computing layers. Agricultural systems typically operate across the edge–cloud continuum, encompassing field devices, intermediate processing nodes, and cloud-based platforms. While IoT platforms provide essential services for data ingestion and device management, they often face limitations... D. Silva, A. Heideker, R. Bianchi, C. Kamienski Download paper
4. Comparison of Machine Learning Models for Within-field Cotton Yield Prediction: Assessing the Value of Temporal Data and Generalizability Cotton (Gossypium hirsitum L.) yield prediction is challenging due to the complex interactions between static environmental factors, such as soil properties and topography, and dynamic variables, including weather and crop management. These factors generate significant spatial and temporal variability within and across fields. While remote sensing technologies, particularly satellite-derived vegetation indices, provide spatially explicit data to evaluate crop health, translating... L. Bastos, L. Fuhrer, W. Porter, G.J. Scarpin, A. Kaur Dhaliwal, A. Bhattarai, A. Jakhar Download paper

Filter results4 paper(s) found.

1. Satellite Imagery to Machine Learning Datasets: An Automated System for Soil Water Stress Monitoring in Agriculture

Satellite remote sensing has become a key data source for precision agriculture, particularly for monitoring vegetation dynamics and soil water stress over large areas. Multispectral satellite imagery enables the computation of vegetation indices, including NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), which are commonly employed to quantify vegetation health, vigor, and canopy development. However, the practical use of satellite imagery in data-driven agricultural... A. Heideker, E.A. Speranza, E. Ferreira, D. Silva, C. Kamienski, R. Bianchi

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2. Automated Leak Classification in Drip Irrigation Systems using Deep Learning and RGB Cameras

The increasing demand for water efficiency in agriculture has driven the development of intelligent irrigation systems. Among them, drip irrigation is widely adopted due to its efficiency; however, these systems are susceptible to leaks caused by mechanical wear, animal interference, and adverse environmental conditions. The manual detection of leaks by human workers in drip irrigation systems is a time-consuming task, difficult to scale, ... F.P. Rivera, C. Kamienski

Download paper

3. An AI-Ready Smart Adapter Architecture for Integrating Heterogeneous Agricultural IoT Systems Across the Edge–Cloud Continuum

The increasing adoption of Internet of Things (IoT) technologies in smart agriculture has resulted in highly heterogeneous environments composed of diverse sensors, communication protocols, and distributed computing layers. Agricultural systems typically operate across the edge–cloud continuum, encompassing field devices, intermediate processing nodes, and cloud-based platforms. While IoT platforms provide essential services for data ingestion and device management, they often face limitations... D. Silva, A. Heideker, R. Bianchi, C. Kamienski

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4. Comparison of Machine Learning Models for Within-field Cotton Yield Prediction: Assessing the Value of Temporal Data and Generalizability

Cotton (Gossypium hirsitum L.) yield prediction is challenging due to the complex interactions between static environmental factors, such as soil properties and topography, and dynamic variables, including weather and crop management. These factors generate significant spatial and temporal variability within and across fields. While remote sensing technologies, particularly satellite-derived vegetation indices, provide spatially explicit data to evaluate crop health, translating... L. Bastos, L. Fuhrer, W. Porter, G.J. Scarpin, A. Kaur Dhaliwal, A. Bhattarai, A. Jakhar

Download paper