Proceedings :: International Society of Precision Agriculture

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Shovic, J.C

Tavares, A.C

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Authors

da Silva, J.P

Araujo, G.A

Carvalho, L

Verçosa, J.P

Tavares, A.C

Wing, K

Onyeoguzoro, D

Everett, M

Shovic, J.C

Silva, V.S

Silva, E.S

Silva, D.O

Oliveira, M.F

Tavares, A.C

Negrini, R.P

Mendes, L.A

Topics

UAV-Based Scouting, Imaging, and Targeted Applications

Wireless Sensor Networks, Edge Computing, and Farm Connectivity

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Poster

Oral

Year

2026

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Authors

Filter results3 paper(s) found.
1. Determination And Calculation Of Eucalyptus Biomass From Lidar Sensor Data And Projection Of Available Biomass Eucalyptus (Eucalyptus spp.) is one of the most widely cultivated forest species in Brazil and worldwide. Eucalyptus biomass is organic matter derived from the eucalyptus tree which can be used as a renewable energy source. In recent years, advances in remote sensing technologies have made it possible to estimate forest biomass more accurately and non-destructively, with the use of LiDAR (Light Detection and Ranging) sensors being particularly noteworthy. This system emits rapid... J.P. Da Silva, G.A. Araujo, L. Carvalho, J.P. Verçosa, A.C. Tavares Download paper
2. An Open-Source, Universal Arduino Library for LoRaWAN Integration with Low-Cost, Long-Range Agricultural Sensor Networks The Data-Gator is a low-cost, open-source sensor platform built on the ESP32 microcontroller. It supports I2C, analog, and Bluetooth Low Energy (BLE) sensor interfaces and can be configured without writing any code. It has been tested at a vineyard and an organic orchard, where it collects readings from a number of sensors at regular intervals. While the platform performs well in these settings, it currently relies on WiFi to send data back from the field. This limits... K. Wing, D. Onyeoguzoro, M. Everett, J.C. Shovic Download paper
3. Satellite Embedding-Based Corn Yield Prediction Using AutoML and Explainable AI Accurate, spatially explicit yield mapping underpins many precision agriculture decisions (e.g., variable-rate inputs and zone management), yet reliable yield monitor data are not always available and can be difficult to standardize across operations. Satellite-based yield models are often built from hand-crafted vegetation indices or phenology metrics, which may limit transferability across fields and years. Here, we evaluated a pixel-level corn yield prediction workflow that uses Satellite Embedding... V.S. Silva, E.S. Silva, D.O. Silva, M.F. Oliveira, A.C. Tavares, R.P. Negrini, L.A. Mendes Download paper

Filter results3 paper(s) found.

1. Determination And Calculation Of Eucalyptus Biomass From Lidar Sensor Data And Projection Of Available Biomass

Eucalyptus (Eucalyptus spp.) is one of the most widely cultivated forest species in Brazil and worldwide. Eucalyptus biomass is organic matter derived from the eucalyptus tree which can be used as a renewable energy source. In recent years, advances in remote sensing technologies have made it possible to estimate forest biomass more accurately and non-destructively, with the use of LiDAR (Light Detection and Ranging) sensors being particularly noteworthy. This system emits rapid... J.P. Da Silva, G.A. Araujo, L. Carvalho, J.P. Verçosa, A.C. Tavares

Download paper

2. An Open-Source, Universal Arduino Library for LoRaWAN Integration with Low-Cost, Long-Range Agricultural Sensor Networks

The Data-Gator is a low-cost, open-source sensor platform built on the ESP32 microcontroller. It supports I2C, analog, and Bluetooth Low Energy (BLE) sensor interfaces and can be configured without writing any code. It has been tested at a vineyard and an organic orchard, where it collects readings from a number of sensors at regular intervals. While the platform performs well in these settings, it currently relies on WiFi to send data back from the field. This limits... K. Wing, D. Onyeoguzoro, M. Everett, J.C. Shovic

Download paper

3. Satellite Embedding-Based Corn Yield Prediction Using AutoML and Explainable AI

Accurate, spatially explicit yield mapping underpins many precision agriculture decisions (e.g., variable-rate inputs and zone management), yet reliable yield monitor data are not always available and can be difficult to standardize across operations. Satellite-based yield models are often built from hand-crafted vegetation indices or phenology metrics, which may limit transferability across fields and years. Here, we evaluated a pixel-level corn yield prediction workflow that uses Satellite Embedding... V.S. Silva, E.S. Silva, D.O. Silva, M.F. Oliveira, A.C. Tavares, R.P. Negrini, L.A. Mendes

Download paper