Proceedings :: International Society of Precision Agriculture

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Salem, M.A

Lemus, S

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Authors

Scudiero, E

Nugent, C.I

Ng, C

Jones, N

Azzam, T

Salunga, N.G

Lemus, S

Rabia, A.H

Salem, M.A

Rabia, A.H

Topics

Education of Precision Agriculture Topics and Practices

Big Data, Data Mining and Deep Learning

Precision Crop Protection

Type

Oral

Poster

Year

2024

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Filter results3 paper(s) found.
1. Cultivating Future Leaders in Sustainable Agriculture: Insights from the Digital Agriculture Fellowship Program at the University of California, Riverside Funded by USDA's National Institute of Food and Agriculture’s Sustainable Agricultural Systems Program and housed at the University of California, Riverside (UCR), the Digital Agriculture Fellowship (DAF) aims at equipping undergraduate students with the knowledge and experience necessary to meet the agricultural challenges posed by climate change and sustainability concerns. The program was established in 2020 and will be funded through 2026. Activities span over fifteen months for... E. Scudiero, C.I. Nugent, C. Ng, N. Jones, T. Azzam, N.G. Salunga, S. Lemus Download paper
2. Potato Disease Detection Using Laser Speckle Imaging and Deep Learning Early detection of potato diseases is essential for minimizing crop loss. Implementing advanced imaging techniques can significantly improve the accuracy and efficiency of disease detection in potato crops. Leveraging machine learning algorithms can further enhance the speed and precision of disease identification, enabling timely intervention measures. This work presents a novel potato disease detection technique using whole-potato speckle imaging and deep learning. Laser Speckle Imaging (LSI),... A.H. Rabia, M.A. Salem Download paper
3. Development of a High-throughput UAV System for Precision Weed Detection and Control Using Laser Speckle Imaging and UV-C Irradiation Traditional weed control methods, predominantly reliant on herbicides or labor-intensive ground robots, present notable environmental and efficiency challenges within agricultural practices. To address these concerns, this study introduces an innovative approach utilizing unmanned aerial vehicles (UAVs) for autonomous weed detection and control in agricultural fields. Our proposed system depends on the agility of UAV platforms, integrating two primary technologies. Firstly, Laser Speckle Imaging... M.A. Salem, A.H. Rabia Download paper

Filter results3 paper(s) found.

1. Cultivating Future Leaders in Sustainable Agriculture: Insights from the Digital Agriculture Fellowship Program at the University of California, Riverside

Funded by USDA's National Institute of Food and Agriculture’s Sustainable Agricultural Systems Program and housed at the University of California, Riverside (UCR), the Digital Agriculture Fellowship (DAF) aims at equipping undergraduate students with the knowledge and experience necessary to meet the agricultural challenges posed by climate change and sustainability concerns. The program was established in 2020 and will be funded through 2026. Activities span over fifteen months for... E. Scudiero, C.I. Nugent, C. Ng, N. Jones, T. Azzam, N.G. Salunga, S. Lemus

Download paper

2. Potato Disease Detection Using Laser Speckle Imaging and Deep Learning

Early detection of potato diseases is essential for minimizing crop loss. Implementing advanced imaging techniques can significantly improve the accuracy and efficiency of disease detection in potato crops. Leveraging machine learning algorithms can further enhance the speed and precision of disease identification, enabling timely intervention measures. This work presents a novel potato disease detection technique using whole-potato speckle imaging and deep learning. Laser Speckle Imaging (LSI),... A.H. Rabia, M.A. Salem

Download paper

3. Development of a High-throughput UAV System for Precision Weed Detection and Control Using Laser Speckle Imaging and UV-C Irradiation

Traditional weed control methods, predominantly reliant on herbicides or labor-intensive ground robots, present notable environmental and efficiency challenges within agricultural practices. To address these concerns, this study introduces an innovative approach utilizing unmanned aerial vehicles (UAVs) for autonomous weed detection and control in agricultural fields. Our proposed system depends on the agility of UAV platforms, integrating two primary technologies. Firstly, Laser Speckle Imaging... M.A. Salem, A.H. Rabia

Download paper