Computer Tools for Diagnosing Citrus Leaf Symptoms (Part 2): Smartphone Apps for Expert Diagnosis of Citrus Leaf Symptoms
Vol. 2020 No. 5, UF/IFAS Citrus Research and Education Center
Vol. 2020 No. 5

Computer Tools for Diagnosing Citrus Leaf Symptoms (Part 2): Smartphone Apps for Expert Diagnosis of Citrus Leaf Symptoms: SL478/SS691, 9/2020

UF/IFAS Citrus Research and Education Center
https://doi.org/10.32473/edis-ss691-2020
Published 2020-10-01
Arnold Schumann
University of Florida
Laura Waldo
University of Florida
Perseveranca Mungofa
University of Florida
Chris Oswalt
University of Florida
A person's hand holding a smartphone, taking a picture of a leaf for analysis. Credit: UF/IFAS
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How to Cite

Schumann, Arnold, Laura Waldo, Perseveranca Mungofa, and Chris Oswalt. 2020. “Computer Tools for Diagnosing Citrus Leaf Symptoms (Part 2): Smartphone Apps for Expert Diagnosis of Citrus Leaf Symptoms: SL478/SS691, 9/2020”. EDIS 2020 (5). Gainesville, FL. https://doi.org/10.32473/edis-ss691-2020.
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Abstract

Visual identification of nutrient deficiencies in foliage is an important diagnostic tool for fine-tuning nutrient management of citrus. This new 2-page article describes a new smartphone app that uses a trained neural network to identify disease and pest symptoms on citrus leaves through your phone's camera. Written by Arnold Schumann, Laura Waldo, Perseveranca Mungofa, and Chris Oswalt, and published by the UF/IFAS Department of Soil and Water Sciences.
https://edis.ifas.ufl.edu/ss691

https://doi.org/10.32473/edis-ss691-2020
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PDF-2020

References

Redmon, Joseph, and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. University of Washington. https://github.com/pjreddie

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) license.

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