Precision Agriculture Laboratory News
Precision Agriculture Lab developed a postharvest inspection system
March 15, 2017
To enable precision agriculture technology in Florida’s citrus industry, a machine vision system was developed
to identify common citrus production problems such as Huanglongbing (HLB), rust mite and wind scar. Objectives of this research were 1) to develop a simultaneous image acquisition system using multiple cameras on a customized conveyor that rotates citrus fruit to allow the imaging hardware to acquire the entire fruit surfaces, 2) to develop a machine vision algorithm with a deep learning technique utilizing a convolutional neural network to accurately inspect the visual characteristics of fruit surface and distinguish HLB-infected citrus from fruit with other common defects and 3) to simulate real-time video processing utilizing a GPU for faster image processing. A real-time video processing with the state-of-the-art deep learning algorithm was developed and tested using uncompressed RGB video streams recorded from the developed hardware. Accuracy of various defect detection by the deep convolutional neural network was 100, 89.7, 94.7, and 88.9 percent for healthy, HLB, rust mite and wind scar classes, respectively. The system can be used in citrus packing houses or developed on a portable conveyor that identifies the severity of the diseases in particular locations and enables site-specific crop management in the field.
Precision Agriculture Lab Published a patent about a Method for Huanglongbing (HLB) Detection
December 23, 2015
Dr. Wonsuk “Daniel” Lee (the director of the Precision Agriculture Laboratory) and Dr. Alireza Pourreza (PostDoc) in colaboration with Dr. Eran Raveh (Agricultural Research Organization ARO, Bet Dagan, Israel), and Dr. Reza Ehsani (Citrus Research and Education Center, UF) published a patent on their Method for Huanglongbing (HLB) Detection. More information...
Precision Agriculture Lab researchers develop machine to count dropped citrus, identify problem areas in groves
August 12, 2015
University of Florida researchers Wonsuk “Daniel” Lee, Daeun "Dana" Choi, Reza Ehsani and Fritz Roka devised a “machine vision system” to count citrus fruit that has dropped early. The device is suitable for various conditions in citrus groves, including addressing problems of variable lighting, giving accurate estimates of dropped fruit counts and providing exact locations of trees with greater fruit drop, indicating a problem area...Full Story
Read more about this invention on The Washington Times, , Brazil Business Today, , noodles, , Phys.org, , Agri Marketing, , Southeast AG NET, , and Growing Florida.
A new PhD student, Hao Gan, joined the precision agriculture laboratoryMay 18th, 2015
|Hao Gan joined the Precision Agriculture team at the University of Florida as a new PhD student. his research is focused on detection of immature green citrus fruit using hyper spectral and thermal imaging. His previous research effort was on the navigation of field robots. He received a bachelor's degree from Yangzhou University (China) and a master's degree from University of Illinois at Urbana-Champaign.|
Citrus Greening (HLB) detection sensorFebruary 25th, 2015 Video by Alireza Pourreza
UF IFAS News released an article about the citrus greening detection sensor that was developed at the precision agriculture laboratory, UF.
January 28, 2015
While a commercially available cure for crop-killing citrus greening remains elusive, University of Florida researchers have developed a tool to help growers combat the insidious disease: an efficient, inexpensive and easy-to-use sensor that can quickly detect whether a tree has been infected.
That early warning could give growers enough lead time to destroy plagued trees and save the rest...Full Story
Read more about this invention on Fresh Fruit Portal, Morning AgClips , Growing produce , Florida Research Consortium , and The World News.