Researchers at the University of Florida (UF) have invented a way to quickly and accurately count the amount of citrus fruit dropped early so growers can see where the citrus greening disease  (HLB) is most affecting groves. 

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.

"This machine could have significant economic value to citrus growers," UF biological engineering professor Wonsuk 'Daniel' Lee said.

"First, fruit drop data would enable crop production estimates to be revised more accurately and timely during the course of a harvest season, and could also assist growers in scheduling their harvests to minimize fruit losses from the most affected areas."

Growers could also use the data to detect where citrus greening is most prevalent in their groves. They could then implement management practices like fertilization programs and irrigation schedules to fight greening and other diseases and minimize the fruit drop.

Currently, fruit drop data are collected by sampling random areas within a specific area and manually counting dropped fruit, which is costly and time-consuming.

Other researchers have developed imaging machines but had problems with the color resolution in their images, depending on the time of day pictures were taken due to the varying light levels.

The UF researchers created an outdoor imaging system with two cameras to obtain accurate color images. They were equipped with microprocessors and had special “charge-coupled device” sensors, which turn light into electrons for greater resolution.

They were also designed with the conditions of citrus groves in mind - dusty and humid with high temperatures and low-hanging branches. Finally, a global positioning system (GPS) receiver was attached.

In a 2013 test, researchers took 180 images at three illumination levels -- dark, medium and bright, with the machine moving at a speed of 5 miles per hour.

Citrus fruit under dark illumination, due to cloud cover, appeared almost brown and there was no distinctive color variation between the background and the fruit, making it hard to count accurately.

However, at medium and bright illumination, the fruit color was orange and bright yellow. Lee’s machine had an accuracy rate of as much as 88%.

The team’s research has been accepted for publication by the Transactions of the ASABE (American Society of Agricultural and Biological Engineers) journal.

The team is already working on a machine vision system using multiple video cameras with high-definition resolution. They plan to make their current machine vision system a real-time system that can be used easily in a commercial citrus grove.

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