Machine Vision System for Determining Citrus Count and Size on a Canopy Shake and Catch Harvester
A machine vision-based citrus fruit counting system was developed for a continuous canopy shake and catch harvester. The system consisted of a 3CCD camera, four halogen lamps, an encoder, and a laptop computer. A total of 719 images were taken during an experiment on a test bench at the Citrus Research and Education Center (Lake Alfred, Fla.) and were used for analysis. The system was also tested on a canopy shake and catch harvester at a grove located in Fort Basinger, Florida, where a total of 773 images were acquired and 60 images were used for validation. Fruit weight was measured during image acquisition in 14 test bench experiments and in two field tests with a commercial canopy shake and catch harvester. An image processing algorithm that could identify fruit and measure its size was developed using a Bayesian classifier, morphological operations, and watershed segmentation. From the sets of color images, the number of fruit and total fruit areas were measured using the developed algorithm. Finally, the number of citrus fruits that were identified by the image processing algorithm during the test bench experiment was compared against actual fruit weight. The coefficient of determination (R2) between them was 0.962. To validate the canopy shake and catch harvester experiment, the number of fruit was counted manually from a total of 60 images. Density clustering was used to enhance the result of the Bayesian classifier. The manual count was compared with the image processing algorithm count. The R2 value was 0.891 between the actual and estimated number of fruit.
Sensing systems for precision agriculture in Florida
Many sensing systems were developed for use in precision agriculture in Florida.Examples include fruit recognition and disease detection platforms.A summary of past efforts is presented.Applications of different sensing systems and future directions are discussed. Many sensing systems have been developed for use in precision agriculture in Florida over the past decade. These systems have been designed for specialty crops such as citrus and blueberry. Systems include those for fruit recognition for yield mapping as well as those for disease detection systems using ground- and aerial-based platforms. Other systems discussed are used in soil phosphorus detection using near-infrared (NIR) and Raman spectroscopy, debris detection generated from citrus mechanical harvesting, detection of citrus fruit dropped on the ground due to disease, citrus leaf nitrogen detection, silage yield mapping, soil nutrients and grain insect detection using NIR spectroscopy. A summary of past efforts is presented in this paper, applications of these different sensing systems are discussed, and future directions are described.
Precision Farming Adoption in Florida Citrus: A Grower Case Study
In this case study the adoption process and investment decision made by an existing 
citrus caretaking organization is analyzed. The case identifies their production practices prior to 
considering the investment in precision farming technologies. The specific objective of this case 
study is to determine if the grower achieved break-even (BE) status on their investment in precision 
farming technologies. 
A cost savings of approximately $34/ha and $55/ha on two groves respectively, was realized after 
the adoption of a VRT fertilizer spreader system.