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.
Development of a Yield Monitor for Citrus Fruits
A method to measure the per-tree yield of citrus fruits was developed based on three 
approaches. Initially, the problem was viewed as a variation on the theme of particle counting as 
performed earlier on flows of fertilizer particles. The main difference was that 1) citrus fruits are 
much larger than fertilizer particles 2) they travel at a significantly lower speeds an 3) their flow 
density may be higher than that of fertilizer. 
The earlier research for fertilizer mass flows used a dedicated time of flight device that 
employed optical magnification to measure small particles with relatively large sensors. A 
second advantage was that the image of the particles could be projected in focus on the sensor plane. In the case of citrus fruit counting there was no need for magnification, since citrus fruits 
are much larger than the sensors. However, for citrus fruits this led to defocus problems, when 
direct lighting was used. 
The theory used in the fertilizer application was capable of dealing with multiple particles 
(clumps) interrupting the sensors simultaneously, which made the method non-intrusive and 
eliminated the need for particle singulation. Although this method was viewed to be transferable 
to the citrus counting problem, an easier and relatively low-tech method was assumed to be 
more reliable. A laser beam was projected just above the surface of a plate on which the fruits 
roll down into a collection bin. Theoretically, if the laser beam is placed low enough, each fruit 
will be counted individually, even if they are clumped together. Tests revealed that this 
assumption does not hold under high-density flow regimes. Therefore, an improved gutter 
system was designed which effectively transforms the flow of fruits into different streams of fruits 
in single file formation. This method, combined with low-placed laser beams, allowed for 
accurate counting of the fruits in the laboratory. In future research, the system will be tested on a 
canopy shaker machine.
Combine Feedrate Sensors
ABSTRACT POTENTIAL electronic grain combine feedrate sensors were tested in typical Indiana wheat, soybean and corn crops. Sensors based upon feeder drive motor differential hydraulic pressure and combine engine speed successfully indicated feedrate. Four other attempted sensors were not successful.