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.
Recognition of partially occluded plant leaves using a modified watershed algorithm
Occlusion is an obstacle to two-dimensional machine vision recognition of plants in natural outdoor scenes. Five 
modifications to the Watershed algorithm were investigated for separating occluded plant leaves, in an attempt to reduce the 
excessive object fragmentation associated with the original Watershed algorithm. The best modified algorithm improved the 
recognition of occluded tomato cotyledons and tomato true leaves improved by a factor of 3 and 2, respectively, after it was 
applied to occluded plant leaves in natural outdoor scenes. Two of the modified Watershed algorithms required about 11% 
less computation time than the original.
Mite population density analysis of stored dried apricots in Turkey
The aim of this study was to determine the population density of mite species attacking stored dried apricots in Malatya, Elazig and Izmir regions of Turkey. Storage construction types and duration of storage were also considered. The samples were collected during 2000–2003 in the most important apricot producing areas (Malatya and Elazig) and the exporting center (Izmir). Frequency of occurrence and rates of intensity of infestations were determined (16.50% from Malatya, 3.19% from Elazig and 80.49% from Izmir). A total of 16 mite species belonging to 13 genera and 11 families were identified during the surveys. The pest species Carpoglyphus lactis (L.) (Astigmata: Carpoglyphidae) was the most abundant species recorded (69.14% of samples). The highest infestation value recorded was 15,534 mites per kg.