Automation of production in the nurseries of flower producing companies using barcode scanners have been attempted but with little success. Stationary laser barcode scanners which have been used for automation have failed due to the close proximity between the barcode and the scanner, and factors such as speed, angle of inclination of the barcode, damage to the barcode and dirt on the barcode. Furthermore, laser barcode scanners are still being used manually in the nurseries making work laborious and time consuming, which leading to reduced productivity. Therefore, an automated image-based barcode detection system to help solve the aforementioned problems was proposed. Experiments were conducted under different situations with clean and artificially soiled Code 128 barcodes in both the laboratory and under real production conditions in a flower producing company. The images were analyzed with a specific algorithm developed with the software tool Halcon. Overall the results from the company showed that the image-based system has a future prospect for automation in the nursery.

HighlightsLaser energy, type of marking on the product and product color affected the Data Matrix detectionHigh laser marking energy resulted in ablation, browning and carbonizationWorking direction of the laser beam affected the print growth of the Data MatrixProposed algorithm successfully decoded the barcodes on Golden Delicious applesAbstract. Product marking in horticulture aims at providing robust and permanent means of marking products and preventing theft, tampering and cheating by customers. Direct part marking has sought to provide solutions to these problems. However, unlike in industry where it has been successful, in horticulture there are still a lot of challenges that prevent successful marking and reading of directly marked barcodes on horticultural products. The laser energy, barcode size, product color and days of storage are important factors that affect the marking, quality and readability of directly marked Data Matrix (DM) on apples. Therefore, the objective of this study was to solve the aforementioned problems with these factors by using Synrad 48-5 CO2 laser (10,600 nm), to mark some apples using low energy levels. Laser energy, the skin of the product and the inertia of the laser beam affected the printing of the DM on the apples. Incomplete marking of the DM at some of the energies used resulted in the DM not being decoded. Generally, there was successful decoding on Golden Delicious compared to Kanzi and Red Jonaprince for 10 days of storage. On the average, the smaller barcode size produced a better detection of the code than the bigger size. The better detection on Golden Delicious can be attributed to the better contrast between the DM and its color. As the days of storage increased, detection decreased for Kanzi and Red Jonaprince. There is a future prospect for directly reading marked apples in real production systems. Keywords: Apple, Apple skin, Barcode size, Data matrix, Laser, Product marking.

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Automation of production in the nurseries of flower producing companies using barcode scanners have been attempted but with little success. Stationary laser barcode scanners which have been used for automation have failed due to the close proximity between the barcode and the scanner, and factors such as speed, angle of inclination of the barcode, damage to the barcode and dirt on the barcode. Furthermore, laser barcode scanners are still being used manually in the nurseries making work laborious and time consuming, which leading to reduced productivity. Therefore, an automated image-based barcode detection system to help solve the aforementioned problems was proposed. Experiments were conducted under different situations with clean and artificially soiled Code 128 barcodes in both the laboratory and under real production conditions in a flower producing company. The images were analyzed with a specific algorithm developed with the software tool Halcon. Overall the results from the company showed that the image-based system has a future prospect for automation in the nursery.