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| eeding uniformity is a crucial index to seeding performance and quality of a grain seeder. A testing approach for seeding precision was developed in this paper, which is an integrated technology of machine vision, pattern recognition, and automatic control. The new method realizes a precise and reliable test of seeding precision, and an automatic control of tester operation. A machine vision based test-bed was developed for performance tests of grain seeders, such as a precision planter, a drill planter, a hill-drop planter, and a corresponding software package was compiled to capture the images of the deposited seeds, to segment the seeds from the background of an image, and to calculate the distances between seeds after precision seeding, the number of seeds per length after drill seeding, and the distance between hills and number of seeds per hill after hill-drop seeding. The date obtained above was then used to estimate the performance of a seeder by the statistical calculations. |
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| Keywords:Seeder performance test, Machine vision, Sequential image, Image splicing |
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