Determining the Percentage of Breakage and the Number of Corn and Pinto Bean Seeds using the Machine Vision System

Shaker, Mohammad

doi:10.22092/amsr.2025.370589.1524

Document Type : Original Article

Author

Mohammad Shaker

Assistant Professor, Agricultural Engineering Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research Education And Extention Organization, Shiraz, Iran

10.22092/amsr.2025.370589.1524

Abstract

In seed purity testing, machine vision can be used as a suitable alternative to human vision. In this research, a machine vision system was used, which included three parts: suction box, sampling box, and imaging box. The above machine vision system was evaluated for seeds of corn and pinto bean. For each type of seed, the performance of the suction device, with three seed plates (with hole diameters of 1, 1.5 and 2 mm) and four suction values (-80, -100, -120 and -130 mmHg) was evaluated. A factorial experiment in the form of a completely randomized design with two factors and five replications was used for statistical analysis of the data and Duncan's test was used to compare the means. In each amount of suction, the total number of seeds of sticked to the seed plate, the number of singled seeds and the number of seeds of sticked together on each hole were counted and their percentage was calculated. After that, for the two types of seeds tested, the algorithm for determining the percentage of breakage and the number of seed coding and validation of the algorithm was evaluated in 30 repetitions. The results showed that for corn seed, treatment 10 (seed plate with holes 2 mm and suction value -100 mm Hg) and for pinto bean seed, treatment 11 (seed plate with 2 mm holes and suction value -120 mm Hg) were the most suitable treatments. The validation results of the algorithm for determining the percentage of breakage and the number of seeds for corn seeds showed that the average accuracy of the algorithm was equal to 100%. For seed of pinto beans, the algorithm validation results showed that the average accuracy of the algorithm in determining the percentage of breakage and the number of seeds were 95.27 and 99.47%, respectively. It is suggested that this system be used to measure and evaluate the physical properties of seeds of different sizes.

Keywords

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Determining the Percentage of Breakage and the Number of Corn and Pinto Bean Seeds using the Machine Vision System

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Volume 25, Issue 90 - Serial Number 90June 2024Pages 79-94

Volume 25, Issue 90 - Serial Number 90
June 2024
Pages 79-94