Abstract
Rice seed production in Malaysia is greatly dependent on the purity of the cultivated paddy seed produced through the government certified paddy seed program. The seeds to be marketed by the seed processors must undergo quality control protocol where the seed lots are sampled from the seed farms and seed processing plants for purity analysis by the enforcing agency at the Seed Testing Laboratory of the Department of Agriculture (DoA). The current inspection conducted by the laboratory is based on a manual process, which is laborious and time-consuming. Therefore, a prototype (Patent ID: PI2018500018) of a machine vision-based rice seed inspection system (RiSe-IViS) was developed to explore the possibility of replacing the existing manual method in distinguishing the weedy rice and cultivated rice seeds under the Standard Jabatan Pertanian Malaysia (SJPM) standard protocol with a modern, effective and efficient technique using an image processing approach. The developed RiSe-IViS prototype consists of two parts i) hardware configuration and ii) software development. This paper discussed the criteria to be established, challenges and limitation encountered in developing the hardware prototype involving the image acquisition setup, lighting configuration and seed plate design. The importance of each criterion to ensure its reproducibility are also discussed. A software programme was developed to assist the user for image acquisition and analysis. The image processing steps undertaken in the programme are also discussed. The RiSe-IViS is expected to classify major rice seed varieties available in Malaysia against the weedy rice variants with superior accuracy.
Highlights
Machine vision technology has been utilized in several applications in the agricultural sectors, such as land-based and aerial-based remote sensing for the application in precision agriculture, natural resources assessment, fresh produce quality assessment, sorting and classification, and in process automation
If a low-resolution lens is paired with a high-resolution image sensor, the overall machine vision system becomes limited by the resolution of the lens
A complementary metal oxide semiconductor (CMOS) sensor was chosen over charge couple device (CCD) sensor due to its lower cost and improved quality as near to CCD sensor
Summary
Machine vision technology has been utilized in several applications in the agricultural sectors, such as land-based and aerial-based remote sensing for the application in precision agriculture, natural resources assessment, fresh produce quality assessment, sorting and classification, and in process automation. Machine vision equipped with hyperspectral, near infrared and infrared camera are able to inspect the internal quality of produce under the light invisible to humans such as ultraviolet (UV), near infrared (NIR) and infrared (IR). The use of machine vision techniques offers numerous potential to helps farmers to make a better management decision besides reducing the time taken to solve complex agricultural problems. As for an instance, machine vision equipped with sensing elements and machine learning techniques provides a powerful set of tools that applied for different field application in agricultural practices (Rehman et al, 2019). The systems developed are fairly accurate, nondestructive and yield consistent remove the possibility of human error and reduce the time taken for decision making
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