Machine vision based alternative testing approach for physical purity, viability and vigour testing of soybean seeds (Glycine max)

Abstract

The conventional methods for seed quality testing have several limitations as they involve visual assessment and are destructive. In this context, a study was performed to assess the suitability of non-contact, non-destructive type imaging techniques such as visible imaging and X-ray imaging for conducting physical purity, viability and vigour tests of soybean seeds. The seeds that appeared healthy in external surface examination using visible tests as well as in internal assessment using X-ray tests were classified as sound seeds while the other seeds were marked as not-sound seeds. The obtained results were then correlated with the results of the standard germination tests. The high correlation results between the imaging tests and the standard conventional germination tests indicate the effectiveness and usability of the proposed image analysis based technique as an attractive alternative to the existing quality assessment methods for soybean seeds.

Image processing to identify damage to soybean seeds

“…Thus, in recent years, the use of algorithms with appropriate pre-processing, with the aim of developing a system that can determine specific qualities of product characteristics, has become of great importance and utility for the businesses and is a technique that could be used in seed selection. MAHAJAN et al (2018) evaluated the potential of using non-destructive image processing techniques to perform tests of physical purity, viability, and vigor of soybean seeds through X-ray images. Results were obtained by correlating the standard germination rate in tests and indicated that the method is effective, fast, and non-destructive, making it a suitable alternative for seed quality tests, contributing to the use of structural tests and removing the limitations of personal inspections.…”

“…These techniques allow a quick, effective, and non-destructive analysis, and techniques based on the use of RGB (system based on primary colors redgreen-blue) images are increasingly used to evaluate the color and shape of seeds, thus guaranteeing their high quality (LIU et al, 2015;MAHAJAN et al, 2018). Thus, in recent years, the use of algorithms with appropriate pre-processing, with the aim of developing a system with product characteristics with specific qualities, has become of great importance and utility for businesses.…”

Interactive machine learning for soybean seed and seedling quality classification

“…IML approaches can therefore be effective in problems with small datasets or complex datasets when traditional machine learning methods become inefficient 6 . The combination of these machine learning algorithms with computer vision has brought new and promising perspectives for analyzing the quality of agricultural products, especially seeds [7][8][9] . With these technologies, many of the limitations now faced by traditional methods of visual seed inspection could be resolved.…”

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

“…This bottleneck has led to many attempts to automate both seed imaging and associated phenotypic analysis, resulting in several research‐based solutions such as G erminator and the pheno Seeder system, as well as the MultiSense tool (Ducournau et al ., 2005; Joosen et al ., 2010; Demilly et al ., 2015; Jahnke et al ., 2016; Ligterink & Hilhorst, 2016; Keil et al ., 2017). More recently, advanced computer‐vision (CV) and machine‐learning (ML) techniques are being applied to germination assays, including the Rice Seed Germination Evaluation System (RSGES) for assessing the germination status of Thai rice species using an artificial neural network (ANN) classifier (Lurstwut & Pornpanomchai, 2017); machine‐vision based analysis of visible and X‐ray images for evaluating soybean seed quality based on physical purity, viability and vigour (Mahajan et al ., 2018); deep learning (DL) algorithms such as U‐Net and ResNet for segmenting and classifying rice seed germination status (Nguyen et al ., 2018); linear discriminant analysis and multispectral imaging combined for classifying cowpea seeds into categories of ageing, germination, and normality (Elmasry et al ., 2019); and a high‐throughput micro‐CT‐RGB (HCR) phenotyping system for dissecting the rice genetic architecture from seedling (Wu et al ., 2019).…”

“…size and shape), cumulative germination rates (e.g. time to 50% germination, T 50 , and the proportion of seeds germinated at the conclusion of an experiment, G max ), and quality traits such as viability and vigour (Ducournau et al ., 2005; Jahnke et al ., 2016; Mahajan et al ., 2018). Nevertheless, the throughput, automation level, and the range of traits of the above solutions are still limited, such that seed imaging and associated germination‐related traits analyses still require human interference.…”