Abstract
Achieving seed germination quality standards poses a real challenge to seed companies as they are compelled to abide by strict certification rules, while having only partial seed separation solutions at their disposal. This discrepancy results with wasteful disqualification of seed lots holding considerable amounts of good seeds and further translates to financial losses and supply chain insecurity. Here, we present the first-ever generic germination prediction technology that is based on deep learning and RGB image data and facilitates seed classification by seed germinability and usability, two facets of germination fate. We show technology competence to render dozens of disqualified seed lots of seven vegetable crops, representing different genetics and production pipelines, industrially appropriate, and to adequately classify lots by utilizing available crop-level image data, instead of lot-specific data. These achievements constitute a major milestone in the deployment of this technology for industrial seed sorting by germination fate for multiple crops.
Highlights
Using representation of the sampling space[14]
Prediction of seed germination by RGB image analysis is applicable across a broad phylogeny
A straight-forward approach would be prediction of seed germinability and usability by classifiers trained with samples of a composite of lots, as it would both expand the training set with morphological variation that can be beneficial for the training process and circumvent the need for classifier training per lot
Summary
Using representation of the sampling space (known as the training set)[14]. The two learning approaches differ in the algorithms employed and the narrowing of machine learning to particular measurable features. When adequately trained with a representative assortment of crop-level seeds, it is further able to properly classify new, untrained lots and varieties, differing from the seeds composing the training set in cultivation time and place, and genetics, potentializing seamless application, without recurring training phases This tool constitutes a solid milestone in the process of developing the very first sorting machine that addresses germination fate per se, with an aim to provide the seed industry with a reliable and consistent solution that outperforms the preparatory procedures applied today with a single sorting event, based on the germinability and usability probabilities of every seed evaluated
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