Knowledge, Innovation and Technology in Scientific Research Vol. 4

Seed is the entry point of every crop production cycle, and the physical, physiological, genetic and health attributes of a seed lot constrain what any subsequent agronomic input can achieve. Precision agriculture has been extended into this domain through machine learning and deep learning applied to optical, spectral, radiographic, magnetic resonance and genomic data, with the stated purpose of replacing slow, destructive and partly subjective seed testing procedures and of tightening control over seed multiplication fields. This review evaluates that claim rather than restating it. Literature identified through Crossref, the Directory of Open Access Journals, Europe PMC, OpenAIRE, Semantic Scholar and Google Scholar, together with standards documentation, was appraised for methodological adequacy, evidence-claim alignment and translational readiness, and was synthesised around the measurement logic of each seed quality attribute rather than around algorithm families. The evidence is strongest and most internally consistent for tasks with a stable, externally auditable reference standard: varietal and species discrimination, detection of internal voids, insect damage and mechanical injury, and germination outcome after a completed germination test. Reported performance is markedly weaker, and reference standards markedly noisier, for seed vigour, for storage longevity and for field emergence, which are precisely the attributes that determine commercial value. Recurrent methodological weaknesses limit confidence across the corpus: reference standards are themselves imprecise, data partitioning frequently permits information leakage between images of the same seed lot, single-lot and single-season designs dominate, class imbalance is handled inconsistently, and models are rarely tested across cultivars, seasons, instruments or production regions. Economic and decision-analytic evaluation is almost entirely absent, and no study identified demonstrated equivalence with a recognised seed testing rule under independent audit. Evidence is geographically concentrated, with limited representation of the seed systems where quality deficits are most consequential. Priorities include multi-lot and multi-site validation, calibration-aware reporting, shared benchmark datasets with physiological ground truth, and explicit comparison against the incumbent test rather than against an untrained baseline.