From seed centres to tray cells: a geometry-aware machine vision framework for full-tray sowing quality assessment in maize plug trays
Year: 2026
Type: article
Abstract: Accurate sowing-quality assessment of maize seedling trays requires both reliable seed localisation and correct correspondence between detected seeds and physical tray cells. This study presents a semi-automated machine-vision framework combining heatmap-based seed-centre localisation, sliding-window inference, global non-maximum suppression (NMS), and four-corner-calibrated adaptive grid assignment. Each assessable cell was classified as empty, single-seeded, or multi-seeded according to the number of assigned seed centres. On source-image-independent subsets, the framework achieved an accuracy of 0.9821 and a macro F1-score of 0.9040 for 27 complete-tray images (3,456 cells), and 0.9798 and 0.9344, respectively, for 9 visible-cell images (1,140 assessable cells). Separately, the larger evaluation sets yielded accuracy/macro F1 values of 0.9783/0.9145 for 44 complete trays and 0.9782/0.9368 for 30 visible-cell images. These results support cell-level sowing-quality assessment under the evaluated acquisition conditions and within manually assessable regions. The source-image-independent subsets control direct source overlap but remain within the same acquisition domain; external generalisation across production sites, imaging devices, tray specifications, and acquisition conditions has not yet been established.
Source: Frontiers in Plant Science
Institution: Shanxi Agricultural University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7223327648