Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning for Mango (Mangifera Indica) Panicle Stage Classification

Version 1 : Received: 11 December 2019 / Approved: 12 December 2019 / Online: 12 December 2019 (04:17:07 CET)

A peer-reviewed article of this Preprint also exists.

Koirala, A.; Walsh, K.B.; Wang, Z.; Anderson, N. Deep Learning for Mango (Mangifera indica) Panicle Stage Classification. Agronomy 2020, 10, 143. Koirala, A.; Walsh, K.B.; Wang, Z.; Anderson, N. Deep Learning for Mango (Mangifera indica) Panicle Stage Classification. Agronomy 2020, 10, 143.

Journal reference: Agronomy 2020, 10, 143
DOI: 10.3390/agronomy10010143

Abstract

A pixel-based segmentation method was demonstrated to be confounded by developmental stage in estimation of flowering of mango. Categorization of panicles into three developmental stages was undertaken with a single and a two-stage deep learning framework (YOLO and R2CNN), using either upright or rotated bounding boxes. For a validation image set and for total panicle count, the models MangoYOLO(-upright), MangoYOLO-rotated, YOLOv3-rotated, R2CNN(-rotated) and R2CNN-upright achieved: (i) RMSEs of 25.6, 16.0, 15.4, 25.8 and 32.3 panicles per tree image, (ii) Mean average precision (mAP) scores of 72.2, 69.1, 65.0, 62.5 and 70.9% and (iii) weighted F1-scores of 76.5, 76.1, 74.9, 74.0 and 82.0, respectively. For a test set of images involving a different orchard and cultivar and use of a different camera, the R2 for machine vision to human count of panicles per tree was 0.86, 0.80, 0.83, 0.81 and 0.76 for the same models, respectively. Thus, models generalised well, but with no consistent benefit from use of rotated over upright bounding boxes. While the YOLOv3-rotated model was superior in terms of total panicle count, the R2CNN-upright model was more accurate for panicle stage classification. To demonstrate practical application, panicle counts were made weekly for an orchard of 994 trees, with a peak detection routine applied to document multiple flowering events.

Subject Areas

bounding box; deep learning; mangifera indica; panicle classification, rotation; segmentation

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