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Open AccessArticle

Deep Learning for Mango (Mangifera indica) Panicle Stage Classification

Institute for Future Farming Systems, Central Queensland University, Building 361, Bruce Highway, Rockhampton, QLD 4701, Australia
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Agronomy 2020, 10(1), 143; https://doi.org/10.3390/agronomy10010143
Received: 11 December 2019 / Revised: 13 January 2020 / Accepted: 15 January 2020 / Published: 18 January 2020
(This article belongs to the Special Issue In-Field Estimation of Fruit Quality and Quantity)
Automated assessment of the number of panicles by developmental stage can provide information on the time spread of flowering and thus inform farm management. A pixel-based segmentation method for the estimation of flowering level from tree images was confounded by the developmental stage. Therefore, the use of a single and a two-stage deep learning framework (YOLO and R2CNN) was considered, using either upright or rotated bounding boxes. For a validation image set and for a total panicle count, the models MangoYOLO(-upright), MangoYOLO-rotated, YOLOv3-rotated, R2CNN(-rotated) and R2CNN-upright achieved weighted F1 scores of 76.5, 76.1, 74.9, 74.0 and 82.0, respectively. For a test set of the images of another cultivar and using 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, there was no consistent benefit from the use of rotated over the use of upright bounding boxes. The YOLOv3-rotated model was superior in terms of total panicle count, and 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. View Full-Text
Keywords: bounding box; deep learning; Mangifera indica; panicle classification; rotation; segmentation bounding box; deep learning; Mangifera indica; panicle classification; rotation; segmentation
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MDPI and ACS Style

Koirala, A.; Walsh, K.B.; Wang, Z.; Anderson, N. Deep Learning for Mango (Mangifera indica) Panicle Stage Classification. Agronomy 2020, 10, 143. https://doi.org/10.3390/agronomy10010143

AMA Style

Koirala A, Walsh KB, Wang Z, Anderson N. Deep Learning for Mango (Mangifera indica) Panicle Stage Classification. Agronomy. 2020; 10(1):143. https://doi.org/10.3390/agronomy10010143

Chicago/Turabian Style

Koirala, Anand; Walsh, Kerry B.; Wang, Zhenglin; Anderson, Nicholas. 2020. "Deep Learning for Mango (Mangifera indica) Panicle Stage Classification" Agronomy 10, no. 1: 143. https://doi.org/10.3390/agronomy10010143

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