A - Papers appearing in refereed journals
El Akrouchi, M., Mhada, M., Gracia, D. R., Hawkesford, M. J. and Gerard, B. 2025. Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture. Frontiers in Plant Science. 16, p. 1472688. https://doi.org/10.3389/fpls.2025.1472688
Authors | El Akrouchi, M., Mhada, M., Gracia, D. R., Hawkesford, M. J. and Gerard, B. |
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Abstract | Quinoa is a resilient, nutrient-rich crop with strong potential for cultivation in marginal environments, yet it remains underutilized and under-researched, particularly in the context of automated yield estimation. In this study, we introduce a novel deep learning approach for quinoa panicle detection and counting using instance segmentation via Mask R-CNN, enhanced with an EfficientNet-B7 backbone and Mish activation function. We conducted a comparative analysis of various backbone architectures, and our improved model demonstrated superior performance in accurately detecting and segmenting individual panicles. This instance-level detection enables more precise yield estimation and offers a significant advancement over traditional methods. To the best of our knowledge, this is the first application of instance segmentation for quinoa panicle analysis, highlighting the potential of advanced deep learning techniques in agricultural monitoring and contributing valuable benchmarks for future AI-driven research in quinoa cultivation |
Keywords | Mask R-CNN; Instance segmentation; Quinoa; Precision agriculture; Deep learning |
Year of Publication | 2025 |
Journal | Frontiers in Plant Science |
Journal citation | 16, p. 1472688 |
Digital Object Identifier (DOI) | https://doi.org/10.3389/fpls.2025.1472688 |
Open access | Published as ‘gold’ (paid) open access |
Funder | Office Chérifien des Phosphate (OCP) |
Funder project or code | OCP/UM6P Bioproducts for African Agriculture |
Publisher's version | |
Output status | Published |
Publication dates | |
Online | 02 Jun 2025 |
Publication process dates | |
Accepted | 25 Apr 2025 |
Publisher | Frontiers Media SA |
ISSN | 1664-462X |
Permalink - https://repository.rothamsted.ac.uk/item/993z6/optimizing-mask-r-cnn-for-enhanced-quinoa-panicle-detection-and-segmentation-in-precision-agriculture