Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture
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 detect...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1472688/full |
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| author | Manal El Akrouchi Manal El Akrouchi Manal Mhada Dachena Romain Gracia Malcolm J. Hawkesford Bruno Gérard |
| author_facet | Manal El Akrouchi Manal El Akrouchi Manal Mhada Dachena Romain Gracia Malcolm J. Hawkesford Bruno Gérard |
| author_sort | Manal El Akrouchi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2d7149f9c2ce42f0b38991c08ddd2e7a |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-2d7149f9c2ce42f0b38991c08ddd2e7a2025-08-20T03:16:17ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-06-011610.3389/fpls.2025.14726881472688Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agricultureManal El Akrouchi0Manal El Akrouchi1Manal Mhada2Dachena Romain Gracia3Malcolm J. Hawkesford4Bruno Gérard5College of Agriculture and Environmental Sciences, University Mohammed VI Polytechnic (UM6P), Ben Guerir, MoroccoSchool of Collective Intelligence, University Mohammed VI Polytechnic (UM6P), Rabat, MoroccoCollege of Agriculture and Environmental Sciences, University Mohammed VI Polytechnic (UM6P), Ben Guerir, MoroccoCollege of Agriculture and Environmental Sciences, University Mohammed VI Polytechnic (UM6P), Ben Guerir, MoroccoSustainable Soils and Crops Department, Rothamsted Research, Harpenden, Hertfordshire, United KingdomCollege of Agriculture and Environmental Sciences, University Mohammed VI Polytechnic (UM6P), Ben Guerir, MoroccoQuinoa 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.https://www.frontiersin.org/articles/10.3389/fpls.2025.1472688/fullMask R-CNNinstance segmentationquinoaprecision agriculturedeep learning |
| spellingShingle | Manal El Akrouchi Manal El Akrouchi Manal Mhada Dachena Romain Gracia Malcolm J. Hawkesford Bruno Gérard Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture Frontiers in Plant Science Mask R-CNN instance segmentation quinoa precision agriculture deep learning |
| title | Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture |
| title_full | Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture |
| title_fullStr | Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture |
| title_full_unstemmed | Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture |
| title_short | Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture |
| title_sort | optimizing mask r cnn for enhanced quinoa panicle detection and segmentation in precision agriculture |
| topic | Mask R-CNN instance segmentation quinoa precision agriculture deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1472688/full |
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