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<p>India has a rich heritage of floral diversity and is well known for its medicinal plants but their identification is a major challenge in ayurvedic pharmaceutics. Hence, automated identification of medicinal flora is pivotal for sustainable pharmacognosy and botanical conservation. <i>Muntingia calabura</i> known for its potent therapeutic properties from high-resolution images. This research presents a novel <i>Binary Flower Pollination Algorithm (BFPA)</i> driven <i>Deep SE-ResNeXt</i> framework for the automated and robust identification of <i>Muntingia calabura.</i> In this study the authors proposed a framework that synergizes the hierarchical feature extraction capability of the SE-ResNeXt architecture with the optimization power of BFPA, inspired by the natural pollination behavior of flowering plants. In this framework, the SE-ResNeXt model, embedded with squeeze-and-excitation (SE) blocks, amplifies channel interdependencies and facilitates discriminative feature learning. Further, to fine-grained the identification ability BFPA is applied, which helped in selecting optimal feature subsets by simulating the global-local pollination balance through Lévy flight-based strategies. This binary optimization approach reduces redundancy in high-dimensional feature spaces, enhancing both computational efficiency and model accuracy. The BFPA-SE-ResNeXt with dense layer framework demonstrated better performance compared to conventional CNNs and other evolutionary optimization-based classifiers, achieving an identification accuracy of 97.50%, precision of 96.37%, and recall of 97%. This research underscores the effectiveness of bio-inspired hybrid intelligence systems for plant species identification and offers a scalable solution for real-world ecological applications.</p>

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Binary flower pollination algorithm driven deep SE-ResNeXt framework for the identification of Muntingia calabura

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Devineni Vijaya Sri,
  • Punyala Ramadevi,
  • Tavva Vamsi Priya,
  • Seeda Thanmai,
  • Matta Tejaswi,
  • Shaik Sadhika,
  • Jalluri Geetha Renuka

摘要

India has a rich heritage of floral diversity and is well known for its medicinal plants but their identification is a major challenge in ayurvedic pharmaceutics. Hence, automated identification of medicinal flora is pivotal for sustainable pharmacognosy and botanical conservation. Muntingia calabura known for its potent therapeutic properties from high-resolution images. This research presents a novel Binary Flower Pollination Algorithm (BFPA) driven Deep SE-ResNeXt framework for the automated and robust identification of Muntingia calabura. In this study the authors proposed a framework that synergizes the hierarchical feature extraction capability of the SE-ResNeXt architecture with the optimization power of BFPA, inspired by the natural pollination behavior of flowering plants. In this framework, the SE-ResNeXt model, embedded with squeeze-and-excitation (SE) blocks, amplifies channel interdependencies and facilitates discriminative feature learning. Further, to fine-grained the identification ability BFPA is applied, which helped in selecting optimal feature subsets by simulating the global-local pollination balance through Lévy flight-based strategies. This binary optimization approach reduces redundancy in high-dimensional feature spaces, enhancing both computational efficiency and model accuracy. The BFPA-SE-ResNeXt with dense layer framework demonstrated better performance compared to conventional CNNs and other evolutionary optimization-based classifiers, achieving an identification accuracy of 97.50%, precision of 96.37%, and recall of 97%. This research underscores the effectiveness of bio-inspired hybrid intelligence systems for plant species identification and offers a scalable solution for real-world ecological applications.