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<p>The cultivation of brinjal is seriously affected by various pathogens which affect crop yield and quality hence the importance of automated disease detection systems in precision agriculture. The conventional image-based diagnostic methods fail to differentiate between similar diseases classes that look similar and this results in misdiagnosis and poor crop management. This research paper advances to suggest a lightweight deep learning model, IEMA-YOLOv11 (Improved Efficient Multi-Scale Attention-based YOLOv11), to successfully identify and detect Brinjal disease successfully in real-time. The model combines an EMA (Efficient Multi-Scale Attention) system, IRMB (Inverted Residual Mobile Blocks), and a LDFM (Local Detail Feature Module) to obtain fine-grained lesion features, as well as a refined MPDIoU loss to achieve a better object localization. The framework was tested against a curated multi-class brinjal disease data of six fungal, two bacterial, three viral and one nematode infection. IEMA-YOLOv11 had a precision of 96.5, a recall of 95.7, mAP@50 of 95.6 and mAP@50:95 of 94.9, with only 5.12M parameters and 17.1 GFLOPs. In comparison to the current benchmarks, IEMA-YOLOv11 was superior to YOLOv8-n by a margin of 2.4% in mAP@50, which has a potential to be used on a large scale to detect brinjal disease in a sustainable manner.</p>

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Lightweight attention enhanced YOLOv11 for accurate multi class detection of brinjal diseases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月11日

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  • Athiraja A,
  • Sujatha E,
  • Gurulakshmi A B,
  • Nagarajan S

摘要

The cultivation of brinjal is seriously affected by various pathogens which affect crop yield and quality hence the importance of automated disease detection systems in precision agriculture. The conventional image-based diagnostic methods fail to differentiate between similar diseases classes that look similar and this results in misdiagnosis and poor crop management. This research paper advances to suggest a lightweight deep learning model, IEMA-YOLOv11 (Improved Efficient Multi-Scale Attention-based YOLOv11), to successfully identify and detect Brinjal disease successfully in real-time. The model combines an EMA (Efficient Multi-Scale Attention) system, IRMB (Inverted Residual Mobile Blocks), and a LDFM (Local Detail Feature Module) to obtain fine-grained lesion features, as well as a refined MPDIoU loss to achieve a better object localization. The framework was tested against a curated multi-class brinjal disease data of six fungal, two bacterial, three viral and one nematode infection. IEMA-YOLOv11 had a precision of 96.5, a recall of 95.7, mAP@50 of 95.6 and mAP@50:95 of 94.9, with only 5.12M parameters and 17.1 GFLOPs. In comparison to the current benchmarks, IEMA-YOLOv11 was superior to YOLOv8-n by a margin of 2.4% in mAP@50, which has a potential to be used on a large scale to detect brinjal disease in a sustainable manner.