Pomegranate Fruit Disease Detection Using Yolov11

• Author(s):

  Chintamani Adak | Anish Chaudhari | Dinesh Sabale | Vikram Mugale

• Keywords:

  Agriculture, Deep Learning, Object Detection, Pomegranate Disease, YOLOv11

• Abstract:

  Early Detection Of Pomegranate Diseases Matters Because Catching Them Late Means Lost Yield And Real Financial Damage. This Paper Describes A Disease Detection System Built On YOLOv11, Which Identifies Bacterial Blight And Fungal Infections Directly From Fruit Images. The Model Achieves 99.2% Precision, 99.1% Recall, And 99.5% MAP@50 On Our Validation Set, While Running At Over 220 FPS On GPU Hardware—demonstrating Strong Suitability For Real-world Agricultural Deployment.

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Other Details

• Paper id:

  IJSARTV12I5105252

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-04

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