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Volume 12 Issue 07
July 2026
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Author(s)
Chintamani Adak Anish Chaudhari Dinesh Sabale Vikram Mugale
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.
Keywords
Agriculture
Deep Learning
Object Detection
Pomegranate Disease
YOLOv11
Paper ID
IJSARTV12I5105252
Publication Date
May 4, 2026
Research Area
Computer Engineering