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GourNet CNN model achieves 97% accuracy in mango leaf disease detection

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed GourNet, a Convolutional Neural Network model designed to detect diseases in mango leaves. Trained on the MangoLeafBD dataset, which includes eight classes (seven diseases and one healthy), GourNet achieved a 97% classification accuracy. The model utilizes data augmentation and preprocessing techniques, and its source code has been made publicly available. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Potential for improved crop management and yield through early disease detection in mango cultivation.

RANK_REASON Academic paper detailing a new CNN model for a specific agricultural task.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Ekram Alam, Jaydip Sanyal, Akhil Kumar Das, Arijit Bhattacharya, Farhana Sultana ·

    GourNet: A CNN-Based Model for Mango Leaf Disease Detection

    arXiv:2604.27764v1 Announce Type: new Abstract: Mango cultivation is crucial in the agricultural sector, significantly contributing to economic development and food security. However, diseases affecting mango leaves can significantly reduce both the production and overall fruit g…

  2. arXiv cs.CV TIER_1 · Farhana Sultana ·

    GourNet: A CNN-Based Model for Mango Leaf Disease Detection

    Mango cultivation is crucial in the agricultural sector, significantly contributing to economic development and food security. However, diseases affecting mango leaves can significantly reduce both the production and overall fruit grade. Detecting leaf diseases at an early stage …

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