Deep learning ensemble boosts plant disease classification accuracy

By PulseAugur Editorial · [1 sources] · 2026-05-15 15:37

Researchers have developed AgriMind, an ensemble deep learning framework designed to automate plant disease classification. This system combines three models—ResNet50, EfficientNet-B0, and DenseNet121—trained on over 20,000 images of pepper, potato, and tomato plants. The ensemble achieved a 99.23% accuracy, significantly reducing the error rate compared to individual models, and demonstrates efficient processing speeds on a GPU. AI

IMPACT Automates plant disease detection, potentially improving agricultural yields and efficiency for farmers.

RANK_REASON The cluster contains an academic paper detailing a new deep learning framework for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Fahima Haque Talukder Jely · 2026-05-15 15:37

AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification

Plant disease detection is still largely manual in Bangladesh, where extension workers eyeball leaf samples across millions of smallholdings. We built AgriMind to automate this: an ensemble of ResNet50, EfficientNet-B0, and DenseNet121 trained on 20,638 PlantVillage images across…

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AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification

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