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This is a Quantitative Study study.

October 5, 2025Plant MethodsOpen Access

Optimized deep learning framework for pomegranate disease detection using nature-inspired algorithms

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Authors

ASAnil SandhiPunjab Technical University RKRajeev KumarThe Sanskrit College and University RBReeta BhardwajDAV University

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Implication

This framework integrates deep learning and optimization to enhance disease detection accuracy in pomegranate crops, suggesting significant reductions in losses.

Key Points

The framework achieved 99.10% accuracy using a Multi-Layer Perceptron classifier, indicating superior disease detection.
With dual-stream feature processing and Hybrid Genetic Algorithm-Particle Swarm Optimization, dimensionality was reduced by up to 70%.
5-fold cross-validation was employed, highlighting the model's robustness against environmental variability and noise.
This automated approach supports precision agriculture by enabling early disease intervention and reducing economic losses.

Cite This Study

Sandhi et al. (2025) studied this question.

synapsesocial.com/papers/68e28310dcef4a166ce03c56 https://doi.org/10.1186/s13007-025-01447-9

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Implication

This framework integrates deep learning and optimization to enhance disease detection accuracy in pomegranate crops, suggesting significant reductions in losses.

Optimized deep learning framework for pomegranate disease detection using nature-inspired algorithms

Discussion

Member takes

Implication

Key Points

Cite This Study

Optimized deep learning framework for pomegranate disease detection using nature-inspired algorithms

Discussion

Member takes

Implication

Key Points

Cite This Study

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