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

Anil Sandhi¹, Rajeev Kumar², Reeta Bhardwaj¹

¹DAV Institute of Engineering and Technology, Jalandhar, Punjab, India.

|October 4, 2025

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This summary is machine-generated.

This study introduces an advanced AI framework for accurate pomegranate disease detection, significantly improving early intervention in agriculture. The optimized model enhances crop yield prediction and reduces economic losses from plant pathogens.

Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Crop diseases pose a significant threat to global food security and agricultural economies.
Pomegranate cultivation faces substantial yield losses (20-40%) due to various pathogens.
Current disease detection methods are labor-intensive, subjective, and lack efficiency, while existing AI models struggle with real-world environmental variations.

Purpose of the Study:

To develop an automated, robust, and computationally efficient framework for detecting pomegranate diseases.
To enhance the accuracy and reliability of plant disease identification in agricultural settings.
To overcome the limitations of traditional methods and existing deep learning models in field conditions.

Main Methods:

Keywords:

Computer vision Deep learning Genetic algorithm Particle swarm optimization Pomegranate fruit disease Resnet101

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Integration of a modified ResNet101 architecture with a Hybrid Genetic Algorithm-Particle Swarm Optimization (HGA-PSO).

Dual-stream image processing utilizing original and noise-augmented (Gaussian, salt-and-pepper, speckle) images for enhanced robustness.

Feature fusion and dimensionality reduction (50-70%) using HGA-PSO to maintain discriminative power.

Main Results:

Achieved 99.10% accuracy, a perfect 1.00 ROC-AUC score, and high precision-recall metrics on a 5,000-image dataset across five classes.
Demonstrated near-zero misclassification through confusion matrices and strong generalization in real-world tests.
Outperformed existing methods like PSO-YOLOv8 (98.86%) and Transformer models (93.13%) in key performance indicators.

Conclusions:

The developed framework offers a scalable and optimized solution for early pomegranate disease detection.
The combination of deep learning and nature-inspired optimization significantly improves robustness against environmental variability and reduces computational load.
Enables precision agriculture by facilitating timely disease intervention, thereby mitigating economic losses and improving crop management.