FloraSyntropy-Net: Scalable Deep Learning with Novel FloraSyntropy Archive for Large-Scale Plant Disease Diagnosis

TL;DR

FloraSyntropy-Net introduces a scalable federated learning framework utilizing a large, diverse plant disease dataset, achieving high accuracy and demonstrating strong generalization across different plant species and datasets.

Contribution

The paper presents FloraSyntropy-Net, a novel federated learning approach with a new dataset, a Memetic Algorithm for model selection, and a Deep Block for improved feature extraction, advancing large-scale plant disease diagnosis.

Findings

Achieved 96.38% accuracy on FloraSyntropy benchmark.

Demonstrated 99.84% accuracy on unrelated Pest dataset.

Established a comprehensive large-scale plant disease dataset with 178,922 images.

Abstract

Early diagnosis of plant diseases is critical for global food safety, yet most AI solutions lack the generalization required for real-world agricultural diversity. These models are typically constrained to specific species, failing to perform accurately across the broad spectrum of cultivated plants. To address this gap, we first introduce the FloraSyntropy Archive, a large-scale dataset of 178,922 images across 35 plant species, annotated with 97 distinct disease classes. We establish a benchmark by evaluating numerous existing models on this archive, revealing a significant performance gap. We then propose FloraSyntropy-Net, a novel federated learning framework (FL) that integrates a Memetic Algorithm (MAO) for optimal base model selection (DenseNet201), a novel Deep Block for enhanced feature representation, and a client-cloning strategy for scalable, privacy-preserving training. FloraSyntropy-Net achieves a state-of-the-art accuracy of 96.38% on the FloraSyntropy benchmark. Crucially, to validate its generalization capability, we test the model on the unrelated multiclass Pest dataset, where it demonstrates exceptional adaptability, achieving 99.84% accuracy. This work provides not only a valuable new resource but also a robust and highly generalizable framework that advances the field towards practical, large-scale agricultural AI applications.