Quantum-Optimized Multi-Attention Framework for Crop Disease Detection in Resource-Constrained Environments

This study addresses the need for accurate crop disease detection in African agricultural systems through a quantum-optimized multi-attention neural network framework. Traditional machine learning approaches fail to capture the complex interactions between plant species, disease manifestations, and pathogen characteristics that determine diagnostic outcomes. We developed a multi-output classification system that simultaneously performs four diagnostic tasks: disease identification (33 classes), binary health classification (healthy/diseased), plant species recognition (9 species), and pathogen type detection (4 categories). The framework integrates quantum-inspired optimization with multi-head attention mechanisms to efficiently explore the 15-dimensional hyperparameter space using a quantum-enhanced Artificial Bee Colony algorithm with 10 artificial bees in three specialized roles. The system achieved 95.24% AUC for disease classification, 92.89% accuracy for binary classification, 86.36% accuracy for plant species recognition, and 65.90% accuracy for pathogen type detection on the PlantVillage dataset. These results demonstrate that quantum-optimized attention mechanisms improve multi-task agricultural disease detection compared to traditional approaches, providing farmers with diagnostic capabilities for resource-constrained environments. Technical Report Series: OBEN-TR-2025-001 Institution: OBEN IT Solutions Corresponding Author:Emmanuel Oben  Website: obenitsolutions.comthis is the first publication in the OBEN IT Solutions Technical Report series. The research addresses food security challenges in African agricultural systems through AI-powered crop disease detection. Dataset: PlantVillage (publicly available at github.com)Programming Language: Python 3.xFramework: TensorFlow 2.19.0 Hardware: Google Colab with NVIDIA L4/T4 GPU For collaboration inquiries or questions about methodology, please contact Our Research Team