Crop disease analysis through hyperspectral images using deep learning models

Abstract

Modern precision agriculture increasingly relies on high-resolution Unmanned Aerial Vehicle (UAV) hyperspectral imagery to map diverse vegetation species and monitor complex crop health. However, processing these massively high-dimensional data cubes has historically required classical deep learning models with unsustainable computational bloat, such as heavy vision transformers or extremely deep convolutional networks. Furthermore, standard optimization pipelines routinely collapse when confronted with the complicated structural complexities of real-world agricultural datasets, which naturally feature severe class imbalances and highly overlapping spatial boundaries. This thesis directly addresses these critical computational and mathematical vulnerabilities by engineering ultra-lightweight, parameter-efficient hybrid quantum-classical architectures. By entirely replacing massive classical dense layers with a parameterized 4-qubit variational quantum circuit, this research demonstrates that quantum mechanics can natively and efficiently synthesize the highly complex, non-linear global dependencies required for accurate field classification. To overcome the distinct spatial and spectral challenges of agricultural data, this work introduces two novel evolutionary frameworks. The first, the Quantum Patch-Graph Transformer (QPGF), mathematically preserves orthogonal crop row geometry by structuring spatial patches into row-normalized 4- nearest neighbor graphs, seamlessly fusing local graph attention with quantum global feature extraction. The second methodology is the Quantum Enhanced CNN-BiSpectralMamba-Quantum architecture, which actively bypasses standard memory bottlenecks by utilizing bidirectional Mamba state-space models to aggressively process continuous spectral sequences at linear complexity. Both architectures are stabilized by a custom Hybrid Cross-Entropy and Log-Cosh Dice loss function. This highly specialized optimization pipeline strictly forces the networks to penalize dominant staple crops and accurately map the topological boundaries of rare, minority vegetation. Rigorous empirical validation on the highly imbalanced, 200-band, 30-class UAV-HSI-Crop dataset proves the absolute efficacy of these hybrid designs. The classical-quantum fusion drastically reduced the total trainable parameter count compared to state-of-the-art classical benchmarks. Despite this incredibly lightweight computational footprint, the QPGF established a robust baseline of 81.92% overall accuracy, while the advanced Quantum Enhanced CNN-BiSpectralMamba achieved a highly competitive peak of 84.83% overall accuracy and 82.07 kappa score. Ultimately, this thesis proves that fusing targeted classical spatial-sequence extractors with quantum state entanglement provides a mathematically elegant and resource efficient diagnostic engine for the future of precision agriculture.