[2603.13354] AgriPath: A Systematic Exploration of Architectural Trade-offs for Crop Disease Classification

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.13354 (cs) [Submitted on 8 Mar 2026 (v1), last revised 8 Apr 2026 (this version, v3)] Title:AgriPath: A Systematic Exploration of Architectural Trade-offs for Crop Disease Classification Authors:Hamza Mooraj, George Pantazopoulos, Alessandro Suglia View a PDF of the paper titled AgriPath: A Systematic Exploration of Architectural Trade-offs for Crop Disease Classification, by Hamza Mooraj and 2 other authors View PDF HTML (experimental) Abstract:Reliable crop disease detection requires models that perform consistently across diverse acquisition conditions, yet existing evaluations often focus on single architectural families or lab-generated datasets. This work presents a systematic empirical comparison of three model paradigms for fine-grained crop disease classification: Convolutional Neural Networks (CNNs), contrastive Vision-Language Models (VLMs), and generative VLMs. To enable controlled analysis of domain effects, we introduce AgriPath-LF16, a benchmark of 111k images spanning 16 crops and 41 diseases with explicit separation between laboratory and field imagery, alongside a balanced 30k subset for standardised training and evaluation. We train and evaluate all models under unified protocols across full, lab-only, and field-only training regimes using macro-F1 and Parse Success Rate (PSR) to account for generative reliability (i.e., output parsability measured via PSR). The results reveal distinct...