Deep Learning for Early Detection and Prediction of Emerging Plant Diseases

Abstract

Emerging plant diseases and invasive agricultural pests pose escalating threats to global food security, crop productivity, and economic stability, exacerbated by climate change-driven shifts in pathogen transmission dynamics. Traditional visual inspection methods are inherently reactive, subjective, and inadequate for pre-symptomatic detection, often resulting in delayed interventions and excessive pesticide application. This comprehensive review examines the transformative role of deep learning architectures in advancing early detection and prediction of plant diseases. We systematically analyze the evolution from foundational Convolutional Neural Networks (CNNs) to sophisticated Vision Transformers (ViTs) and hybrid CNN-Transformer architectures, evaluating their diagnostic performance, architectural innovations, and deployment suitability across agricultural contexts. Critically, we explore multimodal sensing modalities beyond conventional RGB imaging, including hyperspectral imaging for pre-visual biomarker detection, infrared thermography for vascular stress assessment, and environmental microclimate integration for predictive forecasting. The review synthesizes current advances in multimodal fusion architectures, vision-language foundation models, and few-shot learning paradigms that address persistent challenges of data scarcity, domain shift, and the pervasive "lab-to-field" performance degradation. We examine edge deployment strategies, model optimization techniques including INT8 quantization, and explainable AI frameworks essential for practical agricultural adoption. Benchmark datasets are critically assessed for their limitations and generalization dynamics. Key findings demonstrate that integrated multimodal deep learning systems incorporating spatial-spectral-temporal data fusion can achieve proactive detection 5-7 days pre-symptomatically, enabling targeted interventions that reduce prophylactic fungicide application by up to 41%. Despite remarkable progress, significant challenges remain in bridging generalization gaps, developing robust field-deployable systems, and establishing standardized evaluation protocols for real-world agricultural environments.