Deep Learning for Early Detection of Crop Pathogens: A Multimodal Fusion Framework Leveraging Hyperspectral Imaging and Climate Data in Precision Agriculture

Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases

Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.

A Novel Hyperspectral Imaging based Framework for Tomato Leaf Disease Detection

A lightweight deep learning model, the Dual Multi-Layer Perceptron Feature Fusion Network (DMLPFFN) as a hyperspectral-based approach to the four tomato disease stages, which includes healthy, asymptomatic, early and late infection is demonstrated.

Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies

Accurate and quick detection of plant leaf diseases is essential for precision agriculture to intervene promptly and boost crop yields. A new deep learning model called ResVNet has been introduced in this study. It combines the powerful local feature detection of ResNet152 with the global attention capabilities of Vision Transformer (ViT) and utilises Low-Rank Adaptation (LoRA) to accelerate fine-tuning. The PlantVillage dataset, which contains both healthy and diseased tomato samples, was used to train and test ResVNet. Experimental evaluation on the PlantVillage tomato dataset using stratified 5-fold cross-validation demonstrates that the proposed ResVNet model achieves a mean classification accuracy of 97.45%, along with superior macro-precision, macro-recall, and macro-F1 scores compared to existing deep learning architectures. The results of the confusion matrix and the ROC analysis validate its discriminatory power. The results highlight the potential of architectures strengthened with transformers in agricultural diagnostics. For real-time disease detection in the field, ResVNet is perfect for edge device deployment on drones and smartphones thanks to its high accuracy and adaptability. The application of Explainable AI (XAI) technologies for interpretability, integration with the Internet of Things (IoT), and multi-crop classification will all be explored in future studies. We will also look into model compression approaches so we can deploy efficiently in low-resource settings without sacrificing performance.

Multimodal Contextual Learning for Maize Disease Diagnosis: Fusing Visual and Environmental Data

Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV) threaten food security in Zimbabwe, yet traditional image-based deep learning models lack adaptability to varying agro-environmental conditions. This study presents a multimodal contextual learning framework that fuses visual symptoms with environmental data to improve diagnostic accuracy and interpretability. A multimodal Convolutional Neural Network integrates leaf imagery with six environmental parameters (temperature, humidity, rainfall, soil moisture, leafhopper count, days after planting) using a late-fusion architecture with multi-task learning for simultaneous disease classification and five-stage severity estimation, enhanced by Gradient-weighted Class Activation Mapping (Grad-CAM) for visual explanation. A synthesized dataset (n=9,356) representative of Zimbabwean conditions was used. The multimodal approach achieved 94.3% accuracy versus 91.5% for image-only baselines (p<0.001, 95% CI), a 33.2% relative error reduction, with MSV-MLN confusion decreasing by 40.7% (113 to 67 misclassifications). Multi-task performance reached 94.5% for classification and 81.5% for severity estimation (F1=0.805). Grad-CAM analysis revealed environmental integration enhanced attention by 38.1% and localization by 45.2%, with leafhopper count showing the strongest correlation (r=0.62). Ablation studies confirmed that environmental features provided the largest accuracy gain (+3.71%, p<0.001). This work demonstrates that integrating environmental context with visual symptoms enhances diagnostic accuracy and model interpretability, establishing a foundational proof-of-concept for context-aware agricultural AI.

A Hybrid Transfer Learning Framework for Corn Crop Detection Using Deep Convolutional Networks

Corn is a staple crop of global significance; however, foliar diseases may lead to 30–60% yield loss if not detected at an early stage. Conventional visual inspection is time-consuming, subjective, and difficult to scale for smallholder farmers worldwide. To overcome these issues, we present Corn Transfer Learning Network (CTL-Net), an end-to-end hybrid deep learning model for corn leaf disease identification. CTL-Net, which combines Inception-ResNet-v2 as the backbone and MobileNetV3 as a feature extractor in parallel convolutional streams, simultaneously learns diverse scales of texture information from low-level textures, mid-level structural patterns, and high-level disease semantics of RGB leaf images. Adaptive feature fusion is formulated through learnable weighting coefficients and bi-directional spatial–channel attention mechanisms, which further enhance feature discriminability and robustness. The proposed approach is tested on an extensive dataset of 12,456 images from 10 corn diseases, including Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Cercospora Leaf Spot, acquired under controlled and real-field conditions. CTL-Net attains the highest classification accuracy of 99.42%, outperforming DenseNet121 (97.92%), EfficientNetB3 (97.35%), and general stacking models (97.89%). Robustness experiments demonstrate the effectiveness of the proposed method against illumination variations, additive noise, and partial occlusions. CTL-Net enables real-time inference with a latency of 42 ms on an NVIDIA RTX 3090 GPU. Gradient-weighted Class Activation Mapping++ (Grad-CAM++)-based interpretability analysis results in a mean Intersection over Union (IoU) of 87.6% with expert-annotated disease regions. Five-fold cross-validation, ablation studies, and statistical significance testing (p < 0.001) demonstrated that the contribution of each architectural component is significant. To summarize, CTL-Net allows accurate and interpretable predictions in a computationally efficient manner, making it an excellent candidate for mobile and resource-limited applications in precision agriculture.

Enhanced tomato leaf disease detection using multi-modal deep-handcrafted feature fusion with optimization-driven learning techniques