# A review of deep learning architectures for plant disease detection

## TL;DR

This review summarizes how deep learning methods, like CNNs and object detectors, are used to detect plant diseases through images, highlighting challenges and solutions for real-world applications.

## Contribution

The paper systematically reviews DL architectures and strategies for plant disease detection, emphasizing interpretability and real-time deployment solutions.

## Key findings

- CNN architectures like VGG, ResNet, and EfficientNet are effective for plant disease classification.
- XAI techniques such as Grad-CAM improve model interpretability in plant pathology.
- Edge computing and synthetic data generation help address computational and data challenges.

## Abstract

The rapid advancement of deep learning (DL) has revolutionized plant disease detection by enabling highly accurate, image-based diagnostic solutions. This review provides a comprehensive synthesis of DL-based methodologies for plant disease detection, systematically structured around the key stages of the modeling pipeline, encompassing data acquisition, preprocessing, augmentation, classification, detection, segmentation, and deployment.

The review focuses on evaluating convolutional neural network (CNN) architectures such as VGG, ResNet, EfficientNet, and DenseNet across diverse experimental contexts. Classification strategies are categorized according to their integration of visualization techniques (e.g., saliency maps, Grad-CAM) to enhance model interpretability, emphasizing the pivotal role of explainable artificial intelligence (XAI) in plant pathology. Object detection models are systematically examined within both one-stage (YOLO, SSD) and two-stage (Faster R-CNN) paradigms. Furthermore, critical challenges—such as environmental variability, data imbalance, and computational constraints—along with potential solutions including transfer learning, synthetic data generation using generative adversarial networks (GANs) and diffusion models, and edge computing for real-time deployment, are comprehensively discussed.

This review summarizes best practices for dataset selection and model optimization for mobile platforms, emphasizing their role in improving the efficiency and accuracy of plant disease detection systems.

Deep learning-based methods show strong potential to enhance precision and resilience in real-world plant disease detection and monitoring.