A Comprehensive Survey on Machine Learning and Deep Learning Approaches for Potato Leaf Disease Detection

Deep Learning Techniques for Citrus Disease Detection: A Comprehensive Review

Citrus crops are economically vital worldwide, yet they remain highly susceptible to a range of infectious diseases that cause considerable yield and quality losses each year. Early and accurate disease identification is fundamental to sustainable orchard management and food security. Over the past decade, deep learning has emerged as the dominant paradigm for automated plant disease detection, surpassing traditional image-processing pipelines in both accuracy and scalability. This paper presents a comprehensive review of deep learning methodologies applied to citrus disease detection, covering convolutional neural networks (CNNs), attention mechanisms, lightweight architectures, object detection frameworks, multimodal fusion, and edge-computing deployment. Recent studies are critically analyzed with respect to model architecture, dataset characteristics, performance metrics, and deployment context. The review identifies prevailing trends including the shift toward lightweight models for edge devices, the integration of attention modules for fine-grained feature capture, and the growing adoption of multimodal and transformer-based approaches. Key open challenges such as limited data diversity, computational constraints in field deployments, and the need for domain-adaptive models are also discussed, along with prospective research directions. The findings serve as a reference for researchers and practitioners seeking to develop robust, real-time citrus disease detection systems.

Deep Learning Architectures for Fruit and Leaf Disease Detection: A Critical Analysis of Methods, Metrics, Challenges, and Future Research Directions

A complete assessment framework is presented that goes beyond traditional accuracy-based metrics and includes analysis related to confidence calibration and temporal consistency, as well as out-of-distribution robustness and adversarial stability, as well as out-of-distribution robustness and adversarial stability.

Deep Learning for Plant Disease Detection: A Systematic Review

It is suggested that in order to be implementable in the field, future intelligent agricultural diagnosis systems must be able to balance predictive accuracy, explainability, computational efficiency and field adaptability.

EARTHARXIV PREPRINT COVERSHEET Explainable Deep Learning Techniques for Potato Leaf Disease Detection in Smart Agriculture

A comparative study of deep learning models for tomato disease detection

Explainable Transfer Learning for Multi-Crop Plant Disease Identification Under Real-Field Conditions

Two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture are presented, showing that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model.