TL;DR
A comprehensive and systematic review of state-of-the-art methods for detecting potato leaf disease, covering convolutional neural networks, transformer-based architectures, and hybrid models, and a strategic comparative analysis is conducted.
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 learnin...
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.
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.
An explainable deep learning-based framework for multi-class potato leaf disease detection using real-world field images collected from agricultural environments in Bangladesh is proposed and aims to enhance diagnosis accuracy, reduce crop losses, minimize unnecessary pesticide use, and promote smart farming practices.
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.
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