Deep Learning Approaches for Disease Detection Based on Plant Leaf Image: A Review

The demand for rapid and precise plant disease diagnostics has increased with interest in environmentally friendly farming techniques. Deep learning algorithms are employed in image analysis has shown significant promise for the quick and precise detection of plant diseases. Early detection of diseases in plant is crucial. Yet conventional approaches have their limits, as is discussed at the article’s outset. This review article of the relevant literature sums up the state of the art in identifying plant disease and different deep learning methods, such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, which have been applied to the problem of plant disease identification. Several datasets that have been used to train and evaluate deep learning models for identifying plant diseases are also discussed in the paper. Finally, this study wraps up with a discussion of the problems and future possibilities in this subject, including the need to create more reliable and interpretable models depend on deep learning and the integration of several detection paradigms for more precise disease identification.