Improved Diagnosis Accuracy Using Machine Learning for Plant Diseases

In agricultural fields, leaf diseases are a prevalent problem, and they often result in significant crop losses. The early discovery of leaf diseases is critical for reducing the amount of crop damage caused by the disease and preventing its further spread. Deep learning methods, such as convolutional neural networks (CNN), have shown a lot of potential in recent years for accurately diagnosing leaf diseases. In this article, we present a literature review on the use of CNNs for the detection of leaf diseases. We discuss the various architectures and techniques used to increase the accuracy of disease detection. In addition, we go over the datasets that are used for training and testing CNN models, as well as the challenges and potential future possibilities for the application of CNNs to the detection of leaf diseases.