Abstract

Potato, a globally significant food crop ranking as the fourth largest by production, is cultivated in various regions worldwide. However, potato crops are notably susceptible to fungal infections, leading to the occurrence of early blight and late blight diseases. Timely disease control and management measures are pivotal in augmenting crop yields and mitigating agricultural losses for farmers. The capacity to automatically discern diseased crops holds substantial promise for farmers. Consequently, this research endeavors to present the power of transfer learning in SoTA Convolutional Neural Network (CNN) architecture ResNet-50v2 and DenseNet-201 which are finetuned to the task of potato disease detection. In our experimental endeavors, we employed three distinct datasets, and in each instance, we attained state-of-the-art results.