Optimized Deep Learning Model for Disease Prediction in Potato Leaves
Virendra Kumar Shrivastava, Chetan J. Shelke, Aastik Shrivastava, Sachi Nandan Mohanty, Nonita Sharma
Abstract
Food crops are important for nations and human survival. Potatoes are one of the most widely used foods globally. But there are several diseases hampering potato growth and production as well. Traditional methods for diagnosing disease in potato leaves are based on human observations and laboratory tests which is a cumbersome and time-consuming task. The new age technologies such as artificial intelligence and deep learning can play a vital role in disease detection. This research proposed an optimized deep learning model to predict potato leaf diseases. The model is trained on a collection of potato leaf image datasets. The model is based on a deep convolutional neural network architecture which includes data augmentation, transfer learning, and hyper-parameter tweaking used to optimize the proposed model. Results indicate that the optimized deep convolutional neural network model has produced 99.22% prediction accuracy on Potato Disease Leaf Dataset.