Potato Plant Leaf Diseases Identification Using Transfer Learning

Abstract

Background/Objectives: Agriculture is a major food source for Ethiopian population. Plant diseases contribute a great production loss, which can be addressed with continuous monitoring. Early plant disease identification using computer vision and Artificial Intelligence (AI) helps the farmers to take preventive course of action to increase production quality. Manual plant disease identification is strenuous and error-prone. Methods: In this study, we present a convolutional neural network architecture inception-v3 model to detect potato leaf diseases using a deep learning-based transfer learning technique. We used separable convolution in the inception block that can minimize the number of parameters by an outsized margin and to utilize resource efficiently. The inception-V3 model have a higher training accuracy and needs less training time than the main CNN architecture, as the used parameters are fewer. Findings: In this study, there is an improvement on the little noisy on sample images which leads to misidentification of diseases. In our experiment, we have used an RGB color channel image dataset to train model, which yields an overall accuracy performance of 98.7% on the heldout test set. Novelty: In order to identify potato leave diseases, we conducted transfer learning for high performance classification with pixel-wise operation to enhance the number of leaf images. A model based on inception-v3 transfer learning approach is presented in this study for disease identification of potato leave images, thus provide an effective computer-aided recognition model for potato disease classification in the absence of large data.

An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification

“…The EfficientNet acquired better accuracy at 98%. Birhanu Gardie et al [11] identify potato disease from leaf images using the transfer learning method. The comparison of different model accuracies is shown in this work using the same dataset where InceptionV3 acquired the best accuracy at 98.7%.…”