An Early Recognition Approach for Okra Plant Diseases and Pests Classification Based on Deep Convolutional Neural Networks

Abstract

The issue of effective plant disease and pest prevention is compactly connected to the issues of sustainable agronomics and climate change. Okra plant diseases and pests cause intense monetary losses to the growing okra industry, but their accurate and rapid identification remains troublesome due to the lack of efficient approaches. This paper addresses an early recognition approach for controlling the disease and pest spread to ensure quality production of okra. At first, a dataset of fifteen classes is generated from 12476 collected images using nine image augmentation techniques which contains 124760 images of okra plant diseases and pests. Afterwards, state-of-the-art deep learning models such as InceptionResNetV2, Xception, ResNet50, MobileNetV2, VGG16, and AlexNet were utilized with the transfer learning approach. InceptionResNetV2 showed significant performance compared to others, achieved 98.73% and 98.16% accuracy under the training set of 99808 images, and the test set of 6236 images of the used dataset, respectively.