Harnessing the Power of Deep Neural Networks for Accurate Leaf Disease Identification

Abstract

Many countries around the world depends on agriculture, as it helps reduce poverty, increase national income, and improve food security. However, plant diseases often impact food crops, leading to significant annual losses and economic setbacks in agriculture. The best solution of the problem is to identify the plant disease as soon as possible so that necessary steps can take. Traditionally, humans have identified plant diseases visually, but this method is often slow, and also the number of domain experts are less. Recently, there has been significant progress in using deep learning to classify plant diseases. However, the main problem is to collect the sufficient annotated image data to train these models effectively for plant disease classification. Also the limited training data can negatively affect the performance of CNN models. To address this, we designed a Deep Convolutional Generative Adversarial Network (DCGAN) to overcome the issues of over-fitting and to increase the dataset sizes. Here we worked on the dataset called DiaMOS Plant dataset, consisting of 3006 images of pear leaves of four classes (3 diseases and one healthy class). The dataset was very imbalanced, so we used DCGAN on the minority classes separately to enhance the dataset. We developed some CNN models for classification and compared with some Pre-trained models (VGG16, ResNet50, Inception V3). The results showed an average increment of classification accuracy.