Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network

• Main Authors: Punam Bedi, Pushkar Gole
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2021-01-01
• Series: Artificial Intelligence in Agriculture
• Subjects: Plant disease detection; Convolutional autoencoder; Convolutional neural network; Deep learning in agriculture
• Online Access: http://www.sciencedirect.com/science/article/pii/S2589721721000180

Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of the most challenging problems in agriculture. If the diseases are not identified in the early stages, then they may adversely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many researchers have presented different state-of-the-art systems based on Deep Learning and Machine Learning approaches. However, most of these systems either use millions of training parameters or have low classification accuracies. This paper proposes a novel hybrid model based on Convolutional Autoencoder (CAE) network and Convolutional Neural Network (CNN) for automatic plant disease detection. To the best of our knowledge, a hybrid system based on CAE and CNN to detect plant diseases automatically has not been proposed in any state-of-the-art systems present in the literature. In this work, the proposed hybrid model is applied to detect Bacterial Spot disease present in peach plants using their leaf images, however, it can be used for any plant disease detection. The experiments performed in this paper use a publicly available dataset named PlantVillage to get the leaf images of peach plants. The proposed system achieves 99.35% training accuracy and 98.38% testing accuracy using only 9,914 training parameters. The proposed hybrid model requires lesser number of training parameters as compared to other approaches existing in the literature. This, in turn, significantly decreases the time required to train the model for automatic plant disease detection and the time required to identify the disease in plants using the trained model.