Image Based Disease Classification in Grape Leaves Using Convolutional Capsule Network

Abstract Crop protection is the prime hindrance for food security. The plant diseases destroy the overall quality and quantity of the agricultural products. Grape is an important fruit and major source of vitamin C nutrients. The automatic decision making system plays a paramount role in agricultural informatics. This paper aims to detect the diseases in grape leaves using convolutional capsule networks. The capsule network is a promising neural network in the field of deep learning. This network uses a group of neurons as capsules and effectively represents spatial information of features. The novelty of the proposed work relies on the addition of convolutional layers before the primary caps layer which indirectly decrease the number of capsules and speed up the dynamic routing process. The proposed method is experimented with augmented and non-augmented datasets. It effectively detects the diseases of grape leaves with the accuracy of 99.12%. The performance of the method is compared with state-of-the art deep learning methods and produces reliable results.