Context Data Fusion Model Enlightened Multi-Scale Capsule Network for Fruit Diseases Identification

Abstract

Pests and diseases of tomato fruits are the foremost factors leading to the reduction in produce quantity. Effective and prompt differentiation of these lesion symptoms is essential for agricultural production. Due to the lack of a global receptive field, commonly used convolution neural network models restrict the capability on tomato images under complicated backgrounds. In this paper, we present an efficient and robust method for field diagnosis of tomato fruits based on the context data fusion and capsule network. First, in order to make better the point of inadequate images, the Cycle-GAN algorithm is used to realize the augmentation of the diseases dataset. To improve the quality of the generated images, the feature reconstruction loss function is proposed for the Cycle-GAN method, named Fruit-GAN. The proposed Fruit-GAN learns features of healthy and diseased tomatoes, respectively, and forms lesions based on healthy tomato fruit images. Adding lesion tomato images produced on Fruit-GAN to training sets improves the diagnostic capability compared to classical data augmentation techniques. Subsequently, context fusion studies were used to construct intermediate feature maps from different layers into the fruit diseases recognition compact representation to efficiently leverage the feature reuse mechanism. Finally, the fusion model’s enlightened multi-scale capsule network is integrated to form deep features and determine the disease diagnosis. After analyzing the hyper-parameters, the integral technique provides 96.16% accuracy. This research of tomato diseases diagnosis demonstrates the context capsule network model is efficient and establishes a foundation for tomato fruits determination in natural growing environment.