1-Norm twin random vector functional link networks based on Universum data for leaf disease detection

Abstract

Due to rapid climate change and man-made activities, the types of leaf diseases are gradually increasing. As a result, taking the essential measures to recognize and diagnose the problem becomes a top priority. Though manual techniques for identifying leaf disease have been used in the past, research-based studies may produce a better result and overcome the disadvantages of the manual process. Nowadays, the random vector functional link (RVFL) network gaining popularity day by day due to the better generalization ability for various applications. In the spirit of the popular twin support vector machine (TSVM) and the twin variant of the random vector functional link (TRVFL) network, a novel twin RVFL (TRVFL1norm) is suggested in this work. TRVFL1norm overcomes the inherent limitation of TRVFL, which loses the sparsity due to the use of 2 norm and also gives a high penalty to outlier points which leads to degrade the generalization performance. Moreover, it is well known that Universum data can be used as prior knowledge in the training model. Therefore, to make the model more robust and generalized, we propose another model, TRVFL1norm based on the Universum data (UTRVFL1norm). UTRVFL1norm possesses the advantages of both 1-norm as well as Universum data. For both TRVFL1norm and UTRVFL1norm, few of the components in the output matrix will be zero due to sparsity hence, the resultant classifier must depend on the lesser output nodes compared to TRVFL. To verify model’s performance, we have compared the classification performance of proposed TRVFL1norm and UTRVFL1norm models with support vector machine (SVM), Universum SVM (USVM), twin support vector machine (TSVM), Universum TSVM (UTSVM), random vector functional link (RVFL), multilayer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN) on few interesting benchmark datasets. Further, we have also checked the performance of TRVFL1norm and UTRVFL1norm for leaf disease detection as one of the possible applications. The experimental results indicate the efficiency and applicability of the proposed models.