Improving the Accuracy of Prediction of Plant Diseases Using Dimensionality Reduction-Based Ensemble Models

Abstract

In many real-world applications, different features can be obtained and how to duly utilize them in reduced dimension is a challenge. Simply concatenating them into a long vector is not appropriate because each view has its specific statistical property and physical interpretation. Many dimensionality reduction methods have been developed to identify this lower-dimensional space and map data to it, reducing the number of predictors in supervised learning problems and allowing for better visualization of data relations and clusters. However, the plethora of dimensionality reduction techniques provides a variety of nonlinear, linear, global, and local methods, and it is likely that each method captures different data features. Ensemble methods have achieved much success in supervised learning, from Random Forest to AdaBoost. Ensembles exploit diversity and balance bias, variance, and covariance to achieve these results is likely that disparate dimensionality reduction methods will enhance diversity within a dimensionality reduction-based ensemble. AdaBoost and Random Forest are popular ensemble methods which are widely used for classification of target variables. Major problem with ensembles like AdaBoost and Random Forest is that they perform worse when dimensionality of data is high. Random Forest is the predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. The proposed research work aims to improve the performance of classification tasks on diseased plants by exploring t-distributed Stochastic Neighbor Embedding (t-SNE) based Ensemble Models. The infected and healthy plant images are subjected to deep learning model to produce their corresponding image embedding. The high dimensional data with thousands of features is then reduced to a smaller number of features dataset by the state-of-the-art t-SNE algorithm. The significant feature dataset is then given as input to the ensemble models to perform the prediction task.