A Novel Approach for Enhancing Plant Leaf Classification With the Binary Cuckoo Search Algorithm

Computer vision plays a crucial role in current day tasks due to its wide range of merits and applications in many fields like disease diagnosis, medical decisions, military, security, scientific applications, and business. Identifying plant species upon their leaf images is a very challenging and complex task. In this study, we introduced a model for classifying a variety set of plant leaves using different techniques such as factorization machine (FM), dimensionality reduction (DR), and ensemble learning (EL). In our study, FM is used as a classification algorithm, whereas DR is included in two stages, namely, feature extraction (FE) and feature selection (FS). FE has been used to extract the features from images in tabular form; on the other hand, FS is used to reduce the size of the data by getting rid of noisy (redundant and irrelevant) features. In addition, we utilized two methods for FS, filter‐based and wrapper‐based approaches. The used filter was the minimum redundancy maximum relevance, and the used wrapper was the improved binary cuckoo search algorithm that is based on Lévy flight and abandon nest functions. Regarding EL, it was used to declare different versions of FM by passing different subsets of features that are deduced from the original ones to improve its performance. We used the Swedish and Flavia leaf datasets for training and testing phases. The proposed model achieved a high performance in terms of accuracy that produced 95.67% on Swedish dataset and 99.6% on Flavia dataset. According to the results and in comparison to other methods, we proved that our proposed model ensures a favorable plant leaf classification approach. Finally, the proposed wrapper FS approach showed very good results without setting up the number of features to be selected by the user. The entire implementation of this work can be found at https://github.com/Mohammed-Ryiad-Eiadeh/Binary-Cuckoo-Search-For-Plant-Leaf-Prediction .