Optimized featureset in classification of plant leaves images using machine learning models

Abstract

Saving the earth becomes the utmost priority and responsibility of any individual. Environmental and ecosystem health assessment studies require precision farming, enabling early identification of diseases and optimizing crop management. Automatic plant leaf detection will serve as one of the crucial contributions towards biodiversity research. The proposed work provides an optimized feature set in the classification of plant leaves using machine learning techniques. The work uses fourteen different plant leaves, namely, apple, blueberry, cherry, corn, cotton, grape, groundnut, peach, pepper, potato, raspberry, soybean, strawberry, and tomato. A total of 20, 357 images are taken for training and testing purposes. Features include shape, texture, HSI and wavelets. Features are reduced using feature optimization techniques such as XG Boost, Pearson correlation, and chi-squared. In search of the best classifier, five classifiers, namely, random forest, k-nearest neighbor, support vector machine, na¨ıve Bayes and decision tree are varied with their hyperparameters. The SVM classifier gave the best results, achieving an accuracy of 99.59 with four-fold cross-validation. The novelty of the work lies in deploying features using the knowledge gained by farmers. Results reveal that the method outperforms the state-of-the-art works and are found encouraging. In this regard the techniques used here enable us to target the leaves and detect the diseases and further facilitate to opt for preventive measures.