Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment

Abstract

A large number of countries depend on agriculture. However, the biggest challenge for farmers is the plant-related diseases. The most observed part of the plant is leaf but the segmentation of a diseased lesion leaf image suffers from uneven illumination and cluttered complex natural environment problems. To overcome these problems, the proposed methodology is based on the fusion of color balancing and superpixel. First, the input image transforms into a color-balanced image to eliminate the effects of uneven illumination. Second, compact regions are created from the transformed image using superpixel operation and the empirically derived threshold is applied on Histogram of Gradients (HOG) and color channels of superpixel to separate the unwanted background from leaf image. K-means clustering is applied to find a diseased infected image. An extended form of HOG, Pyramid of HOG(PHOG) with Gray Level Co-occurrence Matrix (GLCM) features are used to represent a diseased infected part. Finally, different classifiers are compared and Random Forest (RF) is selected to classify diseases. To assess the robustness of the proposed method, an experiment is conducted to test its accuracy effectively on natural images acquired from various farms, downloaded images from the Internet and Plant-Village color dataset. Results indicate that the proposed method achieved an accuracy of 93.12% on a combined dataset using cross-validation to show that method is effective to classify diseases in the presence of a cluttered background. The proposed system is compared with ALDD method (Dhaygude and Kumbhar, 2013), MVBPDR (Habib et al., 2018), AIMSD (Araujo and Peixoto, 2019) and CCF (Ma et al., 2017). The novelty of the proposed system lies in the fact that the proposed segmentation is effective for all the varied datasets and it is evaluated by True Positive Rate (TPR), False Positive Rate (FPR), False Negative Rate (FNR), Positive Prediction Value (PPV), False Discovery Rate (FDR), Accuracy and F1 score.