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Semantic Segmentation of Diseases in Mushrooms using Enhanced Random Forest

Yacharam Rakesh Kumar, Sekhar Dr. V. Chandra

Machine Graphics and Vision

2023

Vol. 32, No. 2

129--146

Mushrooms are a rich source of antioxidants and nutritional values. Edible mushrooms, however, are susceptible to various diseases such as dry bubble, wet bubble, cobweb, bacterial blotches, and mites. Farmers face significant production losses due to these diseases affecting mushrooms. The manual detection of these diseases relies on expertise, knowledge of diseases, and human effort. Therefore, there is a need for computer-aided methods, which serve as optimal substitutes for detecting and segmenting diseases. In this paper, we propose a semantic segmentation approach based on the Random Forest machine learning technique for the detection and segmentation of mushroom diseases. Our focus lies in extracting a combination of different features, including Gabor, Bouda, Kayyali, Gaussian, Canny edge, Roberts, Sobel, Scharr, Prewitt, Median, and Variance. We employ constant mean-variance thresholding and the Pearson correlation coefficient to extract significant features, aiming to enhance computational speed and reduce complexity in training the Random Forest classifier. Our results indicate that semantic segmentation based on Random Forest outperforms other methods such as Support Vector Machine (SVM), Naïve Bayes, K-means, and Region of Interest in terms of accuracy. Additionally, it exhibits superior precision, recall, and F1 score compared to SVM. It is worth noting that deep learning-based semantic segmentation methods were not considered due to the limited availability of diseased mushroom images.

An ensemble feature method for food classification

Martinel N., Piciarelli C., Micheloni C.

Machine Graphics and Vision

2017

Vol. 26, No. 1/4

13--39

In the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classiﬁcation. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this speciﬁc task. In this work, we adopt a Random Forest classiﬁer to measure the performances of diﬀerent texture ﬁlter banks and feature encoding techniques on three diﬀerent food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classiﬁers with other feature-based state-of-the-art solutions.