Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits

Abstract

Ring rot caused by Botryosphaeria dothidea and anthracnose caused by Colletotrichum gloeosporioides are two important apple fruit diseases. It is critical to conduct timely and accurate distinction and diagnosis of the two diseases for apple disease management and apple quality control. The automatic distinction between the two diseases was investigated based on image processing technology in this study. The acquired disease images were preprocessed via image scaling, color image contrast stretching, and morphological opening and closing reconstruction. Then, two lesion segmentation methods based on circle fitting were proposed and used to conduct lesion segmentation. After comparison with the manual segmentation results obtained via the software Adobe Photoshop CC, Lesion segmentation method 1 was chosen for further disease image processing. The gray images on the nine components in the RGB, HSI, and L*a*b* color spaces of the segmented lesion images were filtered by using multi-scale block local binary pattern operators with the sizes of pixel blocks of 1 × 1, 2 × 2, and 3 × 3, respectively, and the corresponding local binary pattern (LBP) histogram vectors were calculated as the features of the lesion images. Subsequently, support vector machine (SVM) models and random forest models were built based on individual LBP histogram features or different LBP histogram feature combinations for distinguishing the diseases. The optimal SVM model with the distinction accuracies of the training and testing sets equal to 100 and 95.12% and the optimal random forest model with the distinction accuracies of the training and testing sets equal to 100 and 90.24% were achieved. The results indicated that the distinction between the two diseases could be implemented with high accuracy by using the proposed method. In this study, a method based on image processing technology was provided for the distinction of ring rot and anthracnose on apple fruits.

Detection of early bruises on apples using near‐infrared camera imaging technology combined with adaptive threshold segmentation algorithm

“…In recent years, the rapid development of machine learning, especially deep learning, has provided powerful tools and methods for solving practical problems in various fields. Traditional machine learning methods, such as support vector machines (Li et al, 2022; Su et al, 2022), random forests (Feng et al, 2022), k‐nearest neighbors (Nturambirwe et al, 2021), deep learning methods (Liu et al, 2022), and so forth, such as target detection algorithms (Yao et al, 2021; Yuan et al, 2022), semantic segmentation algorithm (Liang et al, 2022), and so forth, combined with machine vision systems have been widely used in the field of fruit bruise detection and have achieved significant results.…”

Identification of Stripe Rust and Leaf Rust on Different Wheat Varieties Based on Image Processing Technology

“…Plant disease identification is a prerequisite and basis of effective disease management. At present, plant disease identification can be carried out using the artificial visual observation method [7][8][9] and methods based on molecular biology technology [10][11][12], remote sensing technology [6,[12][13][14][15], image processing technology [7][8][9]14,[16][17][18], near infrared spectroscopy [5], and Internet of Things technology [19][20][21]. In practice, plant disease identification mainly relies on experienced personnel to implement it using the artificial visual observation method.…”

“…With the rapid development of information technology, devices with photo-taking functions have become widely used in daily life, and it is very convenient and fast to obtain plant disease images. Image-processing technology has been used in the studies on the identification of various plant diseases [7][8][9]14,[16][17][18][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. Plant disease identification based on traditional image-processing technology generally includes plant disease image acquisition, image preprocessing, lesion image segmentation, image feature extraction and selection, and the construction and application of disease image identification models.…”