Abstract
Segmentation is an important part of each machine vision system that has a direct relationship with the final system accuracy and performance. Outdoors segmentation is often complex and difficult due to both changes in sunlight intensity and the different nature of background objects. However, in fruit-tree orchards, an automatic segmentation algorithm with high accuracy and speed is very desirable. For this reason, a multi-stage segmentation algorithm is applied for the segmentation of apple fruits with Red Delicious cultivar in orchard under natural light and background conditions. This algorithm comprises a combination of five segmentation stages, based on: 1- L*u*v* color space, 2- local range texture feature, 3- intensity transformation, 4- morphological operations, and 5- RGB color space. To properly train a segmentation algorithm, several videos were recorded under nine different light intensities in Iran- Kermanshah (longitude: 7.03E; latitude: 4.22N) with natural (real) conditions in terms of both light and background. The order of segmentation stage methods in multi-stage algorithm is very important since has a direct relationship with final segmentation accuracy. The best order of segmentation methods resulted to be: 1- color, 2- texture and 3- intensity transformation methods. Results show that the values of sensitivity, accuracy and specificity, in both classes, were higher than 97.5%, over the test set. We believe that those promising numbers imply that the proposed algorithm has a remarkable performance and could potentially be applied in real-world industrial case.
Highlights
Machine vision systems are used to perform different duties in agriculture and industry, among others
Results showed that the segmentation accuracy based on two classifiers including support vector machine (SVM) and artificial neural network (ANN) were 0.9572 and 0.8705, respectively
The aim of this study is to develop a new segmentation algorithm consisting of various color-based, texture-based, and threshold-based methods, for separation of apple fruits under real conditions
Summary
Machine vision systems are used to perform different duties in agriculture and industry, among others. In environments with complex backgrounds including a wide variety of colors and textures, segmentation is the most sensitive part of a machine vision system (Rahimi-Ajdadi et al 2016; Rahimi-Ajdadi et al 2018). Aquino et al (2017) proposed a segmentation method to count the number of 18 different cultivars of grape berry under artificial light. They used 152 images (126 images for training and 26 images for testing) to design a segmentation algorithm. A segmentation method to detect immature green citrus in citrus gardens was proposed by Zhao et al (2016) They used a combination method of sum of absolute transformed difference (SATD) and color features.
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