A Novel Red Apple Detection Algorithm Based on AdaBoost Learning

Abstract

Abstract:This study proposes an algorithm for recognizing apple trees in images and detecting apples to measure the number of apples on the trees. The proposed algorithm explores whether there are apple trees or not based on the number of image block-unit edges, and then it detects apple areas. In order to extract colors appropriate for apple areas, the CIE L*a*b* color space is used. In order to extract apple characteristics strong against illumination changes, modified census transform (MCT) is used. Then, using the AdaBoost learning algorithm, characteristics data on the apples are learned and generated. With the generated data, the detection of apple areas is made. The proposed algorithm has a higher detection rate than existing pixel-based image processing algorithms and minimizes false detection.

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

“…Image processing at orchards spans a large variety of fruits, such as grapes (Font et al., ; Nuske et al., ), mangoes (Chhabra, Gupta, Mehrotra, & Reel, ; Payne et al., ), apples (Hung, Underwood, Nieto, & Sukkarieh, ; Ji et al., ; Kim, Choi, Choi, Yoo, & Han, ; Linker, Cohen, & Naor, ; Silwal, Gongal, & Karkee, ; Stajnko, Rakun, & Blanke, ; Wang et al., ), citrus (Annamalai, Lee, & Burks, ; Li, Lee, & Hsu, ; Qiang, Jianrong, Bin, Lie, & Yajing, ; Regunathan & Lee, ; Sengupta & Lee, ), kiwifruit (Wijethunga, Samarasinghe, Kulasiri, & Woodhead, ), and peaches (Kurtulmus, Lee, & Vardar, ). Fruit classification is generally performed by transforming image regions into discriminative feature spaces and using a trained classifier to associate them to either fruit regions or background objects, such as foliage, branches, and ground.…”

“…Image processing at orchards spans a large variety of fruits such as grapes (Nuske et al, 2014;Font et al, 2015), mangoes (Chhabra et al, 2012;, apples (Linker et al, 2012;Wang et al, 2013;Stajnko et al, 2009;Silwal et al, 2014;Ji et al, 2012;Kim et al, 2015;Hung et al, 2015), citrus (Li et al, 2011;Annamalai et al, 2004;Sengupta and Lee, 2014;Regunathan and Lee, 2005;Qiang et al, 2014), kiwifruit (Wijethunga et al, 2009) and peaches (Kurtulmus et al, 2014). Fruit classification is generally performed by transforming image regions into discriminative feature spaces and using a trained classifier to associate them to either fruit regions or background objects such as foliage, branches, ground etc.…”

Detection of Young Green Apples in Orchard Environment Using Adaptive Ratio Chromatic Aberration and HOG-SVM

“…Huang et al [12] developed a method based on 2D fuzzy entropy for mature apple detection by combining two-dimensional histogram and genetic optimization algorithm in L*a*b* color space, and this method have a better performance for red apple detection. To solve the problem of varying illumination in apple detection, Kim et al [13] developed an improved method of statistical transformation that reduced impact from illumination by extracting structural information, and successfully detected red apple in orchard through united L*a*b* color space and AdaBoost algorithm, and success rate was 80.68%. Qian et al [14] proposed a detection method for mature apple based on a mixed color model of R/B and V, and success rate achieved 84.9%.…”