Recognising blueberry fruit of different maturity using histogram oriented gradients and colour features in outdoor scenes. (December 2018)

• Record Type: Journal Article
• Authors: Tan, Kezhu; Lee, Won Suk; Gan, Hao; Wang, Shuwen
• Is Part Of: Biosystems engineering. Volume 176(2018)
• Page Start: 59
• Page End: 72
• Publication Date: 2018-12
• Subjects: Blueberry recognition -- Computer vision -- HOG features -- Colour space -- Yield estimation; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2018.08.011
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : For early yield estimation and harvest management, the recognition of blueberry fruit and their maturity is essential since blueberries do not ripen at the same time. This study was conducted to recognise visible blueberry fruit with different maturity using outdoor colour images acquired from a commercial field. The maturity of blueberry was divided into three different growth stages: mature, intermediate and young. The following stepwise algorithm was developed to identify the blueberry fruit: (1) A fruit training set was constructed using 1374 patches cropped from the original colour images. (2) HOG (Histogram Oriented Gradients) feature vectors were calculated from these patches, and a linear SVM (Support Vector Machine) classifier was trained to detect fruit-like regions rapidly. (3) Using a* and b* features in the L*a*b* colour space to discard non-fruit regions as well as categorise three fruit groups on those fruit-like regions. The KNN (K-nearest Neighbour) and a newly developed TMWE (Template Matching with Weighted Euclidean Distance) classifiers were applied to identify the fruit of different maturity. The performance of the method was evaluated using average detection accuracy on the testing images, missed rate, and incorrect detection rate of false positives. KNN classifier yielded the best average accuracy of 86.0%, 94.2% and 96.0% for young fruit, intermediate fruit and mature fruit, respectively. The proposed TMWE classifier gave a relatively high accuracy at lower computation cost. The results indicated that the method of this study is efficient in recognising blueberry fruit with different maturity using colour images in outdoor scenes. Highlights: HOG fruit detector was created to detect potential regions from outdoor images. The components of a* and b* were used to distinguish different blueberry maturity. The proposed TMWE classifier gave a higher accuracy at lower computation cost. This work is promising for making an early yield estimation for blueberry growers.