Abstract
Cucumber fruit appearance quality is an important basis of growth status. In order to improve the quality detection accuracy and processing efficiency of cucumber color image under complicated background, an improved GrabCut algorithm was proposed to extract the cucumber boundary. Firstly, including pixel size normalization, rectangular box set and scale image resolution, pretreatments of cucumber image were adopted to reduce the iteration times and operation time of GrabCut algorithm. Then, the Gaussian mixture model was chosen to find out the possible prospect of target region and background region in the preprocessed rectangular frame on the preliminary modeling. Meanwhile, by the optimization of K-means cluster to the initial GMM model, the effective target area was extracted. Finally, the whole image noise and serrated boundary was removed by morphological operations to segment the outline of the complete target prospects with appropriate structure size. And then the cucumber appearance quality detection instrument was designed to extract the texture and shape features exactly, so that it could obtain cucumber appearance quality and evaluate its growth effectively. With the segmentation experiments by almost 300 cucumber original images from greenhouse in Shandong Province, the results showed that the improved GrabCut algorithm could effectively extract the complete and smooth boundary of cucumber. With relatively high segmentation evaluation index, the precision was 93.88%, the recall rate was 99.35%, the F-Measure reached 96.53%, and the misclassification error was controlled at minimum 5.84%. The average running time was shortened to 1.4023 s. The comparison results showed that the improved GrabCut algorithm was the best, followed by the SLIC and traditional GrabCut method. Cucumber appearance quality detection instrument could also extract more accurate feature parameters. And it could meet the basic growth condition assessment by automatic image processing. Keywords: cucumber, complicated background, quality detection, image processing, GrabCut DOI: 10.25165/j.ijabe.20181104.3090 Citation: Ye H J, Liu C Q, Niu P Y. Cucumber appearance quality detection under complex background based on image processing. Int J Agric & Biol Eng, 2018; 11(4): 193-199.
Highlights
With the application of agricultural intelligence and refined technology, more accurate quality testing of the crops is required
Fresh fruits and vegetables contain rich essential substances which play an active role in improving the health status of people[1,2]
Regarding to detecting the rich information of Cucumber image, most of the existing researches are based on the laboratory and other simple environment[3]
Summary
With the application of agricultural intelligence and refined technology, more accurate quality testing of the crops is required. Several methods have been commonly applied in the field of the evaluation of preservation quality, such as wavelet domain, analytic hierarchy process and principal component analysis, quantum genetic fuzzy neural network, and particle clustering[15,16,17,18,19,20,21,22,23] They were used to detect agricultural and livestock products. Mathematical morphology is used to replace Border Matting technology of GrabCut. 2) On the cucumber quality detection, cucumber appearance quality detection instrument was designed, integrated with the improved GrabCut algorithm, and combined with morphology and gray level co-occurrence matrix[24], to solve the shape features and texture features extraction of cucumber. 3) Combined with cucumber grade standard, the relative weights of the key characteristic parameters are set, and the accurate grading of cucumber is completed
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