No abstract

No abstract

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.20181104.3090Citation: 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.

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.

Vegetables are one of the main crops in China, and pests are one of the important factors affecting the quality of vegetables. In order to improve the recognition accuracy of vegetable pest images, a vegetable pest image recognition method based on improved VGG convolution neural network is proposed. Based on the VGG16 and VGG19 models, the method optimizes the number of full connection layers, replaces the original SoftMax classifier in VGGNet with the three-label SoftMax classifier, optimizes the structure and parameters of the model, and uses the weight parameters of convolution layer and pooling layer in the pre-training model in transfer learning. Experiments were carried out on the self-expanding data set of vegetable pest images, and the performance of the method was tested. Tensorflow was used to train the network model. The experimental results showed that the pre-trained models (VGG16, VGG19, Inception V3, ResNet50) were trained on the vegetable pest image data set to adapt to the recognition of vegetable pest images. The experimental results also showed that compared with Inception V3 and ResNet50, the recognition accuracy of the pre-trained models using VGG16 and VGG19 were higher, and the test accuracy of the two models were 99.90% and 99.99% respectively. Finally, the methods were compared with the traditional VGG method in self-expanding data sets. The results showed that the accuracy of VGG16 model and VGG19 model were improved from 85.90% and 86.21% to 100% and 100% respectively; the classification accuracy of VGG16 model was improved from 64.02% to 99.90%, and the classification accuracy of VGG19 model was improved from 85.83% to 99.99%, which effectively improved the recognition accuracy.