Abstract
Phenotypic characteristics of fruit particles, such as projection area, can reflect the growth status and physiological changes of grapes. However, complex backgrounds and overlaps always constrain accurate grape border recognition and detection of fruit particles. Therefore, this paper proposes a two-step phenotypic parameter measurement to calculate areas of overlapped grape particles. These two steps contain particle edge detection and contour fitting. For particle edge detection, an improved HED network is introduced. It makes full use of outputs of each convolutional layer, introduces Dice coefficients to original weighted cross-entropy loss function, and applies image pyramids to achieve multi-scale image edge detection. For contour fitting, an iterative least squares ellipse fitting and region growth algorithm is proposed to calculate the area of grapes. Experiments showed that in the edge detection step, compared with current prevalent methods including Canny, HED, and DeepEdge, the improved HED was able to extract the edges of detected fruit particles more clearly, accurately, and efficiently. It could also detect overlapping grape contours more completely. In the shape-fitting step, our method achieved an average error of 1.5% in grape area estimation. Therefore, this study provides convenient means and measures for extraction of grape phenotype characteristics and the grape growth law.
Highlights
Phenotypic characteristics of grape particles are important indicators in grape water diagnosis, berry growth monitoring, and grape growth modeling
optical dataset scale (ODS) refers to the detection score when all test set images used the same fixed threshold, and optical image scale (OIS) refers to the detection score when the best threshold was used for each image in the test set
We concluded that improved HED was higher than other models in terms of OIS and frame per second (FPS)
Summary
Phenotypic characteristics of grape particles are important indicators in grape water diagnosis, berry growth monitoring, and grape growth modeling. Lei Yan et al [10] proposed a particle-segmentation method based on contour and ellipse fitting This method had high detection accuracy for elliptical fruit, but when the contour of the fruit was too short, it was easy to cause over-segmentation. Dollár Piotr et al [13] proposed an edge-detection algorithm based on structured forest that worked well, but the detection contour was thick These methods achieved good results on grape segmentation to some extent, there still exists some shortcomings and limitations: (1) The traditional segmentation process is always multi-stage, and the complicated pre-processing and post-processing steps are relatively inefficient. Its main idea is directly predicting the probability of pixel-level contours using CNN Such an algorithm is simple and efficient, and the detection accuracy is high
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