Search

• An image restoration method based on convolutional autoencoder is proposed. • Use the area growth method to divide and code the obstruction according to the contour. • Predict the overall outline of citrus based on the unobstructed citrus outline. • Fill the area to be repaired with the selection method of similar pixels. • The repair rate and image recognition rate have been effectively improved. At present, machine vision and deep learning theories have been widely used in fruit recognition and picking. However, in the process of identification and picking, there are often situations where the target is blocked, existing methods cannot accurately identify, or the identification accuracy rate is low. For improving the recognition rate of fruits under occlusion, this paper proposes an image restoration method based on a convolutional auto-encoder. This method first encodes the obstruction and then determines the general shape of the occluded fruit and compares the general shape with the coding part is combined to determine the area to be repaired. Finally, the area to be repaired is filled with pixels to realize the image repair and recognition of the occlusion map. The average repair rate of the method proposed in this paper is 95.96%, the restoration rate is 3.52% higher than the traditional convolution method, the L2 loss value is 0.63% lower than the traditional convolution method, the average detection accuracy of the restored fruits is 94.77%. [ABSTRACT FROM AUTHOR]

• An algorithm for detecting ripe fruit under natural illumination and occlusion conditions is proposed. • A graying factor K is proposed to remove the complex background of colorful ripe fruits. • Improving the Hough Transform by selecting equidistant points and increasing judgement threshold. • A real contour Hough Transform was proposed to detect ripe fruits or occluded circles. For an accurate detection of ripe fruits under uneven illumination and ubiquitous occlusion conditions, this paper proposes a method to detect and locate ripe fruits based on machine vision. There are four key steps in this method including image graying and background removal, binary image optimization, true contour fragments extraction, and fruit fitting. For testing the proposed method, field experiments were conducted with tomato and citrus, and the ripe fruits in complex environments were successfully detected and located. From the detection experiments, it showed that the recognition rate for ripe fruits in the near zone of the proposed method was higher than 97.44%, the average time consumption was 0.2966 s, and the positioning error was less than 4.41%. In addition, it can be concluded from the comparative experiment that the proposed method is superior to conventional Hough transform, random Hough transform, and other methods based on deep learning in terms of detection rate, time performance and positioning accuracy. Therefore, it can be applied to picking robots for real-time detecting and locating ripe fruits. [ABSTRACT FROM AUTHOR]

• An algorithm for identifying and locating sugarcane stem nodes is proposed. • Use the horizontal projection function of binary image to intercept the region of interest. • Use vertical projection function and local pixels to locate sugarcane stem nodes. • The algorithm has good performance in recognition rate, accuracy and time-consuming. Aiming at the difficulty of sugarcane nodes identification and location during automatic cutting of sugarcane seeds, based on machine vision system, this paper proposed a sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function. Firstly, according to the color and texture characteristics of yellow sugarcane, the captured RGB image of sugarcane was converted into HSV color image. Then, the S-component image in the HSV image was extracted, and the S-component image was binarized by the Otsu algorithm to obtain the binary image, and then the binary image was processed by morphological closed operation, which can eliminate the noise and fill holes of binary image. Subsequently, the sugarcane area was segmented as the region of interest through the horizontal projection of the binary image, which lowered the amount of calculation while reducing interference. Finally, the vertical projection function of the binary image of the region of interest was established, and the function was continuously derived to obtain the minimum points, and then the position of sugarcane nodes was preliminarily determined by the obtained minimum points. Then, the final position of the sugarcane nodes were determined according to the number of nodes to be identified and the sum of pixels of each 5 columns on both sides of the minimum points. The pixel column positions corresponding to the minima of the sum are the accurate nodes positions determined by the proposed algorithm. The experimental results show that the algorithm proposed in this paper has a single node identification rate of 100%, with an average time consumption of 0.15 s, and a position deviation of less than 0.34 mm; a double nodes identification rate of 98.5%, with an average time consumption of 0.21 s, and a position deviation of less than 0.42 mm. Compared with other nodes identification algorithms mentioned in this paper, it has higher identification rate and accuracy. [ABSTRACT FROM AUTHOR]

• A prediction point Hough transform with a maximum error of 0.5°is proposed. • Based on the G-B and 2G-B-R grayscale factors, a new grayscale factor is proposed. • The least square method is used to optimize the traditional Hough transform. Accurate extraction of navigation path is very important for automated navigation of agricultural robots. Based on the machine vision system, this paper proposes a new algorithm for the fitting of navigation path of greenhouse cucumber-picking robots. Aiming at the problems that the traditional Hough transform has a large amount of calculation and the least square method has a low precision, the prediction point Hough transform algorithm is proposed to extract the navigation path. The prediction-point Hough transform contains 4 parts: intercept area of interest, image segmentation, navigation point extraction, navigation path fitting. In this paper, only the final 160-pixel rows of image captured by the camera are taken as the region of interest. In the image segmentation stage, this paper proposes a new graying factor. For navigation path extraction, a regression equation is used to determine the prediction point, and finally the proposed prediction point Hough transform is used to fit the navigation path. The experimental results show that the proposed grayscale factor can well segment cucumber plants and soil, and the segmentation effect is better than 2G-B-R and G-B grayscale factors. The proposed prediction point Hough transform fits the navigation paths with an average error less than 0.5°, which is 10.25° lower than the average error of the least-square method. Also, the computation time of the proposed Hough transform is 17.92 ms. Compared with the traditional Hough transform, it saves 35.20 ms. [ABSTRACT FROM AUTHOR]