An image segmentation algorithm based on double-layer pulse-coupled neural network model for kiwifruit detection

He, Fenghua; Fu, Chunyun; Shao, Huanzhe; Teng, Jianqiao

doi:10.1016/j.compeleceng.2019.106466

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…In the second stage, boundary pixels are defined by two separate criteria, one focusing on color uniformity and the other on shape regularity, flooding from the initial boundary pixels toward pixels that are more likely to be true. The final segmentation result maintains color uniformity in the content rich region, and improves the regularity of superpixels in the content flat region 26 . Pulse coupled neural network (PCNN) is often used to generate fused images by fusion rules, but its performance is sometimes controlled by the selection of parameters.…”

Section: Related Workmentioning

confidence: 90%

Simulation analysis of visual perception model based on pulse coupled neural network

Li¹

2023

Sci Rep

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Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

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Section: Related Workmentioning

confidence: 90%

Simulation analysis of visual perception model based on pulse coupled neural network

Li¹

2023

Sci Rep

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“…Nowadays, ML-based image segmentation approach could be used to predict apple fruit yield on a real-time basis for any geographical region. 19,20 Dehghanisanij et al 21 estimated apple yield through the hybrid ML models with the help of irrigation and climate parameters in the southeast of Urmia Lake, Azerbaijan. Emami and Choopan 22 used the radial basis function (RBF) and the feed-forward neural models to estimate the barley yield in south-eastern Iran.…”

Section: Introductionmentioning

confidence: 99%

“…It was also able to give better performance than the ridge regression model and RM algorithms. Nowadays, ML-based image segmentation approach could be used to predict apple fruit yield on a real-time basis for any geographical region 19 , 20 . Dehghanisanij et al 21 .…”

Section: Introductionmentioning

confidence: 99%

Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

Singha

Gulzar

Swain

et al. 2023

J. Appl. Rem. Sens.

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.Our study established a machine learning (ML) model that could predict the apple yield based on various satellite multisensor data, such as climatological, SAR backscatter, terrain distribution, and soil factors, grouped as 26 subcriteria. A total of 986 apple orchards database were collected from 2018 to 2021 in Kashmir Valley, India covering an area of 277953.7 ha farmland. The novelty of our research is the integration of Google Earth Engine cloud and ML models, namely random forest, support vector machine, extreme gradient boosting, K-nearest neighbors, and Cubist along with the geographic information system/remote sensing technology to create an accurate and comprehensive apple yield prediction model in the precision agriculture realm for highlands. The multicollinearity testing indicated that the tolerance and VIF values of all the conditioning factors were <0.1 and <6.85, respectively, indicating no multicollinearity problems among the apple yield suitability factors. Among the tested ML models, the Cubist model performed best, with R2 of 0.83, root-mean-squared error of 0.56 t / ha, and mean absolute error of 0.2 t / ha. The results showed a low mean fruit yield during 2018 of 12.36 ton / ha, whereas maximum fruit yield was reflected in 2021 of 14.05 ton / ha. The heat map revealed the highest normalized differential vegetation index along with vertical-vertical/ vertical-horizontal polarization backscatter, detected during the pre-event of severe snowfall compared to on- and postevent of snowfall for the respective years. Untimely snowing and infestation due to fungi and bacterial diseases regularly reduce fruit yield in the study area. Our study successfully used of high-resolution optical-SAR data combined with ML models as a promising tool for monitoring the yield variability over the highland areas.

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“…With the evolution and maturity of neural network, PCNN has been favoured by scholars in image segmentation [9,6,7]. As the third generation of neural network, PCNN does not need to train samples.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Combining Pulse Coupled Neural Network and Adaptive Glowworm Algorithm

Zhu

Jian-ping

et al. 2023

ITC

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Image segmentation is one of the key steps of target recognition. In order to improve the accuracy of image segmentation, an image segmentation algorithm combining Pulse Coupled Neural Network(PCNN) and adaptive Glowworm Algorithm(GA) is proposed. The algorithm retains the advantages of the GA. Introduce the adaptive moving step size and the population optimal value as adjustment factors. Enhance the ability to solve the global optimal value, and takes the weighted sum of the cross entropy, information entropy and compactness of the image as the fitness function of the GA. Maintain the diversity of image features and improving the accuracy of image segmentation. Experimental results show that compared with other algorithms, the segmented image obtained by this algorithm has better visual effect and the segmentation performance has the best comprehensive performance. For the seven gray-scale images in the Berkeley segmentation dataset, the segmentation effect is improved by 10.85% compared with TDE algorithm, 9.22% compared with GA algorithm, and 22.58% compared with AUTO algorithm.

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An image segmentation algorithm based on double-layer pulse-coupled neural network model for kiwifruit detection

Cited by 7 publications

References 20 publications

Simulation analysis of visual perception model based on pulse coupled neural network

Simulation analysis of visual perception model based on pulse coupled neural network

Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

Image Segmentation Combining Pulse Coupled Neural Network and Adaptive Glowworm Algorithm

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