Fruit recognition based on pulse coupled neural network and genetic Elman algorithm application in apple harvesting robot

Abstract

In order to improve the harvesting efficiency of apple harvesting robot, this article presents an apple recognition method based on pulse coupled neural network and genetic Elman neural network (GA-Elman). Firstly, we use pulse coupled neural network to segment the captured 150 images, respectively, and extract six color features of R, G, B, H, S, and I and 10 shape features of circular variance, density, the ratio of perimeter square to area, and Hu invariant moments of segmented images, and these 16 features are considered as the inputs of Elman neural network. In order to overcome some defects of Elman neural network, such as, trapping local minimum easily and determining the number of hidden neurons difficultly; in this article, genetic algorithm is introduced to optimize it, and new optimization way is designed, that is, the connection weights and number of hidden neurons separate encoding and evolving simultaneously, in the process of structural evolution at the same time the learning of connection weights is completed, and then the operating efficiency and recognition precision of Elman model are improved. In order to get more precision neural network, and avoid the influence of fruit recognition caused by branches or leaves shadow, apple along with branches and leaves is allowed to train. The results of experiments show that compared with the traditional back-propagation, Elman neural network, and other two recognition algorithms of obscured fruit. the genetic Elman neural network algorithm is the optimal method which successful training rate can reach to 100%, recognition rate of overlapping fruit and obscured fruit can reach to 88.67% and 93.64%, respectively, and the total recognition rate reaches to 94.88%.

On-tree fruit monitoring system using IoT and image analysis

“…In this paper a random forest classifier was applied demonstrate the potential of UAS-based mapping to support the horticulture industry and facilitate orchard-based assessment and it produced >96% accuracy. Based on pulse coupled neural network and genetic Elman neural network (GA-Elman), an apple recognition method is developed by Jia et al (2020). Here to segment the 150 captured images pulse coupled neural network is used.…”

Apple harvesting robot under information technology: A review

“…31 Jia proposed an apple recognition based on pulse-coupled neural network (PCNN) and GA-Elman algorithm, the recognition rate of overlapping fruit and obscured fruit can reach up to 88.67% and 93.64%, and the total recognition rate reaches up to 94.88%. 32 Xu proposed an overlapped apple target segmentation method combining the Snake model and corner detection and segmented 20 overlapping apples with an average error of 6.41. 33 Wang first used the K-means cluster segmentation method to extract the target fruit region and then used the Ncut algorithm to extract the outline of the target fruit.…”

YOLOF-Snake: An Efficient Segmentation Model for Green Object Fruit

“… Henila et al (2020) constructed a fuzzy clustering-based threshold segmentation method for segmenting regions of interest in apple images with the largest cluster of pixels to calculate the threshold value, which has substantially improved the segmentation accuracy and efficiency compared with the grayscale thresholding method. Jia et al (2020a) proposed an apple image recognition method based on PCNN and GA–Elman fusion. First, PCNN is utilized to implement segmentation of the target image, extract color, and shape features, GA–Elman classifier is designed, and finally, object fruit recognition is implemented.…”