Sichuan Pepper Recognition in Complex Environments: A Comparison Study of Traditional Segmentation versus Deep Learning Methods

Jinzhu Lu (), Juncheng Xiang, Ting Liu, Zongmei Gao and Min Liao
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Jinzhu Lu: Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China
Juncheng Xiang: Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China
Ting Liu: Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China
Zongmei Gao: Center for Precision and Automated Agricultural Systems, Department of Biological Systems Engineering, Washington State University, Prosser, WA 99350, USA
Min Liao: Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China

Agriculture, 2022, vol. 12, issue 10, 1-16

Abstract: At present, picking Sichuan pepper is mainly undertaken by people, which is inefficient and presents the possibility of workers getting hurt. It is necessary to develop an intelligent robot for picking Sichuan peppers in which the key technology is accurate segmentation by means of mechanical vision. In this study, we first took images of Sichuan peppers (Hanyuan variety) in an orchard under various conditions of light intensity, cluster numbers, and image occlusion by other elements such as leaves. Under these various image conditions, we compared the ability of different technologies to segment the images, examining both traditional image segmentation methods (RGB color space, HSV color space, k-means clustering algorithm) and deep learning algorithms (U-Net convolutional network, Pyramid Scene Parsing Network, DeeplabV3+ convolutional network). After the images had been segmented, we compared the effectiveness of each algorithm at identifying Sichuan peppers in the various types of image, using the Intersection Over Union(IOU) and Mean Pixel Accuracy(MPA) indexes to measure success. The results showed that the U-Net algorithm was the most effective in the case of single front-lit clusters light without occlusion, with an IOU of 87.23% and an MPA of 95.95%. In multiple front-lit clusters without occlusion, its IOU was 76.52% and its MPA was 94.33%. Based on these results, we propose applicable segmentation methods for an intelligent Sichuan pepper-picking robot which can identify the fruit in images from various growing environments. The research showed good accuracy for the recognition and segmentation of Sichuan peppers, which suggests that this method can provide technical support for the visual recognition of a pepper-picking robot in the field.

Keywords: Sichuan pepper; traditional segmentation; deep learning; picking robot (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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