Desigining of Computer Vision Algorithm to Detect Sweet Pepper for Robotic Harvesting Under Natural Light

Document Type : Research Article

Authors

• A. Moghimi
• M. H. Aghkhani
• M. R. Golzarian

Ferdowsi University of Mashhad

https://doi.org/10.22067/jam.v5i1.23528

Abstract

In recent years, automation in agricultural field has attracted more attention of researchers and greenhouse producers. The main reasons are to reduce the cost including labor cost and to reduce the hard working conditions in greenhouse. In present research, a vision system of harvesting robot was developed for recognition of green sweet pepper on plant under natural light. The major challenge of this study was noticeable color similarity between sweet pepper and plant leaves. To overcome this challenge, a new texture index based on edge density approximation (EDA) has been defined and utilized in combination with color indices such as Hue, Saturation and excessive green index (EGI). Fifty images were captured from fifty sweet pepper plants to evaluate the algorithm. The algorithm could recognize 92 out of 107 (i. e., the detection accuracy of 86%) sweet peppers located within the workspace of robot. The error of system in recognition of background, mostly leaves, as a green sweet pepper, decreased 92.98% by using the new defined texture index in comparison with color analysis. This showed the importance of integration of texture with color features when used for recognizing sweet peppers. The main reasons of errors, besides color similarity, were waxy and rough surface of sweet pepper that cause higher reflectance and non-uniform lighting on surface, respectively.

Keywords

• Automatic harvesting
• Computer vision
• Image processing
• Robot
• Texture index

20.1001.1.22286829.1394.5.1.9.0

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