Abstract
This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable’s non-contact measurement problems and can promote the application of computer vision in agricultural automation.
Highlights
Agricultural automation significantly improves the efficiency of agricultural production through IntelliSense and automation technology
Dang [3] proposed a fruit size detection method based on image processing, which can estimate the diameter of circular fruit using natural images
This paper proposed a non-contact vegetable size measurement method based on keypoint detection and a stereo camera
Summary
Agricultural automation significantly improves the efficiency of agricultural production through IntelliSense and automation technology. Deep learning have been widely applied in agricultural production processes (e.g., plant disease recognition, weed detection, yield prediction and non-contact size estimation). Non-contact crop measurement based on CV and deep learning is an important research direction. Vision-based detection methods mainly utilized surface features such as color and lines to describe the crops’ quality [1,2]. They had not fully exploited the deeper-level information hidden in the images. In [6], Rabby proposed a fruit classification and measurement method based on edge detection, which can briefly describe the color and size of fruits.
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