Abstract
A consistent orientation of ginger shoots when sowing ginger is more conducive to high yields and later harvesting. However, current ginger sowing mainly relies on manual methods, seriously hindering the ginger industry’s development. Existing ginger seeders still require manual assistance in placing ginger seeds to achieve consistent ginger shoot orientation. To address the problem that existing ginger seeders have difficulty in automating seeding and ensuring consistent ginger shoot orientation, this study applies object detection techniques in deep learning to the detection of ginger and proposes a ginger recognition network based on YOLOv4-LITE, which, first, uses MobileNetv2 as the backbone network of the model and, second, adds coordinate attention to MobileNetv2 and uses Do-Conv convolution to replace part of the traditional convolution. After completing the prediction of ginger and ginger shoots, this paper determines ginger shoot orientation by calculating the relative positions of the largest ginger shoot and the ginger. The mean average precision, Params, and giga Flops of the proposed YOLOv4-LITE in the test set reached 98.73%, 47.99 M, and 8.74, respectively. The experimental results show that YOLOv4-LITE achieved ginger seed detection and ginger shoot orientation calculation, and that it provides a technical guarantee for automated ginger seeding.
Highlights
Ginger is a perennial herb whose roots are often made into spices and herbs [1,2].It originated in Asia and is widely grown in various regions, of which China is the world’s most productive country for ginger [3,4]
The rest of the paper is organized as follows: Section 2 describes the creation of the dataset guarantee for the fast and accurate discrimination of the orientation of ginger shoots in and the improvements based on the YOLOv4 network; Section 3 describes the tuning of the ginger seed images
CSPDarknet53, the network computation was greatly reduced after using MobieNetv2 as the backbone network
Summary
Ginger is a perennial herb whose roots are often made into spices and herbs [1,2]. It originated in Asia and is widely grown in various regions, of which China is the world’s most productive country for ginger [3,4]. Many researchers have put forward different improved methods for the backbone network, significantly reducing the model size, while ensuring the accuracy remains largely unchanged. Of this network are ginger and ginger, and and focal loss to solvethe therecognition problems targets of positive and negative sampleshoots imbalance and simple the difference recognitionThe difficulty target size between is large.guarantee. Above and improvements providethem a technical we introduce focal loss to solve the problems of positive and negative sample imbalance fast and accurate discrimination of the orientation of ginger shoots in ginger seed images. The rest of the paper is organized as follows: Section 2 describes the creation of the dataset guarantee for the fast and accurate discrimination of the orientation of ginger shoots in and the improvements based on the YOLOv4 network; Section 3 describes the tuning of the ginger seed images. Describes the tuning of the model parameters and the experimental validation of the proposed method; and Section 4 describes the conclusions of this work
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