IMVTS: A Detection Model for Multi-Varieties of Famous Tea Sprouts Based on Deep Learning

Zhao, Runmao; Liao, Calvin C.Y.; Yu, Tao; Chen, Jianneng; Li, Yatao; Lin, Guimin; Huan, Xiaolong; Wang, Zhiming M.

doi:10.3390/horticulturae9070819

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…But the detection accuracy is not high enough. Zhao et al. (2023) introduced the IMVTS model for classifying tea buds of different varieties with an accuracy of 99.87%.…”

Section: Introductionmentioning

confidence: 99%

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7

Deng,

Chen,

et al. 2024

Front. Plant Sci.

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Variety detection provides technical support for selecting XinHui citrus for use in the production of XinHui dried tangerine peel. Simultaneously, the mutual occlusion between tree leaves and fruits is one of the challenges in object detection. In order to improve screening efficiency, this paper introduces a YOLO(You Only Look Once)v7-BiGS(BiFormer&GSConv) citrus variety detection method capable of identifying different citrus varieties efficiently. In the YOLOv7-BiGS network model, initially, the BiFormer attention mechanism in the backbone of the YOLOv7-based network strengthens the model’s ability to extract citrus’ features. In addition, the introduction of the lightweight GSConv convolution in place of the original convolution within the ELAN of the head component effectively streamlines model complexity while maintaining performance integrity. To environment challenge validate the effectiveness of the method, the proposed YOLOv7-BiGS was compared with YOLOv5, YOLOv7, and YOLOv8. In the comparison of YOLOv7-BiGS with YOLOv5, YOLOv7, and YOLOv8, the experimental results show that the precision, mAP and recell of YOLOv7-BiGS are 91%, 93.7% and 87.3% respectively. Notably, compared to baseline methods, the proposed approach exhibited significant enhancements in precision, mAP, and recall by 5.8%, 4.8%, and 5.2%, respectively. To evaluate the efficacy of the YOLOv7-BiGS in addressing challenges posed by complex environmental conditions, we collected occluded images of Xinhui citrus fruits from the Xinhui orchard base for model detection. This research aims to fulfill performance criteria for citrus variety identification, offering vital technical backing for variety detection endeavors.

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“…But the detection accuracy is not high enough. Zhao et al. (2023) introduced the IMVTS model for classifying tea buds of different varieties with an accuracy of 99.87%.…”

Section: Introductionmentioning

confidence: 99%

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7

Deng,

Chen,

et al. 2024

Front. Plant Sci.

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Optimal training strategy for high-performance detection model of multi-cultivar tea shoots based on deep learning methods

Zhang,

Lu,

Yang

et al. 2024

Scientia Horticulturae

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YOLO Recognition Method for Tea Shoots Based on Polariser Filtering and LFAnet

Peng,

Zhang,

Xian

et al. 2024

Agronomy

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This study proposes a YOLOv5 inspection model based on polariser filtering (PF) to improve the recognition accuracy of the machine vision inspection model for tea leaf shoots when operating under intense outdoor light. To study the influence of the polariser parameters on the quality of the tea shoot image datasets, we improved the YOLOv5 algorithm module, inputted the results obtained from the spatial pyramid pooling structure in the backbone module into the neck module, set the up-sampling link of the neck module as a low-level feature alignment (LFA) structure, and used a bounding box similarity comparison metric based on the minimum point distance (mpdiou) to improve the accuracy of the YOLOv5 detection model. The mpdiou loss function is used to replace the original loss function. Experimental results show that the proposed method can effectively address the impact of intense outdoor light on tea identification, effectively solving the problem of poor detection accuracy of tea buds in the top view state. In the same identification environment, the model mAP50 value increased by 3.3% compared to that of the existing best mainstream detection model, and the mAP50-90 increased by 3.1%. Under an environment of light intensity greater than 5×104 Lux, the proposed YOLOv5s+LFA+mpdiou+PF model reduced the leakage detection rate by 35% and false detection rate by 10% compared to that with YOLOv5s alone.

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IMVTS: A Detection Model for Multi-Varieties of Famous Tea Sprouts Based on Deep Learning

Cited by 3 publications

References 15 publications

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7

Optimal training strategy for high-performance detection model of multi-cultivar tea shoots based on deep learning methods

YOLO Recognition Method for Tea Shoots Based on Polariser Filtering and LFAnet

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