Cited By
View all- Ming RGong QYang CLuo HSong CZhou Z(2024)Feature diffusion reconstruction mechanism network for crop spike head detectionFrontiers in Plant Science10.3389/fpls.2024.145951515Online publication date: 1-Oct-2024
Detection of Ears of Rice in field Based on SSD
Pages 228 - 232
Abstract
In order to improve the yield and quality of rice production, monitoring during rice development is particularly important. At present, rice pictures are recognized manually or by using traditional machine learning methods, but the efficiency is not high, and it is not possible to quickly identify rice in a wide range. Therefore, this paper studies the use of deep learning technology to identify rice ears, which can detect rice in the field environment faster and more accurately. In this paper, we used the SSD algorithm of deep learning object detection to train our own RICE dataset to obtain a rice ear detection model suitable for field environment feeding. The experimental results on the test set show that the mAP of this method can reach 38.1%, which provides a possibility for more in-depth research on neural network detection of rice, provides a basis for rice yield prediction, and better guides the production of rice crops.
References
[1]
SSD: Single Shot MultiBox Detector[C]// European Conference on Computer Vision. Springer International Publishing, 2016.
[2]
Liu Haibo. Automatic detection of tomato diseases and insect pests based on leaf surface pictures [D]. (in Chinese)
[3]
Zhang Shanwen, Zhang Chuanlei, Ding Jun. Forecast model of winter jujube diseases and insect pests in greenhouses based on improved deep confidence network [J]. Journal of Agricultural Engineering, 2017 (19). (in Chinese)
[4]
An Qiangqiang, Zhang Feng, Li Zhaoxing, et al. Image recognition of plant diseases and insect pests based on deep learning [J]. Agricultural Engineering, 2018, 8 (07): 48--50. (in Chinese)
[5]
Wang Xianfeng, Zhang Chuanlei, Zhang Shanwen, et al. Cotton disease and insect pest prediction based on adaptive discriminant deep confidence network [J]. Journal of Agricultural Engineering, 2018 (1): 157--164. (in Chinese)
[6]
Liu Yongbo, Lei Bo, Cao Yan, et al. Maize disease recognition based on deep convolutional neural network [J]. Chinese Agricultural Science Bulletin, 2018, 34 (36): 165--170. (in Chinese)
[7]
Luo Qi. Fruit image recognition system based on deep learning [J]. Agricultural Engineering, 2018, 8 (10): 40--43. (in Chinese)
[8]
Wang Dandan, Fan Chong, Mo Donglin. Design of crop classification system for remote sensing image based on deep learning service [J]. China Agricultural Information, 2018, 30 (06): 86--96. (in Chinese)
[9]
Jiang Min, Shen Yiming, Zhang Jingyao, et al. Research on Rice Diseases and Pests Diagnosis Method Based on Deep Learning [J]. Journal of Luoyang Institute of Technology (Natural Science Edition), 2019, 04, 78--83. (in Chinese)
[10]
Zeng Pingping, Li Linsheng. Research on Fruit Image Classification and Recognition Based on Convolutional Neural Networks [J]. Mechanical Design and Research, 2019, 35 (01): 31--34 + 42. (in Chinese)
[11]
Xie Zhonghong, Xu Huanliang, Huang Qiugui, et al. Freshness detection of spinach based on hyperspectral images and deep learning [J]. Journal of Agricultural Engineering, 2019 (13). (in Chinese)
[12]
Andreas Kamilaris, Francesc X Prenafeta-Boldú. Deep Learning in Agriculture: A Survey[J]. Computers & Electronics in Agriculture, 2018, 147(1):70--90.
[13]
Duan Lingfeng, Xiong Xiong, Liu Qian, et al. Field rice ear segmentation based on deep full convolutional neural network [J]. Journal of Agricultural Engineering, 2018, v.34; No.339 (12): 210--217. (in Chinese)
[14]
Vijay, Badrinarayanan, Alex, et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation. [J].
[15]
Xiong Xiong. Study on rice field ear segmentation and non-destructive yield estimation of field rice based on deep learning [D]. (in Chinese)
[16]
Zhang Qiang, Chen Yunqiang, Li Yunxia, et al. Wheat ears detection and counting system based on convolutional neural network [J]. Journal of the Chinese Society of Agricultural Machinery, 2019, 03, 144--150. (in Chinese)
[17]
Everingham M. The 2005 PASCAL Visual Object Classes Challenge[C]// Selected Proceedings of the first PASCAL Challenges Workshop LNAI. Springer-Verlag, 2006.
[18]
Redmon J, Divvala S, Girshick R, et al. You Only Look Once: Unified, Real-Time Object Detection[J]. 2015.
[19]
Ross Girshick. Fast R-CNN[C]// 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2016.
Index Terms
- Detection of Ears of Rice in field Based on SSD
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Published In
April 2020
563 pages
ISBN:9781450377089
DOI:10.1145/3404555
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- University of Tsukuba: University of Tsukuba
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Association for Computing Machinery
New York, NY, United States
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Published: 20 August 2020
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ICCAI '20
ICCAI '20: 2020 6th International Conference on Computing and Artificial Intelligence
April 23 - 26, 2020
Tianjin, China
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View all- Ming RGong QYang CLuo HSong CZhou Z(2024)Feature diffusion reconstruction mechanism network for crop spike head detectionFrontiers in Plant Science10.3389/fpls.2024.145951515Online publication date: 1-Oct-2024
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