Monitoring the Growth Status of Corn Crop from UAV Images Based on Dense Convolutional Neural Network
文献类型： 外文期刊
作者： Li, Yu 1 ; Zhu, Jia 2 ; Xing, Yuling 1 ; Dai, Zhangyan 3 ; Huang, Jin 1 ; Hassan, Saeed-Ul 5 ;
作者机构： 1.South China Normal Univ, Sch Comp Sci, Guangzhou, Peoples R China
2.Zhejiang Normal Univ, Key Lab Intelligent Educ Technol & Applicat Zheji, Jinhua, Zhejiang, Peoples R China
3.Guangdong Acad Agr Sci, Agrobiol Gene Res Ctr, Guangzhou, Peoples R China
4.Guangdong Key Lab Crop Germplasm Resources Preser, Guangzhou, Peoples R China
5.Metropolitan Univ, Manchester Metropolitan Univ, Comp & Math, Manchester, Lancs, England
关键词： Image classification; UAV; deep learning; cornfield identification
期刊名称：INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE （ 影响因子：1.261； 五年影响因子：1.319 ）
ISSN： 0218-0014
年卷期： 2022 年 36 卷 12 期
页码：
收录情况： SCI
摘要： Monitoring corn crop growth status is of great significance to crop production, breeding, and seed production. The Unmanned Aerial Vehicles' (UAVs) technology makes it possible to use computer vision technology to identify corn growth stage intelligently. A model customized for corn growth status monitoring based on a dense convolutional neural network (CM-CNN) was proposed, including a two-way dense module and a new activation function ELU. The two-way dense module enlarges the receptive field, while the ELU alleviates gradient disappearance and speeds up learning in deep neural networks. Dense architecture concatenates all the previous layer features to enhance feature reuse. The proposed CM-CNN performs well in classifying corn growth stages. Experimental results show that CM-CNN is a state-of-the-art method, with an accuracy of its relevant data up to 99.3%. Compared with other CNN models, viz. AlexNet, ZFNet, VGG, InceptionV3, Xception and ResNet, fewer parameters are in CM-CNN.
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