Apple Counting Network Before Fruit Thinning Period Based On Dilated Convolution
Authors: 
Mengyang Song, Guoquan Jiang, Zhanqiang Huo, Zhengyuan Yang, Hongxu ZhangAuthors Info & Claims
Pages 129 - 134
Abstract
Abstract: Fruit counting is an integral part of achieving precision orchard management. Accurate counting of the number of fruits on a tree can provide critical information for yield estimation, thus promoting precision agriculture. However, today fruit farmers can only support their fieldwork by manual counting, and a reliable and accurate automatic fruit counting method is missing. A pre-thinning apple counting network (FTACNet) is proposed to address the problems of shading, uneven distribution, and fruit scale differences and is validated on the produced dataset. The method uses deep learning algorithms in the field of population counting. FTACNet shows good performance on the dataset, with mean absolute error (MAE) down to 4.14 and mean square error (MSE) down to 5.62. Moreover, the model is end-to-end, the model is small, and can be easily deployed to mobile devices, which has good potential for application in orchards.
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Index Terms
- Apple Counting Network Before Fruit Thinning Period Based On Dilated Convolution
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Published In
December 2022
365 pages
ISBN:9781450398039
DOI:10.1145/3579895
Copyright © 2022 ACM.
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Published: 04 April 2023
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ICNCC 2022
ICNCC 2022: 2022 The 11th International Conference on Networks, Communication and Computing
December 9 - 11, 2022
Beijing, China
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