SPCN: An Innovative Soybean Pod Counting Network Based on HDC Strategy and Attention Mechanism

Ximing Li, Yitao Zhuang, Jingye Li, Yue Zhang, Zhe Wang, Jiangsan Zhao, Dazhi Li and Yuefang Gao ()
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Ximing Li: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yitao Zhuang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Jingye Li: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yue Zhang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zhe Wang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Jiangsan Zhao: Department of Agricultural Technology, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, NO-1431 Ås, Norway
Dazhi Li: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yuefang Gao: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2024, vol. 14, issue 8, 1-19

Abstract: Soybean pod count is a crucial aspect of soybean plant phenotyping, offering valuable reference information for breeding and planting management. Traditional manual counting methods are not only costly but also prone to errors. Existing detection-based soybean pod counting methods face challenges due to the crowded and uneven distribution of soybean pods on the plants. To tackle this issue, we propose a Soybean Pod Counting Network (SPCN) for accurate soybean pod counting. SPCN is a density map-based architecture based on Hybrid Dilated Convolution (HDC) strategy and attention mechanism for feature extraction, using the Unbalanced Optimal Transport (UOT) loss function for supervising density map generation. Additionally, we introduce a new diverse dataset, BeanCount-1500, comprising of 24,684 images of 316 soybean varieties with various backgrounds and lighting conditions. Extensive experiments on BeanCount-1500 demonstrate the advantages of SPCN in soybean pod counting with an Mean Absolute Error(MAE) and an Mean Squared Error(MSE) of 4.37 and 6.45, respectively, significantly outperforming the current competing method by a substantial margin. Its excellent performance on the Renshou2021 dataset further confirms its outstanding generalization potential. Overall, the proposed method can provide technical support for intelligent breeding and planting management of soybean, promoting the digital and precise management of agriculture in general.

Keywords: computer vision; density map-based counting; soybean pod; hybrid dilated convolution; Convolutional Block Attention Module (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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