Research on Rapeseed Seedling Counting Based on an Improved Density Estimation Method

Qi Wang, Chunpeng Li, Lili Huang, Liqing Chen, Quan Zheng and Lichao Liu ()
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Qi Wang: College of Engineering, Anhui Agricultural University, Hefei 230036, China
Chunpeng Li: College of Engineering, Anhui Agricultural University, Hefei 230036, China
Lili Huang: College of Engineering, Anhui Agricultural University, Hefei 230036, China
Liqing Chen: College of Engineering, Anhui Agricultural University, Hefei 230036, China
Quan Zheng: College of Engineering, Anhui Agricultural University, Hefei 230036, China
Lichao Liu: College of Engineering, Anhui Agricultural University, Hefei 230036, China

Agriculture, 2024, vol. 14, issue 5, 1-16

Abstract: The identification of seedling numbers is directly related to the acquisition of seedling information, such as survival rate and emergence rate. It indirectly affects detection efficiency and yield evaluation. Manual counting methods are time-consuming and laborious, and the accuracy is not high in complex backgrounds or high-density environments. It is challenging to achieve improved results using traditional target detection methods and improved methods. Therefore, this paper adopted the density estimation method and improved the population density counting network to obtain the rapeseed seedling counting network named BCNet. BCNet uses spatial attention and channel attention modules and enhances feature information and concatenation to improve the expressiveness of the entire feature map. In addition, BCNet uses a 1 × 1 convolutional layer for additional feature extraction and introduces the torch.abs function at the network output port. In this study, distribution experiments and seedling prediction were conducted. The results indicate that BCNet exhibits the smallest counting error compared to the CSRNet and the Bayesian algorithm. The MAE and MSE reach 3.40 and 4.99, respectively, with the highest counting accuracy. The distribution experiment and seedling prediction showed that, compared with the other density maps, the density response points corresponding to the characteristics of the seedling region were more prominent. The predicted number of the BCNet algorithm was closer to the actual number, verifying the feasibility of the improved method. This could provide a reference for the identification and counting of rapeseed seedlings.

Keywords: density estimation; rapeseed seedling; counting; density map (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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