Rice plant counting in paddy field based on UAV and density map regression

Pichao Liu; Xiaodong Bai; Laiding Zhao; Susong Gu

doi:10.1117/12.2674611

28 April 2023 Rice plant counting in paddy field based on UAV and density map regression

Pichao Liu, Xiaodong Bai, Laiding Zhao, Susong Gu

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 1262628 (2023) https://doi.org/10.1117/12.2674611
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

Rice is one of the most important food crops, and the stability of its yield is crucial to national food security. Rice plant counting becomes critical in fine agriculture research and is closely related to rice yield. In this paper, we proposed a new multi-scale fusion based rice counting method (RPCNet), which consists of one feature extractor frontend and two feature decoder modules namely Density Map Estimator (DME) and Plant Object Recognizer (POR). Fineloss is introduced into DME to improve the network's ability to separate adherent plants. To verify the validity of our method, we conducted experiments on a high-throughput rice plant image dataset. Experiment results show that the MAE and RMSE of the proposed RPCNet are 2.6 and 3.4 respectively, which outperforms states-of-the-art methods. Results suggest that RPCNet can accurately and efficiently estimate the number of rice plants and replace traditional manual counting.

Citation Download Citation

Pichao Liu, Xiaodong Bai, Laiding Zhao, and Susong Gu "Rice plant counting in paddy field based on UAV and density map regression", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 1262628 (28 April 2023); https://doi.org/10.1117/12.2674611

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KEYWORDS

Feature extraction

Unmanned aerial vehicles

Network architectures

Convolution

Deep learning

Image resolution

Semantics

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