Prof. Shaomin Mu Profile

Prof. Shaomin Mu

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Proceedings Article | 11 October 2023 Paper

Lightweight image recognition of peach tree pests based on global context modeling

Cheng Min Lei, Hong Wei Lv, Hui Chen, Xui Mei Wang, Shao Min Mu

Proceedings Volume 12800, 1280039 (2023) https://doi.org/10.1117/12.3004274

KEYWORDS: Modeling, Visualization, Visual process modeling, Image enhancement, Feature extraction, Data modeling, Image classification, Convolution, Image processing, Performance modeling

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Timely and accurate identification of peach tree pests and assistance in pest control is the key to promoting the healthy development of the peach industry. A lightweight peach tree pest image recognition model based on global context modeling is proposed for existing pest image recognition models with high structural complexity, high dependence on computational resources at runtime, and deployment difficulties. First, a lightweight global context modeling module is embedded in the ShuffleNet V2 unit to aggregate all the location information of the input image, obtain global context features, correct the pest information, and improve the feature representation capability of the model. Then, the ordinary convolution layer of ShuffleNet V2 is optimized to add lightweight convolution to improve the feature extraction ability of the model at different stages. Finally, a softmax classifier is connected to obtain a lightweight peach tree pest image recognition model. After experimental verification, the proposed model outperforms neural networks such as AlexNet in terms of recognition accuracy, parameter computation and floating point operation, and the number of parameters is only one-tenth or even a few tenths of AlexNet, which is a lightweight and efficient neural network model that can be easily deployed.

Proceedings Article | 25 May 2023 Paper

Fertilizer sales forecasting model based on transformer-BiGRU

Enquan Cui, Shaomin Mu, Min Hou, Hongwei Lyu, Aiju Shi

Proceedings Volume 12636, 126361U (2023) https://doi.org/10.1117/12.2675308

KEYWORDS: Transformers, Feature extraction, Agriculture

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Forecasting company's future fertilizer sales based on fertilizer sales data are important for reducing production costs for fertilizer companies. Fertilizer sales data have the feature of contextual time-series correlation, and the information of time-series correlation is not considered in the time series (fertilizer price, sales volume, etc.) prediction, which leads to low prediction accuracy. To address the above problems, a Transformer-BiGRU model is pro-posed in this paper. Transformer consists of a multi-headed self-attention, which is capable of mining the multi-feature dependencies between data, and is therefore chosen for multi-feature extraction of fertilizer sales data. However, parameters of transformer are passed in one direction, which cannot extract the temporal information well. Therefore, Bi-directional Gated Recurrent Unit (BiGRU) is introduced to extract the temporal correlation of fertilizer sales data by using its own forward and backward propagation to solve the former problem of poor ex-traction of contextual temporal information. The experimental results show that the Transformer-BiGRU model has the advantage of extracting the temporal correlation of multi-feature data and improves the accuracy of fertilizer sales prediction.

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