BerryNet-Lite: A Lightweight Convolutional Neural Network for Strawberry Disease Identification

Jianping Wang (), Zhiyu Li, Guohong Gao, Yan Wang, Chenping Zhao, Haofan Bai, Yingying Lv, Xueyan Zhang and Qian Li
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Jianping Wang: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Zhiyu Li: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Guohong Gao: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Yan Wang: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Chenping Zhao: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Haofan Bai: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Yingying Lv: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Xueyan Zhang: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
Qian Li: School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China

Agriculture, 2024, vol. 14, issue 5, 1-25

Abstract: With the rapid advancements in computer vision, using deep learning for strawberry disease recognition has emerged as a new trend. However, traditional identification methods heavily rely on manual discernment, consuming valuable time and imposing significant financial losses on growers. To address these challenges, this paper presents BerryNet-Lite, a lightweight network designed for precise strawberry disease identification. First, a comprehensive dataset, encompassing various strawberry diseases at different maturity levels, is curated. Second, BerryNet-Lite is proposed, utilizing transfer learning to expedite convergence through pre-training on extensive datasets. Subsequently, we introduce expansion convolution into the receptive field expansion, promoting more robust feature extraction and ensuring accurate recognition. Furthermore, we adopt the efficient channel attention (ECA) as the attention mechanism module. Additionally, we incorporate a multilayer perceptron (MLP) module to enhance the generalization capability and better capture the abstract features. Finally, we present a novel classification head design approach which effectively combines the ECA and MLP modules. Experimental results demonstrate that BerryNet-Lite achieves an impressive accuracy of 99.45%. Compared to classic networks like ResNet34, VGG16, and AlexNet, BerryNet-Lite showcases superiority across metrics, including loss value, accuracy, precision, F 1-score, and parameters. It holds significant promise for applications in strawberry disease identification.

Keywords: deep learning; strawberry disease identification; lightweight; BerryNet-Lite; attention mechanism (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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