数字农科院2.0

RepDI: A light-weight CPU network for apple leaf disease identification

文献类型： 外文期刊

作者： Jiye Zheng;Kaiyu Li;Wenbin Wu;Huaijun Ruan

作者机构：

关键词： Deep learning;Disease identification;Image processing;Lightweight model

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2023 年 212 卷

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： Apple disease is one of the major factors affecting apple production, and the visual diagnosis of apple leaves is an efficient disease identification solution. In this paper, we propose an efficient lightweight model based on structural reparameterization for apple leaf disease identification, called RepDI for short. To achieve faster inference on the CPU devices, we introduce depth-wise separable convolution and structural reparameterization technology in RepDI, which has different structures during training and inference. In addition, to better capture diseased leaves and disease regions in complex contexts, we propose the parallel dilated attention mechanism module and embed it into RepDI. Experiments show that RepDI can achieve state-of-the-art performance in disease identification task, compared to most lightweight models. Meanwhile, RepDI achieves the fastest inference speed on our desktop CPU, which is an important factor in practical applications. Furthermore, we collect and annotate a novel dataset for apple leaf diseases from real scenarios, called Real-ALD, which is more challenging than previous datasets. And RepDI achieves a top-1 accuracy of 98.92 in the Real-ALD dataset under a limited training configuration. Our code is released to contribute to the plant protection community and we will further explore the potential of RepDI for down-stream detection, segmentation tasks.

分类号：

  • 相关文献

[1]YOLO-WDNet: A lightweight and accurate model for weeds detection in cotton field. Xiangpeng Fan,Tan Sun,Xiujuan Chai,Jianping Zhou. 2024

[2]Lightweight Structure and Attention Fusion for In-Field Crop Pest and Disease Detection. Zijing Luo,Yunsen Liang,Naimin Kong,Lirui Liang,Wenjun Peng,Yujie Yao,Chi Qin,Xiaohan Lu,Mingman Xu,Yining Zhang,Chenyang Lin,Chengyao Jiang,Mengyao Li,Yangxia Zheng,Yameng Jiang,Wei Lu. 2025

[3]A novel method for maize leaf disease classification using the RGB-D post-segmentation image data. Nan, Fei,Song, Yang,Yu, Xun,Nie, Chenwei,Liu, Yadong,Bai, Yali,Zou, Dongxiao,Wang, Chao,Yin, Dameng,Yang, Wude,Jin, Xiuliang. 2023

[4]Intelligent vineyard blade density measurement method incorporating a lightweight vision transformer. Shan Ke,Guowei Dai,Hui Pan,Bowen Jin. 2024

[5]Exploring the potential of nanomaterials (NMs) as diagnostic tools and disease resistance for crop pathogens. Muhammad Jabran,Muhammad Amjad Ali,Saima Muzammil,Adil Zahoor,Faizan Ali,Sarfaraz Hussain,Ghulam Muhae-Ud-Din,Munazza Ijaz,Li Gao. 2024

[6]Nondestructive Detection and Quality Grading System of Walnut Using X-Ray Imaging and Lightweight WKNet. Xiangpeng Fan,Jianping Zhou. 2025

[7]AI-Powered Embedded System for Rapid Detection of Veterinary Antibiotic Residues in Food-Producing Animals. Ximing Li,Lanqi Chen,Qianchao Wang,Mengting Zhou,Jingheng Long,Xi Chen,Jiangsan Zhao,Junjun Yu,Yubin Guo. 2025

[8]Method for Dairy Cow Target Detection and Tracking Based on Lightweight YOLO v11. Li, Zhongkun,Cheng, Guodong,Yang, Lu,Han, Shuqing,Wang, Yali,Dai, Xiaofei,Fang, Jianyu,Wu, Jianzhai. 2025

[9]Design and Experiment of an Automatic Detection System for Cotton Field Pest and Seedling Information. Hua, Wu Hai,Bo, Zhao,Jun, Li Shu,Hua, Mao Wen,Chao, Zhang Xiao. 2014

[10]Comparison of Color Model in Cotton Image Under Conditions of Natural Light. Zhang, J. H.,Kong, F. T.,Wu, J. Z.,Wang, S. W.,Liu, J. J.,Zhao, P.. 2016

[11]Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images. Li Shao-kun,Wang Ke-ru,Wang Chong-tao,Suo Xing-mei,Jiang Ying-tao,Yang Mei. 2010

[12]DETECTION AND POSITION METHOD OF APPLE TREE IMAGE. Mao, Wenhua,Zhang, Xiaochao,Hub, Xiaoan,Mao, Wenhua,Jia, Baoping. 2009

[13]Motion Blurring Direction Identification Based on Second-Order Difference Spectrum. Zhang, Junxiong,Li, Wei,He, Fen. 2011

[14]Application of support vector machine for detecting rice diseases using shape and color texture features. Qing Yao , Zexin Guan , Yingfeng Zhou , Jian Tang , Yang Hu , Baojun Yang. 2009

[15]Diagnostic model for wheat leaf conditions using image features and a support vector machine. K. Du, Z. Sun, Y. Li, F. Zheng, J. Chu, Y. Su. 2016

[16]An Experimental Study of Axial Poisson's Ratio and Axial Young's Modulus Determination of Potato Stems Using Image Processing. Fan, Jiali,Li, Yuyao,Wang, Bing,Gu, Fengwei,Wu, Feng,Yang, Hongguang,Yu, Zhaoyang,Hu, Zhichao. 2022

[17]High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing. Zhang, Huichun,Wang, Lu,Jin, Xiuliang,Bian, Liming,Ge, Yufeng. 2023

[18]Semantic segmentation-based mechanized harvesting soybean quality detection. Jin, Chengqian,Liu, Shikun,Chen, Man. 2022

[19]Image Recognition of Male Oilseed Rape (Brassica napus) Plants Based on Convolutional Neural Network for UAAS Navigation Applications on Supplementary Pollination and Aerial Spraying. Zhu Sun,Xiangyu Guo,Yang Xu,Songchao Zhang,Xiaohui Cheng,Qiong Hu,Wenxiang Wang,Xinyu Xue. 2022

[20]Changes in Vertical Phenotypic Traits of Rice (Oryza sativa L.) Response to Water Stress. Yufan Zhang,Yuanyuan Zha,Xiuliang Jin,Yu Wang,Han Qiao. 2022

作者其他论文 更多>>