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Lin J, Zhang X, Qin Y, Yang S, Wen X, Cernava T, Migheli Q, Chen X. Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0208. [PMID: 39130161 PMCID: PMC11315374 DOI: 10.34133/plantphenomics.0208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/08/2024] [Indexed: 08/13/2024]
Abstract
Accurate identification of plant diseases is important for ensuring the safety of agricultural production. Convolutional neural networks (CNNs) and visual transformers (VTs) can extract effective representations of images and have been widely used for the intelligent recognition of plant disease images. However, CNNs have excellent local perception with poor global perception, and VTs have excellent global perception with poor local perception. This makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition tasks. In this paper, we propose a local and global feature-aware dual-branch network, named LGNet, for the identification of plant diseases. More specifically, we first design a dual-branch structure based on CNNs and VTs to extract the local and global features. Then, an adaptive feature fusion (AFF) module is designed to fuse the local and global features, thus driving the model to dynamically perceive the weights of different features. Finally, we design a hierarchical mixed-scale unit-guided feature fusion (HMUFF) module to mine the key information in the features at different levels and fuse the differentiated information among them, thereby enhancing the model's multiscale perception capability. Subsequently, extensive experiments were conducted on the AI Challenger 2018 dataset and the self-collected corn disease (SCD) dataset. The experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the AI Challenger 2018 dataset and the SCD dataset, with accuracies of 88.74% and 99.08%, respectively.
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Affiliation(s)
- Jianwu Lin
- Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
- Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University, Guizhou Provincial Science and Technology Department, Guiyang 550025, China
| | - Xin Zhang
- Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
- Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University, Guizhou Provincial Science and Technology Department, Guiyang 550025, China
| | - Yongbin Qin
- Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Shengxian Yang
- College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
- Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University, Guizhou Provincial Science and Technology Department, Guiyang 550025, China
| | - Xingtian Wen
- College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
| | - Tomislav Cernava
- School of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton S017 1BJ, UK
| | - Quirico Migheli
- Dipartimento di Agraria and NRD—Nucleo di Ricerca sulla Desertificazione, Università degli Studi di Sassari, Sassari, Italy
| | - Xiaoyulong Chen
- Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University, Guizhou Provincial Science and Technology Department, Guiyang 550025, China
- College of Life Sciences, Guizhou University, Guiyang 550025, China
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2
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Huang Z, Gouda M, Ye S, Zhang X, Li S, Wang T, Zhang J, Song X, Li X, He Y. Advanced deep learning algorithm for instant discriminating of tea leave stress symptoms by smartphone-based detection. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2024; 212:108769. [PMID: 38797010 DOI: 10.1016/j.plaphy.2024.108769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/18/2024] [Accepted: 05/21/2024] [Indexed: 05/29/2024]
Abstract
The primary challenges in tea production under multiple stress exposures have negatively affected its global market sustainability, so introducing an infield fast technique for monitoring tea leaves' stresses has tremendous urgent needs. Therefore, this study aimed to propose an efficient method for the detection of stress symptoms based on a portable smartphone with deep learning models. Firstly, a database containing over 10,000 images of tea garden canopies in complex natural scenes was developed, which included healthy (no stress) and three types of stress (tea anthracnose (TA), tea blister blight (TB) and sunburn (SB)). Then, YOLOv5m and YOLOv8m algorithms were adapted to discriminate the four types of stress symptoms; where the YOLOv8m algorithm achieved better performance in the identification of healthy leaves (98%), TA (92.0%), TB (68.4%) and SB (75.5%). Furthermore, the YOLOv8m algorithm was used to construct a model for differentiation of disease severity of TA, and a satisfactory result was obtained with the accuracy of mild, moderate, and severe TA infections were 94%, 96%, and 91%, respectively. Besides, we found that CNN kernels of YOLOv8m could efficiently extract the texture characteristics of the images at layer 2, and these characteristics can clearly distinguish different types of stress symptoms. This makes great contributions to the YOLOv8m model to achieve high-precision differentiation of four types of stress symptoms. In conclusion, our study provided an effective system to achieve low-cost, high-precision, fast, and infield diagnosis of tea stress symptoms in complex natural scenes based on smartphone and deep learning algorithms.
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Affiliation(s)
- Zhenxiong Huang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China; Department of Nutrition & Food Science, National Research Centre, Dokki, Giza, 12622, Egypt.
| | - Sitan Ye
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
| | - Xuechen Zhang
- College of Engineering, Anhui Agricultural University, Changjiangxi Road, Hefei, 230036, China.
| | - Siyi Li
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, 63 Xiyuangong Road, Fuzhou, 350100, China.
| | - Tiancheng Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
| | - Jin Zhang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang, 310008, China.
| | - Xinbei Song
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
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Shi S, Bao J, Guo Z, Han Y, Xu Y, Egbeagu UU, Zhao L, Jiang N, Sun L, Liu X, Liu W, Chang N, Zhang J, Sun Y, Xu X, Fu S. Improving prediction of N 2O emissions during composting using model-agnostic meta-learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 922:171357. [PMID: 38431167 DOI: 10.1016/j.scitotenv.2024.171357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/24/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Nitrous oxide (N2O) represents a significant environmental challenge as a harmful, long-lived greenhouse gas that contributes to the depletion of stratospheric ozone and exacerbates global anthropogenic greenhouse warming. Composting is considered a promising and economically feasible strategy for the treatment of organic waste. However, recent research indicates that composting is a source of N2O, contributing to atmospheric pollution and greenhouse effect. Consequently, there is a need for the development of effective, cost-efficient methodologies to quantify N2O emissions accurately. In this study, we employed the model-agnostic meta-learning (MAML) method to improve the performance of N2O emissions prediction during manure composting. The highest R2 and lowest root mean squared error (RMSE) values achieved were 0.939 and 18.42 mg d-1, respectively. Five machine learning methods including the backpropagation neural network, extreme learning machine, integrated machine learning method based on ELM and random forest, gradient boosting decision tree, and extreme gradient boosting were adopted for comparison to further demonstrate the effectiveness of the MAML prediction model. Feature analysis showed that moisture content of structure material and ammonium concentration during composting process were the two most significant features affecting N2O emissions. This study serves as proof of the application of MAML during N2O emissions prediction, further giving new insights into the effects of manure material properties and composting process data on N2O emissions. This approach helps determining the strategies for mitigating N2O emissions.
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Affiliation(s)
- Shuai Shi
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Jiaxin Bao
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Zhiheng Guo
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Yue Han
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Yonghui Xu
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Ugochi Uzoamaka Egbeagu
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Liyan Zhao
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Nana Jiang
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Lei Sun
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Xinda Liu
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Wanying Liu
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Nuo Chang
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Jining Zhang
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Yu Sun
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
| | - Xiuhong Xu
- College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China.
| | - Song Fu
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150030, China.
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Ang G, Zhiwei T, Wei M, Yuepeng S, Longlong R, Yuliang F, Jianping Q, Lijia X. Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees. FRONTIERS IN PLANT SCIENCE 2024; 15:1375118. [PMID: 38660450 PMCID: PMC11039839 DOI: 10.3389/fpls.2024.1375118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/19/2024] [Indexed: 04/26/2024]
Abstract
In order to address the challenges of inefficiency and insufficient accuracy in the manual identification of young citrus fruits during thinning processes, this study proposes a detection methodology using the you only look once for complex backgrounds of young citrus fruits (YCCB-YOLO) approach. The method first constructs a dataset containing images of young citrus fruits in a real orchard environment. To improve the detection accuracy while maintaining the computational efficiency, the study reconstructs the detection head and backbone network using pointwise convolution (PWonv) lightweight network, which reduces the complexity of the model without affecting the performance. In addition, the ability of the model to accurately detect young citrus fruits in complex backgrounds is enhanced by integrating the fusion attention mechanism. Meanwhile, the simplified spatial pyramid pooling fast-large kernel separated attention (SimSPPF-LSKA) feature pyramid was introduced to further enhance the multi-feature extraction capability of the model. Finally, the Adam optimization function was used to strengthen the nonlinear representation and feature extraction ability of the model. The experimental results show that the model achieves 91.79% precision (P), 92.75% recall (R), and 97.32% mean average precision (mAP)on the test set, which were improved by 1.33%, 2.24%, and 1.73%, respectively, compared with the original model, and the size of the model is only 5.4 MB. This study could meet the performance requirements for citrus fruit identification, which provides technical support for fruit thinning.
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Affiliation(s)
- Gao Ang
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an, Shandong, China
- Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China
| | - Tian Zhiwei
- Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China
| | - Ma Wei
- Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China
| | - Song Yuepeng
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an, Shandong, China
| | - Ren Longlong
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an, Shandong, China
| | - Feng Yuliang
- College of Engineering, China Agricultural University, Beijing, China
| | - Qian Jianping
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xu Lijia
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an, China
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Dong X, Zhao K, Wang Q, Wu X, Huang Y, Wu X, Zhang T, Dong Y, Gao Y, Chen P, Liu Y, Chen D, Wang S, Yang X, Yang J, Wang Y, Gao Z, Wu X, Bai Q, Li S, Hao G. PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science. Nucleic Acids Res 2024; 52:D1556-D1568. [PMID: 37897364 PMCID: PMC10767946 DOI: 10.1093/nar/gkad917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/21/2023] [Accepted: 10/13/2023] [Indexed: 10/30/2023] Open
Abstract
Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.
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Affiliation(s)
- Xinyu Dong
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Kejun Zhao
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Qi Wang
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
- Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, Guizhou University, Guiyang 550025, Guizhou, China
| | - Xingcai Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Yuanqin Huang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Xue Wu
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Tianhan Zhang
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Yawen Dong
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Yangyang Gao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Panfeng Chen
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Yingwei Liu
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Dongyu Chen
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Shuang Wang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Xiaoyan Yang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Jing Yang
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Yong Wang
- Department of Plant Pathology, Agriculture College, Guizhou University, Guiyang 550025, Guizhou, China
| | - Zhenran Gao
- New Rural Development Research Institute, Guizhou University, Guiyang 550025, Guizhou, China
| | - Xian Wu
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Qingrong Bai
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
| | - Shaobo Li
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Gefei Hao
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China
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Huang Q, Wu X, Wang Q, Dong X, Qin Y, Wu X, Gao Y, Hao G. Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0062. [PMID: 37396495 PMCID: PMC10308957 DOI: 10.34133/plantphenomics.0062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/06/2023] [Indexed: 07/04/2023]
Abstract
Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production, which benefits food production. Object detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating diseases. However, existing methods are still limited to single crop disease diagnosis. More importantly, the existing model has a large number of parameters, which is not conducive to deploying it to agricultural mobile devices. Nonetheless, reducing the number of model parameters tends to cause a decrease in model accuracy. To solve these problems, we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple crops. In detail, we design 2 strategies to build 4 different lightweight models as student models: the YOLOR-Light-v1, YOLOR-Light-v2, Mobile-YOLOR-v1, and Mobile-YOLOR-v2 models, and adopt the YOLOR model as the teacher model. We develop a multistage knowledge distillation method to improve lightweight model performance, achieving 60.4% mAP@ .5 in the PlantDoc dataset with small model parameters, outperforming existing methods. Overall, the multistage knowledge distillation technique can make the model lighter while maintaining high accuracy. Not only that, the technique can be extended to other tasks, such as image classification and image segmentation, to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart agriculture. Our code is available at https://github.com/QDH/MSKD.
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Affiliation(s)
- Qianding Huang
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Xingcai Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Qi Wang
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, Guizhou University, Guiyang 550025, China
| | - Xinyu Dong
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Yongbin Qin
- Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, Guizhou University, Guiyang 550025, China
| | - Xue Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- National Key Laboratory of Green Pesticide, Guizhou University, Guiyang 550025, China
| | - Yangyang Gao
- National Key Laboratory of Green Pesticide, Guizhou University, Guiyang 550025, China
| | - Gefei Hao
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- National Key Laboratory of Green Pesticide, Guizhou University, Guiyang 550025, China
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7
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Dong X, Wang Q, Huang Q, Ge Q, Zhao K, Wu X, Wu X, Lei L, Hao G. PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0054. [PMID: 37213546 PMCID: PMC10194370 DOI: 10.34133/plantphenomics.0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/25/2023] [Indexed: 05/23/2023]
Abstract
Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant diseases is critical to agricultural production. Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved plant disease detection and diagnosis for precision agriculture. In the meantime, most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves. However, the commonly used pre-trained models are from the computer vision dataset, not the botany dataset, which barely provides the pre-trained models sufficient domain knowledge about plant disease. Furthermore, this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision. To address this issue, we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis. In addition, we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification, plant disease detection, plant disease segmentation, and other subtasks. The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time, thereby supporting the better diagnosis of plant diseases. In addition, our pre-trained models will be open-sourced at https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293.
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Affiliation(s)
- Xinyu Dong
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Qi Wang
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
- Address correspondence to: (Q.W.); (G.H.)
| | - Qianding Huang
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Qinglong Ge
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Kejun Zhao
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Xingcai Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Xue Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
| | - Liang Lei
- The School of Physics and Optoelectronic Engineering,
Guangdong University of Technology, Guangzhou 510006, China
| | - Gefei Hao
- State Key Laboratory of Public Big Data, College of Computer Science and Technology,
Guizhou University, Guiyang 550025, China
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education,
Guizhou University, Guiyang 550025, China
- Address correspondence to: (Q.W.); (G.H.)
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