IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i20p14858-d1259315.html
   My bibliography  Save this article

Maize Disease Classification System Design Based on Improved ConvNeXt

Author

Listed:
  • Han Li

    (Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
    School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
    These authors contributed equally to this work.)

  • Mingyang Qi

    (Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
    These authors contributed equally to this work.)

  • Baoxia Du

    (Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China)

  • Qi Li

    (Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China)

  • Haozhang Gao

    (Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China)

  • Jun Yu

    (School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China)

  • Chunguang Bi

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Helong Yu

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Meijing Liang

    (Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA)

  • Guanshi Ye

    (Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China)

  • You Tang

    (Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
    School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China)

Abstract

Maize diseases have a great impact on agricultural productivity, making the classification of maize diseases a popular research area. Despite notable advancements in maize disease classification achieved via deep learning techniques, challenges such as low accuracy and identification difficulties still persist. To address these issues, this study introduced a convolutional neural network model named Sim-ConvNeXt, which incorporated a parameter-free SimAM attention module. The integration of this attention mechanism enhanced the ability of the downsample module to extract essential features of maize diseases, thereby improving classification accuracy. Moreover, transfer learning was employed to expedite model training and improve the classification performance. To evaluate the efficacy of the proposed model, a publicly accessible dataset with eight different types of maize diseases was utilized. Through the application of data augmentation techniques, including image resizing, hue, cropping, rotation, and edge padding, the dataset was expanded to comprise 17,670 images. Subsequently, a comparative analysis was conducted between the improved model and other models, wherein the approach demonstrated an accuracy rate of 95.2%. Notably, this performance represented a 1.2% enhancement over the ConvNeXt model and a 1.5% improvement over the advanced Swin Transformer model. Furthermore, the precision, recall, and F1 scores of the improved model demonstrated respective increases of 1.5% in each metric compared to the ConvNeXt model. Notably, using the Flask framework, a website for maize disease classification was developed, enabling accurate prediction of uploaded maize disease images.

Suggested Citation

  • Han Li & Mingyang Qi & Baoxia Du & Qi Li & Haozhang Gao & Jun Yu & Chunguang Bi & Helong Yu & Meijing Liang & Guanshi Ye & You Tang, 2023. "Maize Disease Classification System Design Based on Improved ConvNeXt," Sustainability, MDPI, vol. 15(20), pages 1-16, October.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14858-:d:1259315
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/20/14858/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/20/14858/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huub Spiertz, 2013. "Challenges for Crop Production Research in Improving Land Use, Productivity and Sustainability," Sustainability, MDPI, vol. 5(4), pages 1-13, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yonglin Shen & Xiuguo Liu, 2015. "Phenological Changes of Corn and Soybeans over U.S. by Bayesian Change-Point Model," Sustainability, MDPI, vol. 7(6), pages 1-23, May.
    2. Lu, Junsheng & Xiang, Youzhen & Fan, Junliang & Zhang, Fucang & Hu, Tiantian, 2021. "Sustainable high grain yield, nitrogen use efficiency and water productivity can be achieved in wheat-maize rotation system by changing irrigation and fertilization strategy," Agricultural Water Management, Elsevier, vol. 258(C).
    3. Ajay Kumar & Sushil Kumar & Komal & Nirala Ramchiary & Pardeep Singh, 2021. "Role of Traditional Ethnobotanical Knowledge and Indigenous Communities in Achieving Sustainable Development Goals," Sustainability, MDPI, vol. 13(6), pages 1-14, March.
    4. Rashmi Kumari & V. Devadas, 2017. "Modelling the dynamics of economic development driven by agricultural growth in Patna Region, India," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 6(1), pages 1-27, December.
    5. Jinmeng Zhang & Shiqiao Zhang & Min Cheng & Hong Jiang & Xiuying Zhang & Changhui Peng & Xuehe Lu & Minxia Zhang & Jiaxin Jin, 2018. "Effect of Drought on Agronomic Traits of Rice and Wheat: A Meta-Analysis," IJERPH, MDPI, vol. 15(5), pages 1-14, April.
    6. Lu, Junsheng & Hu, Tiantian & Geng, Chenming & Cui, Xiaolu & Fan, Junliang & Zhang, Fucang, 2021. "Response of yield, yield components and water-nitrogen use efficiency of winter wheat to different drip fertigation regimes in Northwest China," Agricultural Water Management, Elsevier, vol. 255(C).
    7. Abdulazeez Hudu Wudil & Asghar Ali & Khalid Mushtaq & Sajjad Ahmad Baig & Magdalena Radulescu & Piotr Prus & Muhammad Usman & László Vasa, 2023. "Water Use Efficiency and Productivity of Irrigated Rice Cultivation in Nigeria: An Application of the Stochastic Frontier Approach," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    8. László Szőllősi & Adél Dorottya Erdős, 2023. "Income and Asset Situation of Companies Producing Arable Crops in the Visegrad Countries," Agriculture, MDPI, vol. 13(8), pages 1-20, August.
    9. José Luis Villalpando-Aguilar & Daniel Francisco Chi-Maas & Itzel López-Rosas & Victor Ángel Aquino-Luna & Jesús Arreola-Enríquez & Julia Cristel Alcudia-Pérez & Gilberto Matos-Pech & Roberto Carlos G, 2022. "Urban Agriculture as an Alternative for the Sustainable Production of Maize and Peanut," Agriculture, MDPI, vol. 13(1), pages 1-13, December.
    10. Filipa Monteiro & Luís Catarino & Dora Batista & Bucar Indjai & Maria Cristina Duarte & Maria M. Romeiras, 2017. "Cashew as a High Agricultural Commodity in West Africa: Insights towards Sustainable Production in Guinea-Bissau," Sustainability, MDPI, vol. 9(9), pages 1-14, September.
    11. Pour, Nasim & Webley, Paul A. & Cook, Peter J., 2018. "Opportunities for application of BECCS in the Australian power sector," Applied Energy, Elsevier, vol. 224(C), pages 615-635.
    12. Bocca, Felipe Ferreira & Rodrigues, Luiz Henrique Antunes & Arraes, Nilson Antonio Modesto, 2015. "When do I want to know and why? Different demands on sugarcane yield predictions," Agricultural Systems, Elsevier, vol. 135(C), pages 48-56.
    13. Riccardo Testa & Anna Maria di Trapani & Filippo Sgroi & Salvatore Tudisca, 2014. "Economic Sustainability of Italian Greenhouse Cherry Tomato," Sustainability, MDPI, vol. 6(11), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14858-:d:1259315. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.