Plant disease detection using ensembled CNN framework

• Autorzy: Mondal Subhash , Banerjee Suharta , Mukherjee Subinoy , Sengupta Diganta

Identyfikatory

DOI

10.7494/csci.2022.23.3.4376

• Języki publikacji: EN

Abstrakty

EN

Agriculture exhibits the prime driving force for the growth of agro-based economies globally. In agriculture, detecting and preventing crops from the attacks of pests is a primary concern in today’s world. The early detection of plant disease becomes necessary in order to avoid the degradation of the yield of crop production. In this paper, we propose an ensemble-based convolutional neural network (CNN) architecture that detects plant disease from the images of a plant’s leaves. The proposed architecture considers CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). This approach helped us build a generalized model for disease detection with an accuracy of 97.9% under test conditions.

Słowa kluczowe

EN

convolutional neural network; disease detection; ResNet-50; VGG-19; InceptionV3

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2022
• Tom: T. 23 (3)
• Strony: 321--333
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Mondal Subhash

• Department of Computer Science & Engineering, Meghnad Saha Institute of Technology, Kolkata, India 700150

autor

Banerjee Suharta

• Department of Computer Science & Engineering, Meghnad Saha Institute of Technology, Kolkata, India 700150

autor

Mukherjee Subinoy

• Department of Computer Science & Engineering, Meghnad Saha Institute of Technology, Kolkata, India 700150

autor

Sengupta Diganta

• Department of Computer Science & Engineering, Meghnad Saha Institute of Technology, Kolkata, India 700150
• Department of Computer Science & Business Systems, Meghnad Saha Institute of Technology, Kolkata, India 700150

Bibliografia

• [1] Andrianto H., Suhardi, Faizal A., Armandika F.: Smartphone Application for Deep Learning-Based Rice Plant Disease Detection. In: 2020 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 387–392, 2020. doi: 10.1109/ICITSI50517.2020.9264942.
• [2] Burhan S.A., Minhas S., Tariq A., Hassan M.N.: Comparative Study of Deep Learning Algorithms for Disease and Pest Detection in Rice Crops. In: 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–5, 2020. doi: 10.1109/ECAI50035.2020.9223239.
• [3] Ferentinos K.P.: Deep learning models for plant disease detection and diagnosis, Computers and Electronics in Agriculture, vol. 145, pp. 311–318, 2018. doi: 10. 1016/j.compag.2018.01.009.
• [4] Habiba S.U., Islam M.K.: Tomato Plant Diseases Classification Using Deep Learning Based Classifier From Leaves Images. In: 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), pp. 82–86, 2021. doi: 10.1109/ICICT4SD50815.2021.9396883.
• [5] He K., Zhang X., Ren S., Sun J.: Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016. doi: 10.1109/CVPR.2016.90.
• [6] Huang S., Liu W., Qi F., Yang K.: Development and Validation of a Deep Learning Algorithm for the Recognition of Plant Disease. In: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1951–1957, 2019. doi: 10.1109/HPCC/SmartCity/DSS.2019.00269.
• [7] Ioffe S., Szegedy C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, vol. 37, pp. 448–456, 2015.
• [8] Jasim M.A., AL-Tuwaijari J.M.: Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques. In: 2020 International Conference on Computer Science and Software Engineering (CSASE), pp. 259–265, 2020. doi: 10.1109/CSASE48920.2020.9142097.
• [9] Karim S.: Cotton leaf disease dataset. https://www.kaggle.com/seroshkarim/ cotton-leaf-disease-dataset.
• [10] Lamrahi N.: Kaggle: New Plant Diseases Dataset(Augmented). https://www. kaggle.com/noulam/tomato.
• [11] Marzougui F., Elleuch M., Kherallah M.: A Deep CNN Approach for Plant Disease Detection. In: 2020 21st International Arab Conference on Information Technology (ACIT), pp. 1–6, 2020. doi: 10.1109/ACIT50332.2020.9300072.
• [12] Mohanty S.P., Hughes D.P., Salathe M.: Using Deep Learning for Image-Based Plant Disease Detection, Frontiers in Plant Science, vol. 7, 2016. doi: 10.3389/ fpls.2016.01419.
• [13] Narvekar P., Patil S.N.: Novel algorithm for grape leaf diseases detection, International Journal of Engineering Research and General Science, vol. 3(1), pp. 1240–1244, 2015.
• [14] Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826, 2016.
• [15] Wang Q., He G., Li F., Zhang H.: A novel database for plant diseases and pests classification. In: 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–5, 2020. doi: 10.1109/ ICSPCC50002.2020.9259502.
• [16] Yashwanth M., Chandra M.L., Pallavi K., Showkat D., Kumar P.S.: Agriculture Automation using Deep Learning Methods Implemented using Keras. In: 2020 IEEE International Conference for Innovation in Technology (INOCON), pp. 1–6, 2020. doi: 10.1109/INOCON50539.2020.9298415.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).