Using Google Teachable Machine for the Classification of Wheat Leaf Rust and Stripe Rust
DOI:
https://doi.org/10.55627/pbulletin.005.01.1688Keywords:
Google Teachable Machine, MobileNet, wheat leaf rust, wheat stripe rust, deep learning, plant disease classificationAbstract
Wheat rust diseases, particularly leaf rust (Puccinia triticina) and stripe rust (Puccinia striiformis f. sp. tritici), are major biotic stresses in wheat production. The present study applied the deep learning technology of Google Teachable Machine for automatic detection and classification of two of these rust diseases. Images of diseased wheat plants were acquired, preprocessed, and labeled. The MobileNet architecture was used for training the model; thereafter, the performance was evaluated based on achieving 97–98% across accuracy, precision, recall, and F1-score on the test set. Data augmentation and adjustment of hyperparameters improved the performance of the model. After training, the model was allowed to be converted into TFLite format for usage on mobile, enabling the detection of the disease in real time during the field visit. This approach has shown that AI could be applied in early detection of crop diseases. This no-code approach highlights potential for accessible early disease detection, though field validation is needed.
References
Adesogan AT, Havelaar AH, McKune SL, Eilittä M, Dahl GE, 2020. Animal source foods: Sustainability problem or malnutrition and sustainability solution? Perspective matters. Global Food Security, 25: 100325.
Afzal F, Chaudhari SK, Gul A, Farooq A, Ali H, Nisar S, Sarfraz B, Shehzadi KJ, Mujeeb-Kazi A, 2015. Bread Wheat (Triticum aestivum L.) Under Biotic and Abiotic Stresses: An Overview. In: Crop Production and Global Environmental Issues. Springer, pp. 293-317.
Ahmed SF, Alam MdSB, Hassan M, Rozbu MR, Ishtiak T, Rafa N, Mofijur M, Shawkat Ali ABM, Gandomi AH, 2023. Deep learning modelling techniques: Current progress, applications, advantages, and challenges. Artif. Intell. Rev., 56(11): 13521-13617.
Alomar K, Aysel HI, Cai X, 2023. Data Augmentation in Classification and Segmentation: A Survey and New Strategies. J. Imaging, 9(2): 46.
Ammar MK, Hanafi RS, Choucry MA, Handoussa H, 2023. Structural, functional, nutritional composition and analytical profiling of Triticum aestivum L. Appl. Biol. Chem., 66(1): 48.
Bagga M, Goyal S, 2024. A Comprehensive Study of Public Datasets Used in Precision Agriculture to Classify Diseased and Healthy Crop Leaves. In: 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, pp. 860-864.
Boursianis AD, Papadopoulou MS, Diamantoulakis P, Liopa-Tsakalidi A, Barouchas P, Salahas G, Karagiannidis G, Wan S, Goudos SK, 2022. Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet Things, 18: 100187.
Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, Jongejan J, Pitaru A, Chen A, 2020. Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-8.
Chang S, Yang G, Cheng J, Feng Z, Fan Z, Ma X, Li Y, Yang X, Zhao C, 2024. Recognition of wheat rusts in a field environment based on improved DenseNet. Biosyst. Eng., 238: 10-21.
Chen X, 2020. Pathogens which threaten food security: Puccinia striiformis, the wheat stripe rust pathogen. Food Secur., 12(2): 239-251.
Choudhary M, Sentil S, Jones JB, Paret ML, 2023. Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images. Front. Plant Sci., 14: 1292643.
Demilie WB, 2024. Plant disease detection and classification techniques: A comparative study of the performances. J. Big Data, 11(1): 5.
Figueroa M, Hammond-Kosack KE, Solomon PS, 2018. A review of wheat diseases—a field perspective. Curr. Opin. Plant Biol., 43: 142-149.
Giller KE, Delaune T, Silva JV, Descheemaeker K, Van De Ven G, Schut AGT, Van Wijk M, Hammond J, Hochman Z, Taulya G, Chikowo R, Narayanan S, Kishore A, Bresciani F, Teixeira HM, Andersson JA, Van Ittersum MK, 2021. The future of farming: Who will produce our food? Food Secur., 13(5): 1073-1099.
John MA, Bankole I, Ajayi-Moses O, Ijila T, Jeje T, Lalit P, 2023. Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review. Am. J. Plant Sci., 14(11): 1260-1295.
Kurz TL, Jayasuriya S, Swisher K, Mativo J, Pidaparti R, Robinson DT, 2024. The Impact of Teachable Machine on Middle School Teachers’ Perceptions of Science Lessons after Professional Development. Educ. Sci., 14(4): 417.
Li N, Mayes J, Yu P, 2021. ML Tools for the Web: A Way for Rapid Prototyping and HCI Research. In: Artificial Intelligence for Human Computer Interaction: A Modern Approach. Springer, pp. 315-343.
Liu L, Qiao S, Chang J, Ding W, Xu C, Gu J, Sun T, Qiao H, 2024. A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images. Heliyon, 10(7): e28264.
Long J, Court T, Sedgwick J, Ray R, 2023. Classification of wheat diseases using deep learning networks with field and glasshouse images. Plant Pathol., 72(4): 726-738.
Mi Z, Zhang X, Su J, Han D, Su B, 2020. Wheat stripe rust grading by deep learning with attention mechanism and images from mobile devices. Front. Plant Sci., 11: 558126.
Mosqueira-Rey E, Pereira EH, Alonso-Ríos D, Bobes-Bascarán J, 2022. A classification and review of tools for developing and interacting with machine learning systems. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. ACM, pp. 1092-1101.
Nair KP, 2023. Biodiversity in Agriculture: Sustainability of Soil, Soil Fauna and Soil Flora. Springer Nature Switzerland.
Nigus EA, Taye GB, Girmaw DW, Salau AO, 2023. Development of a Model for Detection and Grading of Stem Rust in Wheat Using Deep Learning. Multimed. Tools Appl., 83(16): 47649-47676.
Q Gao M, Wu P, Yan J, Li S, 2021. A deep-learning-based approach for wheat yellow rust disease recognition from unmanned aerial vehicle images. Sensors, 21(19): 6540.
Ristaino JB, Anderson PK, Bebber DP, Brauman KA, Cunniffe NJ, Fedoroff NV, Finegold C, Garrett KA, Gilligan CA, Jones CM, Martin MD, MacDonald GK, Neenan P, Records A, Schmale DG, Tateosian L, Wei Q, 2021. The persistent threat of emerging plant disease pandemics to global food security. Proc. Natl. Acad. Sci. USA, 118(23): e2022239118.
Sarker IH, 2021. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Comput. Sci., 2(6): 420.
Schirrmann M, Landwehr N, Giebel A, Garz A, Dammer K-H, 2021. Early detection of stripe rust in winter wheat using deep residual neural networks. Front. Plant Sci., 12: 469689.
Shafi U, Mumtaz R, Shafaq Z, Zaidi SMH, Kaifi MO, Mahmood Z, Zaidi SAR, 2022. Wheat rust disease detection techniques: A technical perspective. J. Plant Dis. Prot., 129(3): 489-504.
Singh R, Mahmoudpour A, Rajkumar M, Narayana R, 2017. A review on stripe rust of wheat, its spread, identification and management at field level. Res. Crops, 18(3): 528.
Tobin J, Fong R, Ray A, Schneider J, Zaremba W, Abbeel P, 2017. Domain randomization for transferring deep neural networks from simulation to the real world. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 23-30.
Tzachor A, Devare M, King B, Avin S, Ó hÉigeartaigh S, 2022. Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat. Mach. Intell., 4(2): 104-109.
Wen X, Zeng M, Chen J, Maimaiti M, Liu Q, 2023. Recognition of wheat leaf diseases using lightweight convolutional neural networks against complex backgrounds. Life, 13(11): 2125.
Zahoor I, Ahmad Wani S, Ganaie TA, 2024. Artificial Intelligence in the Food Industry: Enhancing Quality and Safety. 1st ed.
Zeng Q, Ma X, Cheng B, Zhou E, Pang W, 2020. GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning. IEEE Access, 8: 172882-172891.
Zhang X, Han L, Dong Y, Shi Y, Huang W, Han L, González-Moreno P, Ma H, Ye H, Sobeih T, 2021. A deep-learning-based approach for wheat yellow rust disease recognition from unmanned aerial vehicle images. Remote Sens., 13(16): 3129.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ammara Arba Awan, Salman Ahmad, Furqan ur Rehman, Muhammad Ehetisham ul Haq, Qamar Anser Tufail Khan, Muhammad Burhan, Hafiz Muhammad Zia Ullah Ghazali, Sumera Naz, Muhammad Makky Javaid, Mumtaz Hussain, Muhammad Rizwan Khurshid
This work is licensed under a Creative Commons Attribution 4.0 International License.
