Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Vol. 13 No. sp1 (2026): Recent Advances in Agriculture

AI-driven disease detection in guava plants using teachable machine learning models

DOI
https://doi.org/10.14719/pst.11037
Submitted
1 August 2025
Published
16-01-2026

Abstract

Guava (Psidium guajava), a widely cultivated tropical fruit, is highly susceptible to various diseases such as anthracnose, rust, wilt and algal leaf spot, which can lead to significant yield losses and economic setbacks. Traditional methods of disease identification rely heavily on manual inspection, which is time-consuming, subjective and dependent on expert availability, thereby making timely intervention difficult, particularly in rural or resource-limited areas. The integration of artificial intelligence (AI) and image-based machine learning (ML) offers a promising solution to automate and enhance the accuracy of disease detection in plants. This study explores the use of Googles’ teachable machine (GTM), a no-code AI platform for developing a user-friendly, image-based classification model to identify guava leaf diseases. A dataset comprising 600 images, categorised into five classes (healthy, anthracnose, rust, algal leaf spot and wilt), was used to train the model. The model achieved an overall classification accuracy of 96.2 %, with class-wise accuracies ranging from 94.2 % to 97.5 %. Confusion matrix analysis indicated strong predictive capability, with minor misclassifications occurring between visually similar disease symptoms. The results validate the feasibility of using teachable machine for real-world agricultural applications, particularly in field-level diagnosis where accessibility to advanced tools or expertise is limited. This AI-driven approach not only enhances the accuracy and speed of disease detection but also empowers farmers and agricultural workers with a practical tool for timely crop health management. The study underscores the potential of accessible AI platforms in promoting precision agriculture and sustainable fruit production.

References

  1. 1. Murthy GRK. Teachable machine: A no-code machine learning google-based AI application. MOOC on Artificial Intelligence in Agriculture organized by ICAR-NAARM, Hyderabad. 2025. p. 1-28.
  2. 2. Hanif T, Murad R, Amam RS, Ishrat Fatima MZ. Role of horticulture in addressing food security and global nutrition challenges. Cornous Biol. 2024;2(1):45-51. https://doi.org/10.37446/corbio/ra/2.1.2024.45-51
  3. 3. Boora RS. Improvement in guava (Psidium guajava L.): A review. Agric Rev. 2012;33(4):341-9.
  4. 4. Thriveni V, Mishra A, Roy A, Viswanath M. Doubling the guava farming-based farmers’ income through enhancing productivity using hi-tech practices. Pharma Innov J. 2021;10(8):358-62.
  5. 5. Rodríguez-Lira DC, Córdova-Esparza DM, Álvarez-Alvarado JM, Terven J, Romero-González JA, Rodríguez-Reséndiz J. Trends in machine and deep learning techniques for plant disease identification: A systematic review. Agriculture. 2024;14(12):2188. https://doi.org/10.3390/agriculture14122188
  6. 6. Sahu K, Khunte SD, Chandrakar YK, Singh A. Advances in guava crop regulation: A review of recent research and developments. Plant Arch. 2025;25(1):557-64. https://doi.org/10.51470/PLANTARCHIVES.2025.v25.supplement-1.075
  7. 7. Almadhor A, Rauf HT, Lali MIU, Damaševičius R, Alouffi B, Alharbi A. AI-driven framework for recognition of guava plant diseases through machine learning from DSLR camera sensor-based high-resolution imagery. Sensors. 2021;21(11):3830. https://doi.org/10.3390/s21113830
  8. 8. Kumar S. Plant disease management in India: Advances and challenges. Afr J Agric Res. 2014;9(15):1207-17. https://doi.org/10.5897/AJAR2014.7311
  9. 9. Paramesha K, Jalapur S, Hanok S, Puttegowda K, Manjunatha G, Kumara B. Machine learning and deep learning approaches for guava disease detection. SN Comput Sci. 2025;6(4):361. https://doi.org/10.1007/s42979-025-03886-6
  10. 10. Tewari VK, Shrivastava AK. An industry perspective of tractor safety against rollover. AMA Agric Mech Asia Afr Lat Am. 2013;44(3):61-9.
  11. 11. Tiwari P, Shrivastava AK, Dave AK. Anthropometry of tribal farm women to improvise hand tool designs for Bastar district of Chhattisgarh. J Agric Eng. 2021;58(3):205-21. https://doi.org/10.52151/jae2021581.1746
  12. 12. Shrivastava AK, Mehta CR, Pandey MM, Shrivastava AK. Accessibility index of Indian tractors - A case study. AMA Agric Mech Asia Afr Lat Am. 2010;41(3):17-20.
  13. 13. Sharma A, Jain A, Gupta P, Chowdary V. Machine learning applications for precision agriculture: A comprehensive review. IEEE Access. 2020;9:4843-73. https://doi.org/10.1109/ACCESS.2020.3048415
  14. 14. Lu J, Tan L, Jiang H. Review on convolutional neural network applied to plant leaf disease classification. Agriculture. 2021;11(8):707. https://doi.org/10.3390/agriculture11080707
  15. 15. Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016;7:1419. https://doi.org/10.3389/fpls.2016.01419
  16. 16. Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. 2018;145:311-18. https://doi.org/10.1016/j.compag.2018.01.009
  17. 17. Sai S, Kumar S, Gaur A, Goyal S, Chamola V, Hussain A. Unleashing the power of generative AI in Agriculture 4.0 for smart and sustainable farming. Cogn Comput. 2025;17(1):63. https://doi.org/10.1007/s12559-025-10420-6
  18. 18. Nandi RN, Palash AH, Siddique N, Zilani MG. Device-friendly guava fruit and leaf disease detection using deep learning. In: Int Conf Mach Intell Emerg Technol. 2022. p. 49-59. https://doi.org/10.1007/978-3-031-34619-4_5
  19. 19. Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable Machine: Approachable web-based tool for exploring machine learning classification. Ext Abstr CHI Conf Hum Factors Comput Syst. 2020. p. 1-8. https://doi.org/10.1145/3334480.3382839
  20. 20. Saleema AA, Kannan R, Kumar P. Image segmentation techniques for guava leaf disease detection and classification. J Image Process Agric. 2021;3(1):20-27.
  21. 21. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv Prepr. 2017; arXiv:1704.04861.
  22. 22. Too EC, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric. 2019;161:272-79. https://doi.org/10.1016/j.compag.2018.03.032
  23. 23. Brahimi M, Boukhalfa K, Moussaoui A. Deep learning for tomato diseases: Classification and symptoms visualization. Appl Artif Intell. 2017;31(4):299-315. https://doi.org/10.1080/08839514.2017.1315516
  24. 24. Zhang X, Qiao Y, Meng F, Fan C, Zhang M. Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access. 2018;6:30370-77. https://doi.org/10.1109/ACCESS.2018.2844405
  25. 25. Dhaka VS, Meena SV, Rani G, Sinwar D, Ijaz MF, Woźniak M. A survey of deep convolutional neural networks applied for prediction of plant leaf diseases. Sensors. 2021;21(14):4749. https://doi.org/10.3390/s21144749
  26. 26. Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plümer L. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric. 2010;74(1):91-99. https://doi.org/10.1016/j.compag.2010.06.009
  27. 27. Saleem MH, Potgieter J, Arif KM. Plant disease classification: A comparative evaluation of convolutional neural networks and deep learning optimizers. Plants. 2020;9(10):13-19. https://doi.org/10.3390/plants9101319
  28. 28. Singh A, Verma S, Kumar V. SidNet: A hybrid deep learning model for early detection of guava leaf blight. AI Precis Agric. 2023;2(1):33-42.
  29. 29. Barbedo JG. Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng. 2019;180:96-107. https://doi.org/10.1016/j.biosystemseng.2019.02.002

Downloads

Download data is not yet available.