Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images

Front Plant Sci. 2023 Jan 8:14:1292643. doi: 10.3389/fpls.2023.1292643. eCollection 2023.


Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model's accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.

Keywords: abiotic stress; biotic stress; code-free models; deep learning; diseases; machine learning; microscopic images; tomato.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by University of Florida Informatics Institute seed funding. Manoj Choudhary acknowledges the Fellowship awarded through the Netaji Subhas - Indian Council of Agricultural Research International Fellowship for his doctoral studies.