A robotic monitoring system for diseases of pepper in greenhouse

Schor, Noa; Berman, Sigal; Dombrovsky, Aviv; Elad, Y.; Ignát, Tímea; Bechar, Avital

doi:10.3920/978-90-8686-814-8_78

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…This may be due to early disease symptoms being observed first on the lower side of the leaf and expanding to the upper side of the leaf (Osdaghi et al, 2021). 2) It has also been observed that the lower side of the leaf has more characteristic early and uniform symptoms than other plant parts (Schor et al, 2016). In our study, tomato fruit pox, a genetic disorder (Crill et al, 1973) that produces an incipient oval-shaped lesion, had the lowest classification performance in all databases.…”

Section: Discussionmentioning

confidence: 62%

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

Choudhary,

Sentil,

Jones

et al. 2023

Front. Plant Sci.

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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.

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Section: Discussionmentioning

confidence: 62%