An App for Fungal Disease Detection on Plants

Moloo, Raj Kishen; Caleechurn, Keshav

doi:10.1109/iconat53423.2022.9725839

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The applications presented in [10,12,26] concern classification with a higher number of disease cases. The accuracy achieved in [12][13][14]24] was lower than that achieved in our approach. The accuracy achieved in the rest of the approaches was comparable to ours or higher, but most of these approaches support a smaller number of diseases, except for [10,26], where a higher accuracy was achieved for a relatively large number of diseases.…”

Section: Discussioncontrasting

confidence: 73%

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Mobile Plant Disease Classifier, Trained with a Small Number of Images by the End User

et al. 2022

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Mobile applications that can be used for the training and classification of plant diseases are described in this paper. Professional agronomists can select the species and their diseases that are supported by the developed tool and follow an automatic training procedure using a small number of indicative photographs. The employed classification method is based on features that represent distinct aspects of the sick plant such as, for example, the color level distribution in the regions of interest. These features are extracted from photographs that display a plant part such as a leaf or a fruit. Multiple reference ranges are determined for each feature during training. When a new photograph is analyzed, its feature values are compared with the reference ranges, and different grades are assigned depending on whether a feature value falls within a range or not. The new photograph is classified as the disease with the highest grade. Ten tomato diseases are used as a case study, and the applications are trained with 40–100 segmented and normalized photographs for each disease. An accuracy between 93.4% and 96.1% is experimentally measured in this case. An additional dataset of pear disease photographs that are not segmented or normalized is also tested with an average accuracy of 95%.

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Section: Discussioncontrasting

confidence: 73%

“…A mobile application for maize diseases was discussed in [23]. Another mobile application with a user-friendly interface that performs fungal disease detection was presented in [24]. The diagnosis was assisted by meteorological historic data and a chat with the end user.…”

Section: Introductionmentioning

confidence: 99%