Smartphone Application for Diagnosing Maize Diseases in Egypt

Moawad, Nevien; Elsayed, Abdelrahman

doi:10.1109/iit50501.2020.9299067

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…In this approach, an Android application was developed, addressing more than 100 crop disease problems of cotton/wheat leaves/fruits based on fuzzy inference. 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].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%