Abstract
There are different reasons like pests, weeds, and diseases which are responsible for the loss of crop production. Identification and detection of different plant diseases is a difficult task in a large crop field and it also requires an expert manpower. In this paper, the proposed method uses adaptive intensity based thresholding for automatic segmentation of powdery mildew disease which makes this method invariant to image quality and noise. After the segmentation of powdery mildew disease from leaf images, the affected area is quantified which makes this method efficient for grading the level of disease infection. The proposed method is tested on the comprehensive dataset of leaf images of cherry crops, which achieved good accuracy of 99%. The experimental results indicate that proposed method for segmentation of powdery mildew disease affected area from leaf image of cherry crops is convincing and computationally cheap.
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Sengar, N., Dutta, M.K. & Travieso, C.M. Computer vision based technique for identification and quantification of powdery mildew disease in cherry leaves. Computing 100, 1189–1201 (2018). https://doi.org/10.1007/s00607-018-0638-1
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DOI: https://doi.org/10.1007/s00607-018-0638-1