From the report «Severity identification of Potato Late Blight disease from crop images captured under uncontrolled environment» of Biswas, S.; Jagyasi, B.; Singh, B.P.; Lal, M., in Humanitarian Technology Conference – (IHTC), 2014 IEEE Canada International
The late blight is the most important disease for potato, which is caused by a fungus-like organism, Phytophthora infestans in humid conditions with temperature within a specific range. Late Blight is the most devastating disease for Potato in most of the potato growing regions in the world. For optimum use of pesticide and to minimize the yield loss, the identification of disease severity is essential. Therefore, early detection of this disease attack is essential for prevention of huge economic losses.
The key contribution here is an algorithm to determine the severity of Potato Late Blight disease using image processing techniques and neural network. The proposed system takes images of a group of potato leaves with complex background as input which are captured under uncontrolled environment. The algorithm consists of mainly two steps:
(a) Fuzzy c-mean clustering to separate the disease affected area along with background
(b) to extract affected leaf area from background using neural network.
In this proposed approach decorrelation stretching is used to enhance the color differences in the input images. Then Fuzzy C-mean clustering is applied to segment the disease affected area which also include background with same color characteristics. Finally we propose to use the neural network based approach to classify the disease affected regions from the similar color textured background.
The proposed algorithm achieves an accuracy of 93% for 27 images captured in different light condition, from different distances and at different orientations along with complex background.