A new automatic method for disease symptom segmentation in digital photographs of plant leaves

The segmentation of symptoms during image analysis of diseased plant leaves is an essential process for detection and classification of diseases. However, there are challenges involved in the task, many of them related to the variability of image and host/symptom characteristics and conditions. As a result of those challenges, the methods proposed in the literature so far focus on a specific problem and are usually bounded by tight constraints regarding image capture conditions. This research explores a new automatic method for segmenting disease symptoms on plant leaves that was designed to be applicable in a wide range of situations. The proposed technique employs only color channel manipulations and Boolean operations applied on binary masks, thus being simpler and more robust compared to many previously described automatic methods. Its effectiveness is demonstrated by tests performed over a large database containing images of 77 different diseases of 11 plant species. A comparison with manual segmentation is also presented, further reinforcing the advantages of the proposed approach.