Abstract:Accurate and rapid disease severity quantifying is critical for scientific selection of disease control measures. Smartphone-ased systems may facilitate this procedure. Based on Android and digital image processing, a smartphone-based system for cucumber leaf disease severity quantifying was designed and implemented. Leaf images can be obtained by using the smartphone back camera in field, and also can be loaded from local storage of the smartphone. Severity quantifying was done to the image in several steps. Firstly, image pre-processing and non-interested background removal were directly done to the leaf color image. Secondly, the diseased region was discriminated from the leaf region. Finally, disease severity was calculated by the ratio of disease area to leaf area as percentage, and disease grade was also calculated from the disease severity following a national standard. Numerical severity quantifying results were displayed in the interface, and the identified diseased region of the leaf image was marked in red and displayed in the interface as a synthesis image simultaneously. Two background removal algorithm were implemented in the system. One was used for simple background removal, namely super-G, which was used for background removal when the leaf region within a simple artificial background, such as a white A4 sheet. The other one was grabcut, which was a user-interactive background removal method chosen for complex natural background removal. Where the user could roughly point out background and foreground, and then the application would do the rest. For testing performance of the system, totally 50 images of downy mildew infected cucumber leaves were used. Images were acquired from greenhouses in north of Beijing. Results showed that the system could accurately quantify the downy mildew disease severity in acceptable time. Average percentage of false quantifying was 6.56%. Average running time for disease severity quantifying was 1s for disease images with simple artificial backgrounds and 11s (user interaction time was varied with each individual, thus not included) for those with complex natural backgrounds.
