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The development of soybean cultivars resistant to Phakopsora pachyrhizi (also known as Asian Soybean Rust (ASR)) may be achieved through genetic breeding. In the stage of selecting the resistant genotypes, one factor that can be used is to observe visually the severity of the rust symptoms upon leaflets of sampled soybean plants. By being able to quantify the exact amounts of regions with chlorosis, necrosis, and unaffected of each sample an automatic method is developed to grade rust severity and to classify the samples accordingly. The proposed approach uses input images of soybean leaflets, in a range from healthy to different levels of ASR severity, sampled from controlled experiments in a farm in southeast Brazil. Images are then processed by channel transformation (RGB to CIELab), channel distribution analysis, and a clustering algorithm in order to segment the regions in three main areas. Those regions represent the main visual symptoms of the ASR severity, but still with ill-defined transitions between them. A post-processing superpixel algorithm is leveraged upon those results to arrive at a first rust intervals classification. A necrosis’ neighborhood analysis is proposed to compute the intermediate areas and to include them in a final rust index. Results are given upon four different experiments: 1) 200 acquired image samples taken directly from the field; 2) 67 detached leaflets at different evolved periods of infection; 3) 50 images from a previous open database, and 4) 10 images with the standards for diagrammatic scales. By grading rust severity, and separating chlorosis, and necrosis indexes this solution contributes to efforts in managing ASR in crops, and in breeding programs. |
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