| Summary: | Anthracnose and blight are among the most important fungal diseases affecting walnut orchards. Therefore, evaluating germplasms sensitivity for breeding purposes is of high importance. However, visual assessment of disease severity requires extensive time and effort. In this research, a new automated technique was developed through image segmentation to assist accurate automated anthracnose and blight severity estimation. For this purpose, anthracnose and blight severity in 130 walnut genotypes was estimated by visually screening 15 randomly collected leaves per genotype. Then, digital images of the evaluated leaves were acquired and preprocessed. A color segmentation function was developed to estimate the anthracnose and blight infected area. Correlation and regression were used to compare image segmentation results with actual visual results. Results showed that actual results were highly correlated with their counterparts of image segmentation with values of r = 0.91 (R2 = 0.83) and r = 0.96 (R2 = 0.92) for individual samples and genotype averages, respectively. These results indicate that color segmentation method can be reliably used for walnut anthracnose and blight severity estimation as an automatic substitute for expert evaluation. Further research is needed to enhance the system's capability of handling background noise and carry out real-time field inspections.
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