One way of assessing the impact of pathogens or herbivores on host plants is to measure the area of leaf damaged. Common methods to estimate the area damaged include counting the number of squares with damage on a grid overlayed on the leaf surface (Simms, 1993) or visually estimating leaf area damaged by comparison with pictures of leaves with known amounts of damage (Schaffer et al., 1986; Kogan & Turnipseed, 1980). These techniques are simple to implement; however, they are not accurate. In this study we used “Compu Eye, Leaf & Symptom Area” software to make this process easier and more accurate and measure leaf area damaged caused by a phytophagous mite, Bryobia rubrioculus. The Brown mite, Bryobia rubrioculusScheuten (Acari: Tetranychidae), is an injurious pest of fruit trees in the western region of Iran. Feeding by mites induces brown spots on leaf surface. Injured leaves were scanned on a common flat bed color scanner to obtain any symptom on the leaf. A standard green shape with a known area of 400 mm 2 was used to evaluate the accuracy of the software. In order to evaluate leaf area damaged, leaves with symptoms of 30 adult mite injury were collected from ten cultivars of sweet cherry (KB9, KB21, KB10, KB25, Sabima, Hamedan, Zarde 90, Siahe Mashhad, Lambert, Haj Yosefi) and five cultivars of sour cherry (BT5148, BN5150, BT5124, BO5187, BT5154). The image analysis of the standard shape was repeated, for each image, using a series of measurement units, 0.1, 0.2, 0.4, 0.6, 0.8 and 1.0 mm 2 . The customized detection averages revealed 40.67, 38.78, 38.67, 37.75, 37.65, 35.89, 34.38, 32.49, 31.95, 29.15, 28.48, 20.84, 15.04, 12.33 and 7.52% symptom area averages for KB9, KB21, Hamedan, BT5148, BN5150, Zarde 90, KB10, Sabima, BT5124, KB25, Siahe Mashhad, Lambert, Haj Yosefi, BO5187 and BT5154 leaves, respectively.
