Edan, Y., Dept. of Indust. Eng. and Management, Ben-Gurion Univ. of the Negev, P.O. Box 653, Beer-Sheva, 84105, Israel
Pasternak, H., Inst. of Agricultural Eng., A.R.O, Volcani Center
Shmulevich, I., Dept. of Agriculture Eng., Technion, Hebrew University
Rachmani, D., Dept. of Agriculture Eng., Technion, Hebrew University
Guedalia, D., Neural Computation Dept., Hebrew University
Grinberg, S., Inst. of Postharvest Technol., A.R.O, Volcani Center
Fallik, E., Inst. of Postharvest Technol., A.R.O, Volcani Center

A total of 370 tomatoes from two seasons were analyzed using a vision system and three mechanical properties sensors which measured firmness parameters. Multiple linear regresssion indicated classification based on color and firmness could be applied in practical sorting and improves overall classification. Hue values provided adequate information for classification. The best model (R2 = 0.96) based on 13 specific colors yielded severe misclassification of 2.2% for classification into 12 maturity classes and 79% correct classification with all samples classified ± one maturity stage according to USDA standards. A weighted color parameter provided a stable model invariant to changes in lighting conditions and yielded excellent results (R2 = 0.89). Quality classification was successfully achieved using a vision and drop impact sensor.