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The rising demands of quality food products these days lead to requirement of high speed machine vision systems which can detect the defects of food products. An accuracy and cost effectiveness are two key features which a good machine vision system should include. Potato is one of the most popular food product consumed by humans and its production is rising day by day. But post-harvest losses are reported very high due to poor packing and grading facilities. So qualitative grading of potatoes in Machine vision system was performed by extracting the best thresholds. This algorithm grade the potato into defected or healthy using entropy feature defined earlier in the literature. This technique can be efficiently used in hand held potato grading machines due to its low complexity as no background removal is required in this technique before grading the potato. The threshold value set in this algorithm is analyzed using various threshold values and optimal value which can grade potato efficiently is used. The potatoes used for testing this algorithm were of chipsona-1 variety and were purchased from local Indian market. This algorithm was found to be efficient for detecting common defects found on potato skin like cracks and rots. Keywords– Potato, Entropy, Texture feature, Segmentation, Machine Vision, Cracks, Rots.

Optimum threshold value to segment potato image in hand-held potato grading machine