Abstract: Agriculture products are being more demanding in
market today. To increase its productivity, automation to produce
these products will be very helpful. The purpose of this work is to
measure and determine the ripeness and quality of watermelon. The
textures on watermelon skin will be captured using digital camera.
These images will be filtered using image processing technique. All
these information gathered will be trained using ANN to determine
the watermelon ripeness accuracy. Initial results showed that the best
model has produced percentage accuracy of 86.51%, when measured
at 32 hidden units with a balanced percentage rate of training dataset.