Authors - Luong Vinh Quoc Danh, Truong Minh Nhan, Nguyen Tan Dat, Nguyen Vinh Thanh, Do Chi Tam, Le Tan My, Nguyen Chanh Nghiem Abstract - Accurate avocado ripeness assessment is essential for ensuring product quality and effective postharvest management, yet conventional evaluation methods remain largely destructive, time-consuming, and limited to representative samples. This paper presents a non-destructive ripeness assessment method combining microwave sensing with feedforward neural network (FNN) classification. A custom-designed open-ended coaxial probe connected to a vector network analyzer was employed to measure the complex reflection coefficient S11 of avocado samples over a frequency range of 1.1–3.1 GHz. Variations in the dielectric properties of avocado flesh during ripening produce corresponding and measurable changes in the S11 characteristics, from which magnitude, phase, and frequency features were extracted and used as inputs to the FNN classifier. The proposed system achieved an overall classification accuracy of 87% in discriminating among three ripeness stages – unripe, ripe, and overripe – thereby demonstrating its viability as a rapid, costeffective, and non-destructive alternative to conventional destructive ripeness assessment methods.
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