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May 7, 2026Results in Engineering1 citationsOpen Access

Fuzzy Cognitive Map Optimisation via Genetic Algorithms for Non-Destructive Maturity Prediction of Hass Avocados Using Bioimpedance Spectroscopy

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FSFroylan Jimenez SanchezLGLiliana Vitola Garrido

Key Points

  • The aim is to develop a non-destructive method for predicting the maturity of Hass avocados using bioimpedance spectroscopy and fuzzy cognitive maps optimized by genetic algorithms.
  • Longitudinal measurements were taken from 150 Hass avocados at 50 kHz and 100 kHz frequencies over four visits.
  • Inputs such as soil nitrogen, phosphorus, potassium, and humidity were monitored, while employing a leave-one-fruit-out protocol for training.
  • The framework accuracy was assessed by comparing with five baseline models using mean absolute error as the metric.
  • FCM-GA achieved MAE = 0.053, significantly outperforming baseline methods (p < 0.01).
  • Impedance measurements indicated 92% harvest identification effectiveness when plateau conditions were met.
  • Soil nitrogen and humidity were identified as significant factors influencing impedance.

Abstract

• FCM-GA achieves MAE = 0.053, outperforming five baselines (p 0.28) • Impedance plateau (|ΔZ100| 0.28 ), K as the principal inhibitor ( S ¯ = − 0.24 ), and P as agronomically negligible ( S ¯ < 0.05 ). Convergence diagnostics confirm that the GA reaches fitness ϕ ≥ 0.90 within a mean of 2, 140 ± 680 generations at ∼ 90 s per fruit on commodity hardware. The framework costs ≈ USD 30 per BIS unit and requires no laboratory analysis of the sampled fruit. The harvest-timing indicator is presented as a preliminary observation; validation against independent destructive maturity measurements is required to confirm the impedance plateau as a reliable harvest signal.

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Cite This Study

Sanchez et al. (2026) studied this question.

synapsesocial.com/papers/69fc2c4b8b49bacb8b347e71https://doi.org/10.1016/j.rineng.2026.110854
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