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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.

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 SanchezUniversity of Sucre LGLiliana Vitola GarridoUniversity of Sucre

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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Sanchez et al. (2026) studied this question.

synapsesocial.com/papers/69fc2c4b8b49bacb8b347e71 https://doi.org/10.1016/j.rineng.2026.110854

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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

View Full Paper

FSFroylan Jimenez SanchezUniversity of Sucre LGLiliana Vitola GarridoUniversity of Sucre

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

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Cite This Study

Sanchez et al. (2026) studied this question.

synapsesocial.com/papers/69fc2c4b8b49bacb8b347e71 https://doi.org/10.1016/j.rineng.2026.110854

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