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Neuro-Fuzzy System to Predict Timely Harvest in Stevia Crops

Author

Listed:
  • Shanti-Maryse Gutiérrez-Magaña

    (Division of Postgraduate Studies and Research, Technological Institute of Colima, National Technological Institute of Mexico, Colima 28976, Mexico)

  • Noel García-Díaz

    (Division of Postgraduate Studies and Research, Technological Institute of Colima, National Technological Institute of Mexico, Colima 28976, Mexico)

  • Leonel Soriano-Equigua

    (Faculty of Mechanical and Electrical Engineering, University of Colima, Colima 28400, Mexico)

  • Walter A. Mata-López

    (Faculty of Mechanical and Electrical Engineering, University of Colima, Colima 28400, Mexico)

  • Juan García-Virgen

    (Division of Postgraduate Studies and Research, Technological Institute of Colima, National Technological Institute of Mexico, Colima 28976, Mexico)

  • Jesús-Emmanuel Brizuela-Ramírez

    (Division of Postgraduate Studies and Research, Technological Institute of Colima, National Technological Institute of Mexico, Colima 28976, Mexico)

Abstract

Agriculture is essential for food production and raw materials. A key aspect of this sector is harvest, the stage at which the commercial part of the plant is separated. Timely harvesting minimizes post-harvest losses, preserves product quality, and optimizes production processes. Globally, a substantial amount of food is wasted, impacting food security and natural resources. To address this problem, an Adaptive Neuro-Fuzzy Inference System was developed to predict timely harvesting in crops. Stevia, a native plant from Brazil and Paraguay, with an annual production of 100,000 to 200,000 tons and a market of 400 million dollars, is the focus of this study. The system considers soil pH, Brix Degrees, and leaf colorimetry as inputs. The output is binary: 1 indicates timely harvest and 0 indicates no timely harvest. To assess its performance, Leave-One-Out Cross-Validation was used, obtaining an r 2 of 0.99965 and an Absolute Residual Error of 0.00064305, demonstrating its accuracy and robustness. In addition, an interactive application that allows farmers to evaluate crop status and optimize decision-making was developed.

Suggested Citation

  • Shanti-Maryse Gutiérrez-Magaña & Noel García-Díaz & Leonel Soriano-Equigua & Walter A. Mata-López & Juan García-Virgen & Jesús-Emmanuel Brizuela-Ramírez, 2025. "Neuro-Fuzzy System to Predict Timely Harvest in Stevia Crops," Agriculture, MDPI, vol. 15(8), pages 1-22, April.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:8:p:840-:d:1633788
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    References listed on IDEAS

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