Neuro-Fuzzy System to Predict Timely Harvest in Stevia Crops

Shanti-Maryse Gutiérrez-Magaña, Noel García-Díaz (), Leonel Soriano-Equigua, Walter A. Mata-López, Juan García-Virgen and Jesús-Emmanuel Brizuela-Ramírez
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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

Agriculture, 2025, vol. 15, issue 8, 1-22

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.

Keywords: agriculture; timely harvest; pH; Brix Degrees; colorimetry; Stevia; Adaptive Neuro-Fuzzy Inference System (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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