The accurate estimation of the age of orange trees is a critical task in orchard management, providing valuable insights into tree growth, yield prediction, and the implementation of optimal agricultural practices. Traditional methods, such as counting growth rings, while precise, are often labor-intensive and invasive, requiring tree cutting or core sampling. These techniques are impractical for large-scale application, as they are time-consuming and may cause damage to the trees. A novel non-invasive system based on fuzzy logic, combined with linear regression analysis, has been developed to estimate the age of orange trees using easily measurable parameters, including trunk diameter and height. The fuzzy inference system (FIS) offers an adaptive, intuitive, and accurate model for age estimation by incorporating these key variables. Furthermore, a multiple linear regression analysis was performed, revealing a statistically significant correlation between the predictor variables (trunk diameter and height) and tree age. The regression coefficients for diameter (p = 0.0134) and height (p = 0.0444) demonstrated strong relationships with tree age, and an R-squared value of 0.9800 indicated a high degree of model fit. These results validate the effectiveness of the proposed system, highlighting the potential of combining fuzzy logic and regression techniques to achieve precise and scalable age estimation. The model provides a valuable tool for orchard managers, agronomists, and environmental scientists, offering an efficient method for monitoring tree health, optimizing fruit production, and promoting sustainable agricultural practices.
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