Advanced Estimation of Orange Tree Age Using Fuzzy Inference and Linear Regression Models
Journal Title: International Journal of Knowledge and Innovation Studies - Year 2024, Vol 2, Issue 3
Abstract
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.
Authors and Affiliations
Muhammad Shahkar Khan
Keywords
Related Articles
- EP ID EP752397
- DOI https://doi.org/10.56578/ijkis020301
- Views 8
- Downloads 1
Utilizing Edge Cloud Computing and Deep Learning for Enhanced Risk Assessment in China’s International Trade and Investment
Amidst a transformative economic milieu in China, domestic enterprises are venturing into the global market, exposing them to intensified perils in international trade and investment. This research elucidates the interna...
Advanced Logarithmic Aggregation Operators for Enhanced Decision-Making in Uncertain Environments
This study introduces logarithmic operations tailored to intuitionistic fuzzy sets (IFSs) aimed at mitigating uncertainty in decision-making processes. Through logarithmic transformations, the membership and non-membersh...
Enhanced Detection of Soybean Leaf Diseases Using an Improved Yolov5 Model
To facilitate early intervention and control efforts, this study proposes a soybean leaf disease detection method based on an improved Yolov5 model. Initially, image preprocessing is applied to two datasets of diseased s...
Evaluating the Knowledge Economies within the European Union: A Global Knowledge Index Ranking via Entropy and CRADIS Methodologies
In this study, a novel methodology is proposed for ranking the knowledge economies of European Union (EU) countries, leveraging their positioning within the global knowledge index (GKI). The GKI, encompassing seven pivot...
A Blockchain Cross-Chain Solution Based on Relays
Blockchain has attracted widespread attention due to its unique features such as decentralization, traceability, and tamper resistance. With the rapid development of blockchain technology, an increasing number of industr...