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Predicting tobacco leaf quality is crucial for the global industry. Multilayer perceptron neural network-genetic algorithm (MLPNN-GA) models accurately forecast quality based on chemical components and blue mold severity (BMS), outperforming traditional regression.

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Area of Science:

  • Agricultural Science
  • Plant Science
  • Data Science

Background:

  • Tobacco (Nicotiana tabacum L.) quality directly impacts cigarette flavor and industry profitability.
  • Key internal quality factors include blue mold severity (BMS), chlorophyll (Chl), nitrogen (N), sugar (S), nicotine (Nt), chloride (Cl), and potassium (K).
  • Accurate prediction of these factors can enhance farmer income, particularly in low- and middle-income countries.

Purpose of the Study:

  • To evaluate the leaf quality of four tobacco cultivars (Bergerac, Bell, Burly, Basma) under varying conditions.
  • To develop and compare predictive models for tobacco leaf quality based on chemical composition and BMS.
  • To identify the most influential factors affecting tobacco leaf quality.

Main Methods:

  • Field evaluation of BMS, Chl, N, S, Nt, Cl, K, green weight (GW), dry weight (DW), and leaf quality over two growing seasons.
  • Application of multiple linear regression, stepwise regression, ordinary least squares, partial least squares, principal component regression, and MLPNN-GA for quality prediction.
  • Comparative analysis of model accuracy using R-squared values.

Main Results:

  • Bell cultivar exhibited the highest leaf quality across both growing seasons.
  • MLPNN-GA models demonstrated superior prediction accuracy (R2 up to 1.00) compared to the best regression models (R2 up to 0.82).
  • Analysis revealed BMS as the most significant factor influencing the leaf quality of all tested cultivars.

Conclusions:

  • MLPNN-GA is a highly effective tool for predicting tobacco leaf quality based on chemical components and BMS.
  • The developed models offer a practical approach to optimizing tobacco quality and potentially increasing farmer profitability.
  • Understanding the sensitivity of leaf quality to specific factors like BMS is key for targeted agricultural management.