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Journals / Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi / 2024 / Cilt: 41 Sayı: 3

Developing Leaf Area Prediction Model for Curly Lettuce Grown Under Salinity Stress and Applied with Foliar Salicylic Acid

Tuzluluk Stresi Koşullarında Yetiştirilen ve Yapraktan Salisilik Asit Uygulanan Kıvırcık Marul İçin Yaprak Alanı Tahmin Modelinin Geliştirilmesi

Mehmet Kiremit(ONDOKUZ MAYIS UNIV)0000-0002-7394-303X
Pages
178–185
Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Finally, using leaf area prediction models that consider stress conditions can enhance crop management by allowing accurate monitoring of plant health and growth in agriculture.

📌 3 Key Takeaways

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Objective & Hypothesis:Accurate and non-destructive methods for measuring leaf area are crucial for understanding the growth and physiological variations of plants under stress conditions.
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Key Findings & Data:This investigation aimed to develop and assess the effectiveness of various regression models for predicting the leaf area of curly lettuce cultivated under different irrigation water salinities (IWS: 0.30, 4.15, 8.0 dS m-1) and salicylic acid doses (SA: 0, 1, 2 mM).
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Conclusion & Impact:Finally, using leaf area prediction models that consider stress conditions can enhance crop management by allowing accurate monitoring of plant health and growth in agriculture.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Lactuca sativa · yaprak boyutları · tahribatsız yöntemler

Abstract

Accurate and non-destructive methods for measuring leaf area are crucial for understanding the growth and physiological variations of plants under stress conditions. This investigation aimed to develop and assess the effectiveness of various regression models for predicting the leaf area of curly lettuce cultivated under different irrigation water salinities (IWS: 0.30, 4.15, 8.0 dS m-1) and salicylic acid doses (SA: 0, 1, 2 mM). The coefficient of determination (R2) values for the models ranged from 0.505 to 0.968, with Root Mean Square Error (RMSE) values between 4.59 and 17.79 cm² and Mean Absolute Error (MAE) values of 3.44 to 13.05 cm². Using only leaf length (LL) and leaf width (LW) can effectively estimate the leaf area of curly lettuce plants (Model 3, R²: 0.962, RMSE: 7.58 cm², MAE: 5.34 cm²). Incorporating IWS and SA into prediction models enhanced their accuracy and reliability. The best model for estimating the leaf area of curly lettuce was found from Model 13, which integrated all four parameters—SA, IWS, LL, and LW—achieving the highest R² (0.968) and the lowest RMSE (4.59 cm²) and MAE (3.44 cm²). Finally, using leaf area prediction models that consider stress conditions can enhance crop management by allowing accurate monitoring of plant health and growth in agriculture.

Özet

Stres koşulları altında bitkilerin büyümesini ve fizyolojik değişimlerini anlamak için yaprak alanının doğru ve bitkiye zarar vermeyen yöntemlerle ölçülmesi büyük önem taşımaktadır. Bu çalışmada farklı sulama suyu tuzlulukları (IWS: 0.30, 4.15, 8.0 dS m-1) ve salisilik asit dozları (SA: 0, 1, 2 mM) altında yetiştirilen kıvırcık marulun yaprak alanını tahmin etmek için çeşitli regresyon modellerinin geliştirilmesi ve etkinliğinin değerlendirilmesini amaçlanmıştır. Modeller için R2 değerleri 0.505 ile 0.968 arasında, RMSE değerleri 4.59 ile 17.79 cm² ve MAE değerleri 3.44 ile 13.05 cm² arasında bulunmuştur. Sadece yaprak uzunluğu (LL) ve yaprak genişliği (LW) kullanılarak kıvırcık marul bitkilerinin yaprak alanı etkili bir şekilde tahmin edilebileceği anlaşılmıştır (Model 3, R²: 0.962, RMSE: 7.58 cm², MAE: 5.34 cm²). IWS ve SA' nın tahmin modellerine dahil edilmesi elde edilen regresyon eşitliklerinin doğruluk ve güvenilirliklerini artırmıştır. Kıvırcık marulun yaprak alanını tahmin etmek için en iyi model, en yüksek R² (0.968) ve en düşük RMSE (4.59 cm²) ve MAE (3.44 cm²) değerlerinin elde edildiği dört parametreyi (SA, IWS, LL ve LW) entegre eden Model 13 olduğu belirlenmiştir. Sonuç olarak, stres koşullarını dikkate alan yaprak alanı tahmin modellerinin kullanılması, tarımda bitki sağlığı ve büyümesinin doğru bir şekilde izlenmesine olanak sağlayarak ürün yönetimini iyileştirebilir.

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Cite This Article

Kiremit, M., *, M. K. (2024). Developing Leaf Area Prediction Model for Curly Lettuce Grown Under Salinity Stress and Applied with Foliar Salicylic Acid. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, Cilt: 41 Sayı: 3, 178–185. https://doi.org/10.55507/gopzfd.1537132

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