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<p>Various real-time sensor technologies have emerged as a&#xa0;viable alternative to traditional approaches in nitrogen&#xa0;(N) crop status management. However, real-time sensor systems possess limitations that may diminish their efficacy and performance in assessing N&#xa0;status in crops. This study developed predictive models for N&#xa0;assessment in pineapple (<i>Ananas comosus</i>) cultivated on mineral soils by integrating real-time sensing with laboratory-based analysis. Real-time data were collected at the university farm in Jasin, Melaka, using the Yara ALS N‑sensor and SPAD-502 chlorophyll meter, while laboratory-based analysis was conducted using the Flash 2000 organic elemental analyzer. The linear regression models achieved high coefficients of determination (<i>R</i><sup>2</sup> = 0.81, 0.956, and 0.927), demonstrating robust predictive capability. These findings highlight the potential of combining sensor-based and laboratory approaches to enhance site-specific N&#xa0;management for pineapple cultivation.</p>

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Precision Nitrogen Management in Pineapple Cultivation Through Synergizing Real-Time Sensing and Laboratory-Based Analysis

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Eva Jacqueline Steven,
  • Darius El Pebrian,
  • Siti Fairuz Nurr Sadikan,
  • Mohd Khairy Zahari,
  • Siti Amni Ismail,
  • Nur Maizatul Idayu Othman

摘要

Various real-time sensor technologies have emerged as a viable alternative to traditional approaches in nitrogen (N) crop status management. However, real-time sensor systems possess limitations that may diminish their efficacy and performance in assessing N status in crops. This study developed predictive models for N assessment in pineapple (Ananas comosus) cultivated on mineral soils by integrating real-time sensing with laboratory-based analysis. Real-time data were collected at the university farm in Jasin, Melaka, using the Yara ALS N‑sensor and SPAD-502 chlorophyll meter, while laboratory-based analysis was conducted using the Flash 2000 organic elemental analyzer. The linear regression models achieved high coefficients of determination (R2 = 0.81, 0.956, and 0.927), demonstrating robust predictive capability. These findings highlight the potential of combining sensor-based and laboratory approaches to enhance site-specific N management for pineapple cultivation.