Monitoring of Bread Wheat Plants with Multi-Layer Remote Sensing Approach
PAKISTAN JOURNAL OF AGRICULTURAL SCIENCES, cilt.63, sa.1, ss.213-228, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 63 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.21162/pakjas/26.950
- Dergi Adı: PAKISTAN JOURNAL OF AGRICULTURAL SCIENCES
- Derginin Tarandığı İndeksler: Scopus, Science Citation Index Expanded (SCI-EXPANDED)
- Sayfa Sayıları: ss.213-228
- Akdeniz Üniversitesi Adresli: Evet
Özet
Remote sensing techniques are increasingly utilized in agricultural research and production systems for the effective monitoring of crop growth and health. This two-year study aimed to assess the relationships between spectral reflectance characteristics, vegetation indices, and plant height estimates derived from multi-platform remote sensing approaches under split nitrogen fertilization regimes applied at different growth stages of wheat. The experiment was conducted using a randomized complete block design, with data collected during the tillering, stem elongation, booting, heading, and flowering stages. Ground-based hyperspectral measurements and unmanned aerial vehicle (UAV)-derived imagery were acquired at each developmental stage. Vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and Simple Ratio Index (VI), were calculated from both ground-based and UAV data. Plant height estimates were generated using a normalized Digital Surface Model (nDSM) derived from Digital Surface Model (DSM) and Digital Terrain Model (DTM) data. Statistical analyses indicated that both wheat growth stages and nitrogen application rates had significant effects on spectral responses in the blue (p < 0.05), green, red, and near-infrared (p <0.001) regions. The lowest energy absorption across all spectral bands was observed in the control (N0) treatment, whereas the highest absorption was recorded in the N3 treatment, particularly within the green, red, and NIR regions. Vegetation index values exhibited highly significant differences (p <0.001) among nitrogen treatments and developmental stages. Moreover, strong correlations were identified between UAV-derived plant height estimates and ground-measured growth parameters, confirming the reliability of UAV-based observations. Overall, the results demonstrate that multi-layer remote sensing approach provides a robust and efficient approach for monitoring the physiological and phenological responses of wheat to varying nitrogen management strategies, with particular effectiveness in capturing the dynamic growth patterns throughout the crop’s phenological cycle.