Unmanned-Aerial-Vehicle-Based Multispectral Monitoring of Nitrogen Content in Canopy Leaves of Processed Tomatoes

Hao Zhang, Li Zhang, Hongqi Wu (), Dejun Wang, Xin Ma, Yuqing Shao, Mingjun Jiang and Xinyu Chen
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Hao Zhang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Li Zhang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Hongqi Wu: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Dejun Wang: Institute of Western Agriculture, CAAS, Changji 831100, China
Xin Ma: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Yuqing Shao: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Mingjun Jiang: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
Xinyu Chen: College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2025, vol. 15, issue 3, 1-18

Abstract: Nitrogen serves as a critical nutrient influencing the yield and quality of processed tomatoes; however, traditional methods for assessing its levels are both labor-intensive and costly. This study aimed to explore an efficient monitoring approach by analyzing the relationship between leaf nitrogen content (LNC) and canopy spectral reflectance characteristics throughout the growth stages of processed tomatoes at the Laolong River Tomato Base in Changji City, Xinjiang. The experimental design incorporated nine treatments, each with three replicates. LNC data were obtained using a dedicated leaf nitrogen content analyzer, while drones were utilized to capture multispectral images for the extraction of vegetation indices. Through Pearson correlation analysis, the optimal spectral variables were identified, and the relationships between LNC and spectral variables were established using models based on backpropagation (BP), multiple linear regression (MLR), and random forests (RFs). The findings revealed that the manually measured LNC data exhibited two peak values, which occurred during the onset of flowering and fruit setting stages, displaying a bimodal pattern. Among the twelve selected vegetation indices, ten demonstrated spectral sensitivity, passing the highly significant 0.01 threshold, with the Normalized Difference Chlorophyll Index (NDCI) showing the highest correlation during the full bloom stage. The combination of the NDCI and RF model achieved a prediction accuracy exceeding 0.8 during the full bloom stage; similarly, models incorporating multiple vegetation indices, such as RF, MLR, and BP, also reached prediction accuracies exceeding 0.8. Consequently, during the seedling establishment and initial flowering stages (vegetation coverage of <60%), the RF model with multiple vegetation indices was suitable for monitoring LNC; during the full bloom stage (vegetation coverage of 60–80%), both the RF model with the NDCI and the MLR model with multiple indices proved effective; and during the fruit setting and maturation stages (vegetation coverage of >80%), the BP model was more appropriate. This research provides a scientific basis for the cultivation management of processed tomatoes and the optimization of nitrogen fertilization within precision agriculture. It advances the application of precision agriculture technologies, contributing to improved agricultural efficiency and resource utilization.

Keywords: processed tomato; leaf nitrogen content; UAV remote sensing technology; vegetation cover; vegetation index; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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