Precision Agriculture for Dragon Fruit: A Novel Approach Based on Nighttime Light Remote Sensing

The dragon fruit industry holds significant market potential and is crucial for rural economic development. However, a comprehensive understanding and precise technical approach for analyzing the spatiotemporal dynamics of dragon fruit agriculture remain lacking. This study utilizes Nighttime Light (NTL) remote sensing data and proposes the Vegetation and Impervious area Adjusted Nighttime light Dragon fruit Index (VIANDI) to extract artificial light sources associated with dragon fruit cultivation. Furthermore, a regression model is constructed to estimate production based on light intensity. By integrating geospatial analysis methods, this study reveals the spatiotemporal evolution of dragon fruit cultivation area and production in Guangxi, China, from 2017 to 2022. The results demonstrate that the proposed method effectively monitors the dynamics of dragon fruit agriculture, achieving a Kappa Coefficient of 0.72 for area extraction and a Mean Relative Error (MRE) of 8.90% for production estimation. The spatial pattern of dragon fruit production follows a northwest–southeast distribution, with its centroid located in Nanning. The spatial expansion of cultivation areas exhibited an initial growth phase followed by stabilization, whereas production distribution transitioned from expansion to aggregation, maintaining an overall upward trend. Notably, 2019 marks a key turning point in these trends. Additionally, the rapid increase in light pollution intensity within cultivation areas warrants further attention. The study results have advanced precise monitoring of dragon fruit agriculture and enhanced understanding of its spatiotemporal evolution patterns.

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Publication Details

Journal
Agriculture
Published
2025-05-07
DOI
https://doi.org/10.3390/agriculture15091014
Citations
2
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
2.60

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article

Precision Agriculture for Dragon Fruit: A Novel Approach Based on Nighttime Light Remote Sensing

Liang Zhong, Xiaosheng Liu, Tianhao Zhan
2 citations
Agriculture
Remote Sensing and Land Use
2.60
article

Precision Agriculture for Dragon Fruit: A Novel Approach Based on Nighttime Light Remote Sensing

Liang Zhong, Xiaosheng Liu, Tianhao Zhan
article en
2 citations

Abstract

The dragon fruit industry holds significant market potential and is crucial for rural economic development. However, a comprehensive understanding and precise technical approach for analyzing the spatiotemporal dynamics of dragon fruit agriculture remain lacking. This study utilizes Nighttime Light (NTL) remote sensing data and proposes the Vegetation and Impervious area Adjusted Nighttime light Dragon fruit Index (VIANDI) to extract artificial light sources associated with dragon fruit cultivation. Furthermore, a regression model is constructed to estimate production based on light intensity. By integrating geospatial analysis methods, this study reveals the spatiotemporal evolution of dragon fruit cultivation area and production in Guangxi, China, from 2017 to 2022. The results demonstrate that the proposed method effectively monitors the dynamics of dragon fruit agriculture, achieving a Kappa Coefficient of 0.72 for area extraction and a Mean Relative Error (MRE) of 8.90% for production estimation. The spatial pattern of dragon fruit production follows a northwest–southeast distribution, with its centroid located in Nanning. The spatial expansion of cultivation areas exhibited an initial growth phase followed by stabilization, whereas production distribution transitioned from expansion to aggregation, maintaining an overall upward trend. Notably, 2019 marks a key turning point in these trends. Additionally, the rapid increase in light pollution intensity within cultivation areas warrants further attention. The study results have advanced precise monitoring of dragon fruit agriculture and enhanced understanding of its spatiotemporal evolution patterns.

AgricultureVol. 15(9)
Jiangxi University of Science and Technology (CN), Xiamen University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 12%
Remote Sensing and Land Use
2.60
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