Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning

Tung-Ching Su, Tsung-Chiang Wu () and Hsin-Ju Chen
Additional contact information
Tung-Ching Su: Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, Taiwan
Tsung-Chiang Wu: Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, Taiwan
Hsin-Ju Chen: Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, Taiwan

Land, 2025, vol. 14, issue 6, 1-17

Abstract: This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at various growth stages. The Modified Perpendicular Drought Index (MPDI) was calculated to quantify soil drought conditions. Simultaneously, soil samples were collected to measure the actual soil moisture content. These datasets were used to develop a Gradient Boosting Regression (GBR) model to estimate soil moisture across the entire field. The resulting AI-based model can guide decisions on the timing and scale of supplemental irrigation, ensuring water is applied only when needed during crop growth. Furthermore, MPDI values and wheat spike samples were used to construct another GBR model for yield prediction. When applying MPDI values from multispectral imagery collected at a similar stage in the following year, the model achieved a prediction accuracy of over 90%. The proposed approach offers a reliable solution for enhancing the resilience and productivity of dryland crops under climate stress and demonstrates the potential of integrating remote sensing and machine learning in precision water management.

Keywords: dry farmland; gradient boosting regression (GBR); hyperspectral sensors; modified perpendicular drought index (MPDI); unmanned aerial vehicles (UAVs) (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/6/1179/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/6/1179/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:6:p:1179-:d:1667843

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().