Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning
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 Kin...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2073-445X/14/6/1179 |
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| author | Tung-Ching Su Tsung-Chiang Wu Hsin-Ju Chen |
| author_facet | Tung-Ching Su Tsung-Chiang Wu Hsin-Ju Chen |
| author_sort | Tung-Ching Su |
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| description | 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. |
| format | Article |
| id | doaj-art-f7b5f1e7ec6341ea8acf282a38c112a4 |
| institution | Matheson Library |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-f7b5f1e7ec6341ea8acf282a38c112a42025-06-25T14:04:31ZengMDPI AGLand2073-445X2025-05-01146117910.3390/land14061179Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep LearningTung-Ching Su0Tsung-Chiang Wu1Hsin-Ju Chen2Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, TaiwanDepartment of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, TaiwanDepartment of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, TaiwanThis 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.https://www.mdpi.com/2073-445X/14/6/1179dry farmlandgradient boosting regression (GBR)hyperspectral sensorsmodified perpendicular drought index (MPDI)unmanned aerial vehicles (UAVs) |
| spellingShingle | Tung-Ching Su Tsung-Chiang Wu Hsin-Ju Chen Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning Land dry farmland gradient boosting regression (GBR) hyperspectral sensors modified perpendicular drought index (MPDI) unmanned aerial vehicles (UAVs) |
| title | Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning |
| title_full | Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning |
| title_fullStr | Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning |
| title_full_unstemmed | Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning |
| title_short | Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning |
| title_sort | improving agricultural efficiency of dry farmlands by integrating unmanned aerial vehicle monitoring data and deep learning |
| topic | dry farmland gradient boosting regression (GBR) hyperspectral sensors modified perpendicular drought index (MPDI) unmanned aerial vehicles (UAVs) |
| url | https://www.mdpi.com/2073-445X/14/6/1179 |
| work_keys_str_mv | AT tungchingsu improvingagriculturalefficiencyofdryfarmlandsbyintegratingunmannedaerialvehiclemonitoringdataanddeeplearning AT tsungchiangwu improvingagriculturalefficiencyofdryfarmlandsbyintegratingunmannedaerialvehiclemonitoringdataanddeeplearning AT hsinjuchen improvingagriculturalefficiencyofdryfarmlandsbyintegratingunmannedaerialvehiclemonitoringdataanddeeplearning |