In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards
Smart agriculture utilizes sensors and data analytics to automate management, ultimately enhancing crop yields and quality. Sensors play a pivotal role in realizing the potential of smart agriculture. However, many studies rely on environment-aware monitoring, which may not be comprehensive enough,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/115973/ http://psasir.upm.edu.my/id/eprint/115973/1/115973.pdf |
| _version_ | 1848866902566240256 |
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| author | Huang, Wentao Yang, Haonan Wang, Yangfeng Ding, Phebe Nawi, Nazmi Mat Zhang, Xiaoshuan |
| author_facet | Huang, Wentao Yang, Haonan Wang, Yangfeng Ding, Phebe Nawi, Nazmi Mat Zhang, Xiaoshuan |
| author_sort | Huang, Wentao |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Smart agriculture utilizes sensors and data analytics to automate management, ultimately enhancing crop yields and quality. Sensors play a pivotal role in realizing the potential of smart agriculture. However, many studies rely on environment-aware monitoring, which may not be comprehensive enough, as different crops respond uniquely to similar environments. This study presents a novel approach for assessing crop growth and health status during agricultural production. To achieve this, we designed five agricultural sensor systems, consisting of four common sensor types with varying shapes (lamellar, wrap-around, ring, and needle), alongside one modified array sensor system. Validation was conducted by simultaneously measuring multiple fruit trees. The findings revealed that the needle system and the array system were particularly effective at detecting fruit growth. Notably, only the array system demonstrated the capability to monitor fruit health status. Additionally, we introduced fruit quality parameters (QP) as a means to visualize their health status. Statistical analyses demonstrated a strong correlation (r > 0.85) and reliable prediction (R2>0.80) between both individual and tissue impedance of fruits and their chemical indicators. In particular, the impedance variation of the fruit can effectively reflect changes in its internal structure and moisture content. By analyzing the impedance data at different frequencies, the growth condition of the mango fruit can be accurately assessed. Furthermore, Granger's causality test showed that changes in fruit tissue impedance statistically led to changes in individual impedance. The sensor system developed in this study is a real-time and in-situ crop detection tool that promotes smart agriculture. |
| first_indexed | 2025-11-15T14:27:59Z |
| format | Article |
| id | upm-115973 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:27:59Z |
| publishDate | 2025 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1159732025-04-21T03:04:42Z http://psasir.upm.edu.my/id/eprint/115973/ In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards Huang, Wentao Yang, Haonan Wang, Yangfeng Ding, Phebe Nawi, Nazmi Mat Zhang, Xiaoshuan Smart agriculture utilizes sensors and data analytics to automate management, ultimately enhancing crop yields and quality. Sensors play a pivotal role in realizing the potential of smart agriculture. However, many studies rely on environment-aware monitoring, which may not be comprehensive enough, as different crops respond uniquely to similar environments. This study presents a novel approach for assessing crop growth and health status during agricultural production. To achieve this, we designed five agricultural sensor systems, consisting of four common sensor types with varying shapes (lamellar, wrap-around, ring, and needle), alongside one modified array sensor system. Validation was conducted by simultaneously measuring multiple fruit trees. The findings revealed that the needle system and the array system were particularly effective at detecting fruit growth. Notably, only the array system demonstrated the capability to monitor fruit health status. Additionally, we introduced fruit quality parameters (QP) as a means to visualize their health status. Statistical analyses demonstrated a strong correlation (r > 0.85) and reliable prediction (R2>0.80) between both individual and tissue impedance of fruits and their chemical indicators. In particular, the impedance variation of the fruit can effectively reflect changes in its internal structure and moisture content. By analyzing the impedance data at different frequencies, the growth condition of the mango fruit can be accurately assessed. Furthermore, Granger's causality test showed that changes in fruit tissue impedance statistically led to changes in individual impedance. The sensor system developed in this study is a real-time and in-situ crop detection tool that promotes smart agriculture. Elsevier 2025-02 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/115973/1/115973.pdf Huang, Wentao and Yang, Haonan and Wang, Yangfeng and Ding, Phebe and Nawi, Nazmi Mat and Zhang, Xiaoshuan (2025) In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards. Sensors and Actuators A: Physical, 382. art. no. 116134. pp. 1-13. ISSN 0924-4247; eISSN: 0924-4247 https://www.sciencedirect.com/science/article/pii/S0924424724011282 10.1016/j.sna.2024.116134 |
| spellingShingle | Huang, Wentao Yang, Haonan Wang, Yangfeng Ding, Phebe Nawi, Nazmi Mat Zhang, Xiaoshuan In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards |
| title | In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards |
| title_full | In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards |
| title_fullStr | In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards |
| title_full_unstemmed | In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards |
| title_short | In-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards |
| title_sort | in-situ precision sensing for smart agriculture using multi-electrode sensor array systems in orchards |
| url | http://psasir.upm.edu.my/id/eprint/115973/ http://psasir.upm.edu.my/id/eprint/115973/ http://psasir.upm.edu.my/id/eprint/115973/ http://psasir.upm.edu.my/id/eprint/115973/1/115973.pdf |