- Chinese Core Journal
- Chinese Science Citation Database (CSCD) Source journal
- Journal of Citation Report of Chinese S&T Journals (Core Edition)
| Citation: |
LI Hongzhen, ZHAO Kaixuan, HE Yongkang. Development of methane detection system for dairy farms based on remote sensing[J]. Journal of South China Agricultural University, 2024, 45(5): 772-781. DOI: 10.7671/j.issn.1001-411X.202404040
|
To address the limitations of fixed-point gas sensors in methane detection at dairy farms, such as limited detection points and incomplete detection, this study developed a methane remote sensing detection system using unmanned aerial vehicle (UAV) sensing technology. The aim was to achieve rapid and extensive detection of methane distribution in dairy farms.
Initially, a methane remote sensing detection sensor was designed based on the principle of spectral absorption, and its detection accuracy was verified through experiments. Subsequently, field tests were conducted at the Shengsheng Dairy Farm in Mengjin County, Luoyang City, Henan Province, to create a methane concentration distribution map within the dairy farm. The relationship between methane concentration levels in active areas of cows and the number of cows present in the area was also examined.
Analysis of the experimental results showed that the average unit error of the designed methane remote sensing detection module was less than 3.27 mg/m3. A significant positive correlation was found between the methane concentration in a certain area and the number of cows active in that area, with a Pearson correlation coefficient of 0.934.
The designed methane remote sensing detection system exhibits high accuracy in practical applications, and is capable of meeting the demand for detection of methane distribution in dairy farms.
| [1] |
BENNETZEN E H, SMITH P, PORTER J R. Agricultural production and greenhouse gas emissions from world regions: The major trends over 40 years[J]. Global Environmental Change, 2016, 37: 43-55. doi: 10.1016/j.gloenvcha.2015.12.004
|
| [2] |
STAVI I, LAL R. Agriculture and greenhouse gases, a common tragedy. A review[J]. Agronomy for Sustainable Development, 2013, 33(2): 275-289. doi: 10.1007/s13593-012-0110-0
|
| [3] |
LIN J, KHANNA N, LIU X, et al. China’s non-CO2 greenhouse gas emissions: Future trajectories and mitigation options and potential[J]. Scientific Reports, 2019, 9: 16095. doi: 10.1038/s41598-019-52653-0
|
| [4] |
GAMBHIR A, NAPP T, HAWKES A, et al. The contribution of non-CO2 greenhouse gas mitigation to achieving long-term temperature goals[J]. Energies, 2017, 10(5): 602. doi: 10.3390/en10050602
|
| [5] |
FRANK S, BEACH R, HAVLÍK P, et al. Structural change as a key component for agricultural non-CO2 mitigation efforts[J]. Nature Communications, 2018, 9: 1060. doi: 10.1038/s41467-018-03489-1
|
| [6] |
BOGAERTS M, CIRHIGIRI L, ROBINSON I, et al. Climate change mitigation through intensified pasture management: Estimating greenhouse gas emissions on cattle farms in the Brazilian Amazon[J]. Journal of Cleaner Production, 2017, 162: 1539-1550. doi: 10.1016/j.jclepro.2017.06.130
|
| [7] |
CHEN M, CUI Y, JIANG S, et al. Toward carbon neutrality before 2060: Trajectory and technical mitigation potential of non-CO2 greenhouse gas emissions from Chinese agriculture[J]. Journal of Cleaner Production, 2022, 368: 133186. doi: 10.1016/j.jclepro.2022.133186
|
| [8] |
BAČĖNINAITĖ D, DŽERMEIKAITĖ K, ANTANAITIS R. Global warming and dairy cattle: How to control and reduce methane emission[J]. Animals, 2022, 12(19): 2687. doi: 10.3390/ani12192687
|
| [9] |
BLACK J L, DAVISON T M, BOX I. Methane emissions from ruminants in Australia: Mitigation potential and applicability of mitigation strategies[J]. Animals, 2021, 11(4): 951. doi: 10.3390/ani11040951
|
| [10] |
GIAMOURI E, ZISIS F, MITSIOPOULOU C, et al. Sustainable strategies for greenhouse gas emission reduction in small ruminants farming[J]. Sustainability, 2023, 15(5): 4118. doi: 10.3390/su15054118
|
| [11] |
VAGHAR SEYEDIN S M, ZEIDI A, CHAMANEHPOUR E, et al. Methane emission: Strategies to reduce global warming in relation to animal husbandry units with emphasis on ruminants[J]. Sustainability, 2022, 14(24): 16897. doi: 10.3390/su142416897
|
| [12] |
BURGUÉS J, MARCO S. Environmental chemical sensing using small drones: A review[J]. Science of the Total Environment, 2020, 748: 141172. doi: 10.1016/j.scitotenv.2020.141172
|
| [13] |
LI C, HAN W, PENG M, et al. An unmanned aerial vehicle-based gas sampling system for analyzing CO2 and atmospheric particulate matter in laboratory[J]. Sensors, 2020, 20(4): 1051. doi: 10.3390/s20041051
|
| [14] |
ZHANG T, ZHANG W, YANG R, et al. CO2 capture and storage monitoring based on remote sensing techniques: A review[J]. Journal of Cleaner Production, 2021, 281: 124409. doi: 10.1016/j.jclepro.2020.124409
|
| [15] |
GIORDAN D, HAYAKAWA Y, NEX F, et al. Review article: The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management[J]. Natural Hazards and Earth System Sciences, 2018, 18(4): 1079-1096. doi: 10.5194/nhess-18-1079-2018
|
| [16] |
ASADZADEH S, DE OLIVEIRA W, DE SOUZA C. UAV-based remote sensing for the petroleum industry and environmental monitoring: State-of-the-art and perspectives[J]. Journal of Petroleum Science and Engineering, 2022, 208.
|
| [17] |
GÓMEZ C, GREEN D R. Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping[J]. Arabian Journal of Geosciences, 2017, 10(9): 202. doi: 10.1007/s12517-017-2989-x
|
| [18] |
ZHOU F, GU J, CHEN W, et al. Measurement of SO2 and NO2 in ship plumes using rotary unmanned aerial system[J]. Atmosphere, 2019, 10(11): 657. doi: 10.3390/atmos10110657
|
| [19] |
SILVESTRI M, DIAZ J A, RABUFFI F, et al. MultiGAS detection from airborne platforms on Italian volcanic and geothermal areas[J]. Remote Sensing, 2023, 15(9): 2390. doi: 10.3390/rs15092390
|
| [20] |
ALLEN G, HOLLINGSWORTH P, KABBABE K, et al. The development and trial of an unmanned aerial system for the measurement of methane flux from landfill and greenhouse gas emission hotspots[J]. Waste Management, 2019, 87: 883-892. doi: 10.1016/j.wasman.2017.12.024
|
| [21] |
蒲施桦, 李厅厅, 王浩, 等. 畜禽养殖污染气体监测技术综述[J]. 农业环境科学学报, 2019, 38(11): 2439-2448. doi: 10.11654/jaes.2019-0623
|
| [22] |
ZENG L, HE M, YU H, et al. An H2S sensor based on electrochemistry for chicken coops[J]. Sensors, 2016, 16(9): 1398. doi: 10.3390/s16091398
|
| [23] |
DEY A. Semiconductor metal oxide gas sensors: A review[J]. Materials Science and Engineering: B, 2018, 229: 206-217. doi: 10.1016/j.mseb.2017.12.036
|
| [24] |
LIN T, SHAH S B, WANG-LI L, et al. Development of MOS sensor-based NH3 monitor for use in poultry houses[J]. Computers and Electronics in Agriculture, 2016, 127: 708-715. doi: 10.1016/j.compag.2016.07.033
|
| [25] |
介邓飞, 泮进明, 应义斌. 规模化畜禽养殖污染气体现场检测方法与仪器研究进展[J]. 农业工程学报, 2015, 31(1): 236-246. doi: 10.3969/j.issn.1002-6819.2015.01.032
|
| [26] |
BARRANCOS J, BRIZ S, NOLASCO D, et al. A new method for estimating greenhouse gases and ammonia emissions from livestock buildings[J]. Atmospheric Environment, 2013, 74: 10-17. doi: 10.1016/j.atmosenv.2013.03.021
|
| [27] |
李鑫安. 基于TDLAS的畜禽舍内氨气浓度检测系统研究[D]. 武汉: 华中农业大学, 2021.
|
| [28] |
YOON S U, CHOI S M, LEE J H. A study on the development of livestock odor (ammonia) monitoring system using ICT (information and communication technology)[J]. Agriculture, 2021, 12(1): 46. doi: 10.3390/agriculture12010046
|
| [29] |
NEETHIRAJAN S. Transforming the adaptation physiology of farm animals through sensors[J]. Animals, 2020, 10(9): 1512. doi: 10.3390/ani10091512
|
| [30] |
MENDES L, OGINK N, EDOUARD N, et al. NDIR gas sensor for spatial monitoring of carbon dioxide concentrations in naturally ventilated livestock buildings[J]. Sensors, 2015, 15(5): 11239-11257. doi: 10.3390/s150511239
|
| 1. |
李福权，张瑞祥，赵哲，陈乐天，谢先荣. 基因编辑设计和分析辅助工具的研究进展. 科学通报. 2025(16): 2449-2467 .
| |
| 2. |
王晨雨，刘孟军，王立新，刘志国. CRISPR/Cas9技术研究进展及其在园艺植物中的应用进展. 园艺学报. 2024(07): 1439-1454 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: