Design and Implementation of A Soil Quality Intelligent Analysis App for Farmers’ Needs

YUANKexin, GAIYuefeng, CHENXiuyu, XUDongyun, CHENHongyan, LIYuhuan

Journal of Agriculture ›› 2024, Vol. 14 ›› Issue (11) : 22-29.

PDF(3429 KB)
Home Journals Journal of Agriculture
Journal of Agriculture

Abbreviation (ISO4): Journal of Agriculture      Editor in chief: Shiyan QIAO

About  /  Aim & scope  /  Editorial board  /  Indexed  /  Contact  / 
PDF(3429 KB)
Journal of Agriculture ›› 2024, Vol. 14 ›› Issue (11) : 22-29. DOI: 10.11923/j.issn.2095-4050.cjas2024-0105

Design and Implementation of A Soil Quality Intelligent Analysis App for Farmers’ Needs

Author information +
History +

Abstract

Finding out the condition of soil quality is a prerequisite for ensuring national food security and developing smart agriculture. According to the characteristics of multi-point and wide distribution of farmers, it is necessary to provide regional distribution information of farmers in order to obtain soil quality information quickly and accurately. Based on the existing remote sensing inversion model of soil quality, this paper adopted ArcGIS Enterprise and other related software, and used Android mobile terminal as the platform, designed and developed soil quality (water, fertilizer, salinity and alkalinity) intelligent analysis APP for the needs of farmers. The three-layer structure of data layer, service layer and user layer was used to develop three functional modules of basic service, remote sensing inversion of soil quality and analysis and decision making, which could help farmers quickly and accurately grasp field soil quality information, and provide decision-making suggestions such as fertilization guidance and salinization treatment. The research results contributed to improving agricultural production efficiency, promoting the development of smart agriculture, which were of great significance for achieving agricultural modernization and information management.

Key words

soil quality / intelligent analysis APP / ArcGIS / smart agriculture / remote sensing inversion / agricultural production efficiency

Cite this article

Download Citations
YUAN Kexin , GAI Yuefeng , CHEN Xiuyu , et al . Design and Implementation of A Soil Quality Intelligent Analysis App for Farmers’ Needs[J]. Journal of Agriculture. 2024, 14(11): 22-29 https://doi.org/10.11923/j.issn.2095-4050.cjas2024-0105

References

[1]
魏义长, 王振营, 王同朝, 等. 土壤墒情实时监测与精准灌溉系统的设计(英文)[J]. 农业工程学报, 2013, 29(17):80-86.
[2]
JAIN K R, MUKHERJEE A, KARMAKAR P, et al. Experimental performance of soil monitoring system using IoT technique for automatic drip irrigation[J]. International journal of communication systems, 2023, 36(18).
[3]
高晶. 基于ArcGIS Engine的农业遥感监测系统的设计与应用[D]. 北京: 中国科学院大学, 2017.
[4]
ZHANG T, ZHANG Y, WANG A, et al. Intelligent analysis cloud platform for soil moisture-nutrients-salinity content based on quantitative remote sensing[J]. Atmosphere, 2023, 14(1):23.
[5]
张治, 高明秀, 朱昌达. 基于WebGIS的盐碱地水盐动态监测系统[J]. 土壤, 2019, 51(2):413-417.
[6]
李德仁. 展望5G/6G时代的地球空间信息技术[J]. 测绘学报 2019, 48(12):1475-1481.
随着通信技术的发展,5G/6G时代逐渐到来。在新的网络环境下,地球空间信息技术的发展也将催生新的发展趋势。文章首先对5G/6G时代进行了论述,分析其主要特点。然后阐述了5G/6G时代下地球空间信息技术的发展趋势(真三维实景模型的形成,地球空间信息处理的智能化和自动化,地球空间信息服务的社会化和大众化)。最后分析了新时代背景下我国自主的通导遥一体化空天信息实时智能服务系统建设的必要性;分析其发展路线(局域服务系统,区域服务系统,全球服务系统)和技术储备;对5G/6G、大数据和人工智能技术支撑下,我国地球空间信息技术的发展进行了展望。
[7]
王娇娇, 杨忠, 杨小冬, 等. 基于移动GIS的家庭农场精准施肥系统设计[J]. 中国农业信息, 2019, 31(2):62-71.
[8]
彭炜峰, 刘芳, 李光林, 等. 丘陵地区农田土壤信息监测系统的研究[J]. 农机化研究, 2021, 43(4):65-69.
[9]
张锡煜, 李思佳, 王翔, 等. 基于Sentinel-2卫星影像的黑龙江绥化市土壤全氮定量遥感反演[J]. 农业工程学报, 2023, 39(15):144-151.
[10]
CHEN H, MA Y, ZHU A, et al. Soil salinity inversion based on differentiated fusion of satellite image and ground spectra[J]. International journal of applied earth observation and geoinformation, 2021, 101:102360.
[11]
刘焕军, 张美薇, 杨昊轩, 等. 多光谱遥感结合随机森林算法反演耕作土壤有机质含量[J]. 农业工程学报, 2020, 36(10):134-140.
[12]
XU H, XU D, CHEN S, et al. Rapid determination of soil class based on visible-near infrared, mid-infrared spectroscopy and data fusion[J]. Remote sensing, 2020, 12:1512.
[13]
YU S, BU H, DONG W, et al. Construction and evaluation of prediction model of main soil nutrients based on spectral information[J]. Applied sciences, 2022, 12(13):6298.
[14]
GUO L, SUN X, FU P, et al. Mapping soil organic carbon stock by hyperspectral and time-series multispectral remote sensing images in low-relief agricultural areas[J]. Geoderma, 2021, 398:115-118.
[15]
AINIWAER M, DING J, KASIM N, et al. Regional scale soil moisture content estimation based on multi-source remote sensing parameters[J]. International journal of remote sensing, 2020, 41(9):3346-3367.
[16]
郑建乐, 张家祯, 刘微, 等. 土壤有机质含量高光谱定量反演研究[J]. 北方园艺, 2022(16):83-91.
[17]
陈红艳, 赵庚星, 李玉环, 等. 消除水分因素影响的野外原状土壤盐分高光谱建模估测[J]. 农业工程学报, 2018, 34(12):120-125.
[18]
MA Y, CHEN H, ZHAO G, et al. Spectral index fusion for salinized soil salinity inversion using sentinel-2A and UAV images in a coastal area[J]. IEEE access, 2020, 8:159595-159608.
[19]
郭宇柏. 谈企业级GIS产品ArcGIS Enterprise的应用[J]. 山西建筑, 2019, 45(21):152-153.
[20]
贾建华, 陈动. ArcGIS Server在构建企业级地理信息系统中的应用[J]. 测绘科学, 2009, 34(3):186-188.
[21]
康玲, 傅俊锋, 王怀清, 等. 基于ArcGIS Server的WebGIS应用系统开发[J]. 水电能源科学, 2007(1):26-29.
[22]
肖剑平. ArcSDE在地理空间数据存储中的应用研究[J]. 地理空间信息, 2006(6):32-35.
[23]
李德元, 姚文龙, 杨二龙, 等. 基于ArcSDE文件地理数据库存储和设计的应用研究[J]. 测绘与空间地理信息, 2016, 39(2):82-84.
[24]
孟维成. 对基于Java语言实现数据库的访问研究[J]. 软件, 2022, 43(2):169-171.
[25]
张俊伶, 张江周, 申建波, 等. 土壤健康与农业绿色发展:机遇与对策[J]. 土壤学报, 2020, 57(4):783-796.
PDF(3429 KB)

Accesses

Citation

Detail

Sections
Recommended

/