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Precision AgricultureSCIE

国际简称：PRECIS AGRIC 参考译名：精准农业

中科院分区
2区
CiteScore分区
Q1
JCR分区
Q1

基本信息：: ISSN：1385-2256; E-ISSN：1573-1618; 是否OA：未开放; 是否预警：否; TOP期刊：是

出版信息：: 出版地区：NETHERLANDS; 出版商：Springer US; 出版语言：English; 出版周期：Bimonthly; 出版年份：1999; 研究方向：农林科学-农业综合

评价信息：: 影响因子：5.4; H-index：51; CiteScore指数：12.3; SJR指数：1.192; SNIP指数：1.902

发文数据：: Gold OA文章占比：33.23%; 研究类文章占比：95.58%; 年发文量：113; 自引率：0.0806...; 开源占比：0.2776; 出版撤稿占比：0; 出版国人文章占比：0.12; OA被引用占比：0.1459...

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英文简介期刊介绍 CiteScore数据中科院SCI分区 JCR分区发文数据常见问题

英文简介Precision Agriculture期刊介绍

Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming.

There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to:

Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc.

Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc.

Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc.

Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc.

Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc.

Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.

期刊简介Precision Agriculture期刊介绍

《Precision Agriculture》自1999出版以来，是一本农林科学优秀杂志。致力于发表原创科学研究结果，并为农林科学各个领域的原创研究提供一个展示平台，以促进农林科学领域的的进步。该刊鼓励先进的、清晰的阐述，从广泛的视角提供当前感兴趣的研究主题的新见解，或审查多年来某个重要领域的所有重要发展。该期刊特色在于及时报道农林科学领域的最新进展和新发现新突破等。该刊近一年未被列入预警期刊名单，目前已被权威数据库SCIE收录，得到了广泛的认可。

该期刊投稿重要关注点：

预计审稿时间： 24周，或约稿
国际TOP期刊
农林科学
AGRICULTURE, MULTIDISCIPLINARY
SCIE
中科院2区
非预警

Cite Score数据（2024年最新版）Precision Agriculture Cite Score数据

CiteScore：12.3
SJR：1.192
SNIP：1.902

学科类别	分区	排名	百分位
大类：Agricultural and Biological Sciences 小类：General Agricultural and Biological Sciences	Q1	8 / 221	96%

CiteScore 是由Elsevier(爱思唯尔)推出的另一种评价期刊影响力的文献计量指标。反映出一家期刊近期发表论文的年篇均引用次数。CiteScore以Scopus数据库中收集的引文为基础，针对的是前四年发表的论文的引文。CiteScore的意义在于，它可以为学术界提供一种新的、更全面、更客观地评价期刊影响力的方法，而不仅仅是通过影响因子(IF)这一单一指标来评价。

历年Cite Score趋势图

中科院SCI分区Precision Agriculture 中科院分区

中科院 2023年12月升级版 综述期刊：否 Top期刊：否

大类学科	分区	小类学科	分区
农林科学	2区	AGRICULTURE, MULTIDISCIPLINARY 农业综合	2区

中科院分区表 是以客观数据为基础，运用科学计量学方法对国际、国内学术期刊依据影响力进行等级划分的期刊评价标准。它为我国科研、教育机构的管理人员、科研工作者提供了一份评价国际学术期刊影响力的参考数据，得到了全国各地高校、科研机构的广泛认可。

中科院分区表 将所有期刊按照一定指标划分为1区、2区、3区、4区四个层次，类似于“优、良、及格”等。最开始，这个分区只是为了方便图书管理及图书情报领域的研究和期刊评估。之后中科院分区逐步发展成为了一种评价学术期刊质量的重要工具。

历年中科院分区趋势图

JCR分区Precision Agriculture JCR分区

2023-2024 年最新版

按JIF指标学科分区	收录子集	分区	排名	百分位
学科：AGRICULTURE, MULTIDISCIPLINARY	SCIE	Q1	8 / 89	91.6%

按JCI指标学科分区	收录子集	分区	排名	百分位
学科：AGRICULTURE, MULTIDISCIPLINARY	SCIE	Q1	4 / 89	96.07%

JCR分区的优势在于它可以帮助读者对学术文献质量进行评估。不同学科的文章引用量可能存在较大的差异，此时单独依靠影响因子(IF)评价期刊的质量可能是存在一定问题的。因此，JCR将期刊按照学科门类和影响因子分为不同的分区，这样读者可以根据自己的研究领域和需求选择合适的期刊。

历年影响因子趋势图

发文数据

2023-2024 年国家/地区发文量统计

国家/地区数量
USA76
CHINA MAINLAND40
Spain23
Brazil22
GERMANY (FED REP GER)21
Australia20
France19
England13
Italy13
Canada9

本刊中国学者近年发表论文

1、A novel sampling design considering the local heterogeneity of soil for farm field-level mapping with multiple soil properties
Author: Wang, Yongji; Qi, Qingwen; Bao, Zhengyi; Wu, Lili; Geng, Qingling; Wang, Jun

Journal: PRECISION AGRICULTURE. 2023; Vol. 24, Issue 1, pp. 1-22. DOI: 10.1007/s11119-022-09926-y
2、Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms
Author: Yu, Danyang; Zha, Yuanyuan; Sun, Zhigang; Li, Jing; Jin, Xiuliang; Zhu, Wanxue; Bian, Jiang; Ma, Li; Zeng, Yijian; Su, Zhongbo

Journal: PRECISION AGRICULTURE. 2023; Vol. 24, Issue 1, pp. 92-113. DOI: 10.1007/s11119-022-09932-0
3、Pineapple (Ananas comosus) fruit detection and localization in natural environment based on binocular stereo vision and improved YOLOv3 model
Author: Liu, Tian-Hu; Nie, Xiang-Ning; Wu, Jin-Meng; Zhang, Di; Liu, Wei; Cheng, Yi-Feng; Zheng, Yan; Qiu, Jian; Qi, Long

Journal: PRECISION AGRICULTURE. 2023; Vol. 24, Issue 1, pp. 139-160. DOI: 10.1007/s11119-022-09935-x
4、Predicting the ripening time of 'Hass' and 'Shepard' avocado fruit by hyperspectral imaging
Author: Han, Yifei; Bai, Shahla Hosseini; Trueman, Stephen J.; Khoshelham, Kourosh; Kamper, Wiebke

Journal: PRECISION AGRICULTURE. 2023; Vol. , Issue , pp. -. DOI: 10.1007/s11119-023-10022-y
5、Influences of wind vortex intensity of rotor UAV on rice morphology and yield
Author: Wei, Xu; Zhang, Zhixun; Li, Huifen; Wu, Han; Lv, Jia; Wu, Longmei; Zhou, Meizhen; Li, Jiyu

Journal: PRECISION AGRICULTURE. 2023; Vol. , Issue , pp. -. DOI: 10.1007/s11119-023-10017-9
6、UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat
Author: Fei, Shuaipeng; Hassan, Muhammad Adeel; Xiao, Yonggui; Su, Xin; Chen, Zhen; Cheng, Qian; Duan, Fuyi; Chen, Riqiang; Ma, Yuntao

Journal: PRECISION AGRICULTURE. 2023; Vol. 24, Issue 1, pp. 187-212. DOI: 10.1007/s11119-022-09938-8
7、Efficient tomato harvesting robot based on image processing and deep learning
Author: Miao, Zhonghua; Yu, Xiaoyou; Li, Nan; Zhang, Zhe; He, Chuangxin; Li, Zhao; Deng, Chunyu; Sun, Teng

Journal: PRECISION AGRICULTURE. 2023; Vol. 24, Issue 1, pp. 254-287. DOI: 10.1007/s11119-022-09944-w
8、Grape leaf disease identification with sparse data via generative adversarial networks and convolutional neural networks
Author: Chen, Yiping; Wu, Qiufeng

Journal: PRECISION AGRICULTURE. 2023; Vol. 24, Issue 1, pp. 235-253. DOI: 10.1007/s11119-022-09941-z