智慧农田害虫时空监测系统设计
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摘要
传统农业虫害测报存在时间、人力成本高,准确度难以保证等问题,难以适应现代农业发展。本文在微小农业害虫自动监控装置的基础上,结合时空分析原理和卷积神经网络识别技术,提出智慧农田害虫时空监测系统方案。文章从前端、服务器端、数据库端三方面阐述设计思路。基于设计的系统,智能识别害虫种类、数目,在此基础上进行时空分析,预测可能发生虫害的地点、趋势,最终达到农田害虫时空监测的自动化和智能化的效果。
Traditional agricultural pest survey and report has many problems, such as time cost highly, needs many people and can not ensure accuracy, so it can't suit the development of modern agriculture. Based on the automatic minor field pest device, this paper comes up with a smart field pest time-space monitor system combined with the principle of time space analysis and convolutional neural network. This paper elaborates the design ideas in three aspects:the front end, the server end, and the database end. Based on the designed system, smartly classify the pest species and get the amount of pests, time and space analyze and predict the possible area where pest disaster will occur. In the future, the system can enhance the ability of automatically and smartly monitor agricultural field pest.
Traditional agricultural pest survey and report has many problems, such as time cost highly, needs many people and can not ensure accuracy, so it can't suit the development of modern agriculture. Based on the automatic minor field pest device, this paper comes up with a smart field pest time-space monitor system combined with the principle of time space analysis and convolutional neural network. This paper elaborates the design ideas in three aspects:the front end, the server end, and the database end. Based on the designed system, smartly classify the pest species and get the amount of pests, time and space analyze and predict the possible area where pest disaster will occur. In the future, the system can enhance the ability of automatically and smartly monitor agricultural field pest.
引文
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[12] 张大鹏,张锦,郭敏泰,等.开源WebGIS软件应用开发技术和方法研究[J].测绘科学,2011(5):193-196.
[13] 郑逢斌,元沐南,王栋,等.基于开源WebGIS的遥感数据共享方法研究[J].河南大学学报(自然科学版),2016(4):438-446.
[14] 刘德营,王家亮,林相泽,等.基于卷积神经网络的白背飞虱识别方法[J].农业机械学报,2018(5):51-56.
[2] 杨大蓉.中国智慧农业产业发展策略[J].江苏农业科学,2014(4):1-2.
[3] 张云慧,程登发.突发性暴发性害虫监测预警研究进展[J].植物保护,2013(5):55-61.
[4] 张红涛,胡玉霞,刘新宇,等.农田害虫实时检测装置的设计与实现[J].河南农业科学,2007(12):63-65.
[5] 赵汗青,沈佐锐,于新文.数学形态学在昆虫分类学上的应用研究.Ⅱ.在总科阶元上的应用研究[J].昆虫学报,2003,46(2):201-208.
[6] LI Zhenyu, ZHOU Zuji, SHEN Zuorui, et al. Automated Identification of Mosquito (Diptera:Culicidae)Wingbeat Frequencies by Artificial Neural Network[J]. Journal of Sichuan Agricultural University, 2005(4):411-416.
[7] 韩瑞珍,何勇.基于计算机视觉的大田害虫远程自动识别系统[J].农业工程学报,2013(3):156-162.
[8] 王翠娟.数字梨树新型农村建设系统设计[J].北京测绘,2015(2):148-150.
[9] 何鑫.基于GeoServer和OpenLayers的WebGIS地理信息服务系统的设计与实现[J].山西交通科技,2017(4):122-126.
[10] 蒋佩伶,苗放,张峻骁.基于GeoServer和OpenLayers的WebGIS实现[J].甘肃科技,2009(22):33-34.
[11] 曹益铭,季民,李彦杰.基于WebGIS的河南省预警制作与发布系统设计[J].北京测绘,2017(6):77-80.
[12] 张大鹏,张锦,郭敏泰,等.开源WebGIS软件应用开发技术和方法研究[J].测绘科学,2011(5):193-196.
[13] 郑逢斌,元沐南,王栋,等.基于开源WebGIS的遥感数据共享方法研究[J].河南大学学报(自然科学版),2016(4):438-446.
[14] 刘德营,王家亮,林相泽,等.基于卷积神经网络的白背飞虱识别方法[J].农业机械学报,2018(5):51-56.