Smart Farming for Sustainable Rice Production:An Insight into Application,Challenge,and Future Prospect

Rice has a huge impact on socio-economic growth,and ensuring its sustainability and optimal utilization is vital.This review provides an insight into the role of smart farming in enhancing rice productivity.The applications of smart farming in rice production including yield estimation,smart irrigation systems,monitoring disease and growth,and predicting rice quality and classifications are highlighted.The challenges of smart farming in sustainable rice production to enhance the understanding of researchers,policymakers,and stakeholders are discussed.Numerous efforts have been exerted to combat the issues in rice production in order to promote rice sector development.The effective implementation of smart farming in rice production has been facilitated by various technical advancements,particularly the integration of the Internet of Things and artificial intelligence.The future prospects of smart farming in transforming existing rice production practices are also elucidated.Through the utilization of smart farming,the rice industry can attain sustainable and resilient production systems that could mitigate environmental impact and safeguard food security.Thus,the rice industry holds a bright future in transforming current rice production practices into a new outlook in rice smart farming development.

Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms effectively.They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes.Since IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the picture.Hence,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime.Here,a conceptual framework of the proposed scheme and statistical design model has beenwell defined.The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization.The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

Indoor smart farming by inducing artificial climate for high value-added crops in optimal duration

The global population is increasing rapidly as compared to food production;approximately three times more food would be required in 2050.Climate change affects crop production by causing sudden changes in weather conditions,including rain,storms,heat waves,doughiness,and water shortages.Farming with smart technology provides a productive solution.Smart farming is a productive solution that provides a great resource of income and improves the countries’economy by exporting consumable goods and preventing food security problems.Smart agriculture provides a combination of flexibility,remote access,and automation through the use of intelligent control technologies.Many countries are working towards smart and intelligent agriculture farming that analyzes crop,soil fertility,pests and weeds,and other problems caused by mismanagement and incompetence.However,smart agricultural farming is less widely adopted in agriculture as a result of high costs and little understanding of technology.In this study,An artificial climate control chamber(ACCC)was designed for cultivating plants by controlling the optimal parameters,especially the light spectrum.In ACCC,influential plant factors such as light,moisture,humidity,and fertilizer concentration have been controlled intelligently.Light spectrum was controlled by time periods in the previous system,while in the system proposed in this study,the light was controlled by image processing.In an artificial control chamber,the plant growth stages have been determined through image processing techniques.Datasets of image images have been used to organize specific intensities of the light spectrum.This intelligent system provides aid in the speed breeding procedure through variant spectrums of light and fertilizers combinations.In the research study,the yield and quality of intelligent farming are enhanced.

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

REVIEW Digitalization and Big data in smart farming-a review

Agriculture is facing increasing challenges due to several factors such as population growth and climate change.Smart Farming is enabling the use of detailed digital information to guide decisions along the agricultural value chain.New technologies and solutions have been applied to provide alternatives to assist in information gathering and processing,and thereby contribute to increased agricultural productivity.Thus,the main objective of this article is to present a bibliometric analysis regarding digitalization and Big Data applications in Smart Farming.A total of 2401 articles were found and,based on ProKnow-C methodology criteria,thirty-nine publications were selected and analysed.Furthermore,the main solutions and opportunities about the topic were recognized aiming to direct future research.

Plant functional remote sensing and smart farming applications

Plants have the distinctive 3D spatial structure that varies among organs,species and communities,and the spatial structure changes as they interact with their environments.The functions linked to fundamental biological activities such as transpiration,photosynthesis,and growth are also affected by the spatial structure and the environment.In order to promote smart farming using information and communication technology(ICT),it is necessary to measure and utilize information at the cell-organ of plants to the individual and the community levels and the environments in two or even three dimensions.Therefore,this paper introduced the outline of remote sensing of plant functioning and examples of the 3D remote sensing from relatively short distances using drones and ground Lidar.The quality control of rice in the paddy field and chlorophyll fluorescence imaging for photosynthetic diagnosis were also introduced.In addition,a field smart farm and a smart greenhouse,which heavily utilize ICT,built at Takasaki University of Health and Welfare in Gunma,Japan,were also introduced.

Evaluating system of rice intensification using a modified transplanter:A smart farming solution toward sustainability of paddy fields in Malaysia

This paper presents the study reports on evaluating a new transplanting operation by taking into accounts the interactions between soil,plant,and machine in line with the System of Rice Intensification(SRI)practices.The objective was to modify planting claw(kuku-kambing)of a paddy transplanter in compliance with SRI guidelines to determine the best planting spacing(S),seed rate(G)and planting pattern that results in a maximum number of seedling,tillers per hill,and yield.Two separate experiments were carried out in two different paddy fields,one to determine the best planting spacing(S=4 levels:s_(1)=0.16 m×0.3 m,s_(2)=0.18 m×0.3 m,s_(3)=0.21 m×0.3 m,and s_(4)=0.24 m×0.3 m)for a specific planting pattern(row mat or scattered planting pattern),and the other to determine the best combination of spacing with seed rate treatments(G=2 levels:g1=75 g/tray,and g2=240 g/tray).Main SRI management practices such as soil characteristics of the sites,planting depth,missing hill,hill population,the number of seedling per hill,and yield components were evaluated.Results of two-way analysis of variance with three replications showed that spacing,planting pattern and seed rate affected the number of one-seedling in all experiment.It was also observed that the increase in spacing resulted in more tillers and more panicle per plant,however hill population and sterility ratio increased with the decrease in spacing.While the maximum number of panicles were resulted from scattered planting at s_(4)=0.24 m×0.3 m spacing with the seed rate of g1=75 g/tray,the maximum number of one seedling were observed at s_(4)=0.16 m×0.3 m.The highest and lowest yields were obtained from 75 g seeds per tray scattered and 70 g seeds per tray scattered treatment respectively.For all treatments,the result clearly indicates an increase in yield with an increase in spacing.

Soil Clinics: Farmers Teaching Smart-Farming to Farmers

In Thailand, the site-specific nutrient management technology, known as “Tailor-made Fertilizer Technology (TFT)”, for rice, maize and sugarcane in the Northeastern region was developed between 1997-2007, using the concepts of precision agriculture together with an approach of building capacity of small farmers. TFT, also called Smart-farming, comprises four components, namely 1) soil series identification, 2) N-P-K testing by soil test kit, 3) fertilizer recommendations using decision-aids and a simplified version of a complex model and 4) farmer empowerment. The benefit of TFT at the rice field of the Huay Kamin chairman farmer group was one example, the technology has been disseminated to the 80 members with a total planting area of about 320 ha. The results revealed chemical fertilizer reduction of 69%, and rice yield increased some 10% - 20% with the improved fertilizer application method. The farmers were encouraged to establish “Soil Clinics” in their communities. In a Soil Clinic, designated and trained farmer leaders analyze soil samples for member farmers and provide TFT recommendations while providing access to fertilizer materials available for sale at competitive prices. At present, there are about 70 soil clinics in 20 provinces with the support of many government and private sectors.

Climate Change Impacts and Adaptation in Rainfed Farming Systems: A Modeling Framework for Scaling-Out Climate Smart Agriculture in Sub-Saharan Africa

Improving agricultural water productivity, under rainfed or irrigated conditions, holds significant scope for addressing climate change vulnerability. It also offers adaptation capacity needs as well as water and food security in the southern African region. In this study, evidence for climate change impacts and adaptation strategies in rainfed agricultural systems is explored through modeling predictions of crop yield, soil moisture and excess water for potential harvesting. The study specifically presents the results of climate change impacts under rainfed conditions for maize, sorghum and sunflower using soil-water-crop model simulations, integrated based on daily inputs of rainfall and evapotranspiration disaggregated from GCM scenarios. The research targets a vast farming region dominated by heavy clay soils where rainfed agriculture is a dominant practice. The potential for improving soil water productivity and improved water harvesting have been explored as ways of climate change mitigation and adaptation measures. This can be utilized to explore and design appropriate conservation agriculture and adaptation practices in similar agro-ecological environments, and create opportunities for outscaling for much wider areas. The results of this study can suggest the need for possible policy refinements towards reducing vulnerability and adaptation to climate change in rainfed farming systems.

中国大田无人农场关键技术研究与建设实践

智慧农业是现代农业的发展方向,无人农场是实现智慧农业的重要途径。为了探索和推广无人农场在现代农业中的应用,华南农业大学对大田无人农场的关键技术进行了深入研究,包括无人农场作业环境、作业对象和作业机械装备信息的数字化感知技术;土地整治、耕整、种植、播种、田间管理和收获方案的智能化决策技术;农机自动导航和农机精准作业的精准化作业技术;农作物生长、农机运维和农场经营管理的智慧化管理技术。2020年在广东增城创建全球首个无人农场,实现了五大功能,包括耕种管收生产环节全覆盖,机库田间转移作业全自动,自动避障异况停车保安全,作物生产过程实时全监控,智能决策精准作业全无人。取得了显著的经济、社会和生态效益,2021年广东增城水稻无人农场种植的优质丝苗米十九香产量达到9934.35 kg/hm^(2),比当地的平均产量高32%;2023年湖南益阳千山红镇再生稻无人农场两季产量达到17988 kg/hm^(2),说明了人不下田也能种地,也能种好地。截至2023年11月,在国内15个省启动了30个无人农场的建设,包括水稻、小麦、玉米和花生4种作物,实践结果证明了无人农场和智慧农业发展的巨大潜力,为解决“谁来种地”和“如何种地”提供了重要途径。

奶牛智慧养殖技术装备及其数字孪生系统构建

数字孪生被视为智慧农业新的发展阶段。通过建立农业生产的物理系统和数字空间,数字孪生技术实现了“连接-感知-决策-控制”一体化,可以通过更高水平的智能控制应对农业生产系统的复杂性。我国农业数字孪生系统发展相对较晚,相关研究与技术装备整体处于探索和起步阶段。本文基于奶牛养殖多维信息采集、数据分析和生产仿真模型的研究,面向饲喂管理、繁殖管理、健康管理和环境调控等关键生产场景,建立奶牛场在虚拟空间的数字化映像,构建了奶牛养殖数字孪生系统,实现了对养殖技术流程与生产装备的数据驱动、实时反馈、模拟推演和以虚控实的智能管控,并在江苏省某千头规模奶牛场进行了具体应用和初步验证。

数字化在奶业现代化发展中的应用研究

随着信息技术的发展,全球已经进入数字经济时代。奶业是农业现代化的代表性产业,加快数字化转型是抢抓新一轮科技革命和产业变革机遇的必然选择。国家出台了一系列政策措施推动奶业数字化、智能化发展,信息技术、数字技术、人工智能技术开始不断应用到奶牛养殖、生鲜乳运输,以及乳制品加工、销售、仓储、物流等全产业链的各个环节。在养殖环节,数字化可以帮助牧场降低饲料、疫病防治、人工等环节的成本,有效提高牧场生产效率,保障牧场质量安全水平,帮助牧场实现精准化管理决策,为行业发展注入新的动能。未来,在养殖成本增加、环境约束趋严、外部挑战加剧的背景下,奶业数字化将成为产业发展的必然趋势。

我国奶业发展研究的现状、热点及趋势——基于中国知网数据库(CNKI)的CiteSpace可视化分析

[目的]围绕我国奶业发展进行文献计量和可视化分析,探讨我国奶业发展研究的现状、热点和趋势,为我国奶业发展提供数据支持。[方法]以中国知网数据库(CNKI)为数据来源,检索1992—2023年我国奶业发展研究领域的期刊文献,利用可视化分析工具CiteSpace绘制奶业发展研究领域的知识图谱,结合文献计量学方法分析该领域的研究现状、热点以及主题演变。[结果]根据设置的检索条件,共纳入739篇期刊文献,年度发文量以2002年最多,达到60篇;发文数量排名前三位的作者是刘玉满(17篇)、李胜利(15篇)、刘芳(15篇),排名前三位的机构是中国社会科学院农村发展研究所(19篇)、中国农业科学院农业信息研究所(18篇)、东北农业大学经济管理学院(17篇);奶业研究领域已经形成了相对严密、成熟的合作关系网络,中国农业科学院农业信息研究所、中国农业大学动物科技学院、中国社会科学院农村发展研究所、中国农业科学院北京畜牧兽医研究所的合作密度较高,相互之间的联系较多;奶业、奶业发展等是该领域的研究热点;“奶业发展”“奶源基地”“乳品厂”“奶牛业”“产奶量”等关键词出现时间较早,而最近则出现了“低碳发展”“规制理路”“智能养殖”等关键词。[结论]奶业发展这一主题的研究热点呈动态变化,研究作者及机构之间的合作密度较高,合作网络在分布上存在区域异质性,低碳发展、智能养殖等可能是未来研究的重点方向。

奶牛智慧养殖关键技术研究与应用

奶牛养殖业是我国农业产业的重要组成部分,也是支撑我国乳制品加工企业发展的关键产业。然而,传统的粗放式养殖模式下,需要大量的人力和物力投入,导致奶牛生产效率低下,牛奶质量也无法保证。随着物联网、人工智能等新一代信息技术的快速发展,中国的奶牛养殖业正逐步从过去的粗放型养殖模式向规模化、集约化、智能化的养殖模式发展。本文通过查阅文献,结合生产实际,综述了智能环境控制、个体识别、精准饲喂、生理和行为监测等智慧养殖关键技术的研究进展及其在奶牛生产中的应用,并为本文提到的关键技术绘制了一幅智慧养殖模式图,以期为我国奶牛养殖业提高生产效益和转型升级提供理论和技术支持。

基于“耕读教育-智慧教学”的智慧农业专业教材建设改革与创新研究

该研究基于“耕读教育-智慧教学”的理念,从教材建设目标、教材内容创新、数字化教材开发、校企合作教材编写以及评价体系改革等方面,探索在教材建设中“耕读教育”和“智慧教学”相结合的新模式,将耕读教育与专业实践教育有机智慧的结合,从而提高学生对智慧农业专业理论知识的掌握程度和实际运用能力,开辟新时代耕读教育实践,智慧教学的新途径,有助于提高教材的质量和教学效果,培养出更多具备创新能力和实践能力的智慧农业专业人才。

基于LoRa的智慧农田土壤环境监测系统研究

在信息化全面到来的时代背景下,农业生产正向着规模化、智慧化、信息化的方向发展。以西南山区特色农产品生产为研究背景,针对农田管理和农作物生长监测问题展开研究,将现代信息处理技术、传感器技术与试验田环境因素相结合,设计一种基于LoRa无线通讯技术的智慧农田土壤环境监测系统。通过对试验田土壤温湿度、土壤pH值、土壤氮磷钾浓度、电导率和光照强度等土壤环境数据进行采集、存储、处理和分析,可实现数据远程共享、土壤参数预警、辅助决策等智慧服务功能。该环境检测系统为农作物在不同阶段的生长情况提供数据支撑,进而改良试验田的土壤环境。

肉牛智慧养殖技术研究进展

肉牛智慧养殖技术是肉牛养殖业由粗放型向集约型转型升级的关键技术,在提升养殖效益及管理效率上发挥越来越重要的作用。国内肉牛养殖业面临着智能化设备利用率低、养殖场管理效率低、养殖成本偏高等产业突出问题。本文从肉牛个体识别技术、智慧表型采集技术、智慧发情鉴定技术、自动化饲喂技术、疾病检测技术、以及牛舍环境监测与清洁等6个方面概述了当前肉牛智慧养殖技术的研究进展和现状,阐述了关键技术的应用和原理,并对今后肉牛智慧养殖技术的发展进行了展望,以期为我国肉牛养殖智慧化发展提供参考。

智慧农场建设的相关技术及实践应用

智慧农场是备受关注和快速发展的领域,构建可持续发展的智慧农场是研究的一个焦点。本文探讨智慧农场可持续化发展的关键技术要素以及其之间的关系,并以万通农场庄园的实践应用,探讨智慧农场可持续化关系图。研究的结果表明,智慧农场可持续化发展的基本要素在于关键技术的发展,关键技术构建了基本框架,而基本框架的不断改进又推动了智慧农场的可持续化发展,可持续发展的智慧农场能够不断适应市场发展前景,市场需求最终也推动了技术革新。

面向中小猪场的生猪体温监测智能耳标研究现状及优化对策

生猪是我国农业重要基础产业之一,生猪体温监测在疫病防治中具有重要作用。针对中小规模养猪场生猪个体信息监控信息化水平不高,以及提高其性价比为目的,结合重庆农村中小规模养猪场实际情况,比较分析传统的生猪体温监测方式及存在的主要问题,提出适用于中小养猪场体温监测的智能电子耳标设计方案。

Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

This paper presents a comprehensive review of emerging technologies for the internet of things(IoT)-based smart agriculture.We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT,such as unmanned aerial vehicles,wireless technologies,open-source IoT platforms,software defined networking(SDN),network function virtualization(NFV)technologies,cloud/fog computing,and middleware platforms.We also provide a classification of IoT applications for smart agriculture into seven categories:including smart monitoring,smart water management,agrochemicals applications,disease management,smart harvesting,supply chain management,and smart agricultural practices.Moreover,we provide a taxonomy and a side-by-side comparison of the state-ofthe-art methods toward supply chain management based on the blockchain technology for agricultural IoTs.Furthermore,we present real projects that use most of the aforementioned technologies,which demonstrate their great performance in the field of smart agriculture.Finally,we highlight open research challenges and discuss possible future research directions for agricultural IoTs.