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%.

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.

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.

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.

Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual agriculture.Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture.Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques.Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist.With this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent agriculture.The MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination purposes.Besides,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not needed.The MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

Real-Time Crop Prediction Based on Soil Fertility and Weather Forecast Using IoT and a Machine Learning Algorithm

The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.

An Optimal Classification Model for Rice Plant Disease Detection

Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.

Secure Irrigation System for Olive Orchards Using Internet of Things

Smart irrigation system,also referred as precision irrigation system,is an attractive solution to save the limited water resources as well as to improve crop productivity and quality.In this work,by using Internet of things(IoT),we aim to design a smart irrigation system for olive groves.In such IoT system,a huge number of low-power and low-complexity devices(sensors,actuators)are interconnected.Thus,a great challenge is to satisfy the increasing demands in terms of spectral efficiency.Moreover,securing the IoT system is also a critical challenge,since several types of cybersecurity threats may pose.In this paper,we address these issues through the application of the massive multiple-input multiple-output(M-MIMO)technology.Indeed,M-MIMO is a key technology of the fifth generation(5G)networks and has the potential to improve spectral efficiency as well as the physical layer security.Specifically,by exploiting the available M-MIMO channel degrees of freedom,we propose a physical layer security scheme based on artificial noise(AN)to prevent eavesdropping.Numerical results demonstrate that our proposed scheme outperforms traditional ones in terms of spectral efficiency and secrecy rate.

SPAD Value Difference between Blueberry Cultivar ‘STAR’ by Planted Ground and Pot

In the smart farm,we can control every detail for production.Collecting every factor that affects the crop’s final yield is necessary to optimize its efficiency.The SPAD values were observed in the‘Star’cultivar blueberry(Vaccinium darrowii)three times a day and at three different plant heights.The pattern of SPAD value change was different by the planting position.Ground planted blueberry(V.darrowii)represented a stable SPAD value during the day and at the different heights.However,the SPAD value was increased by time in pot-planted blueberry(V.darrowii).Also,the SPAD value of pot-planted blueberry was lower than ground planted blueberry(V.darrowii).Even when plants were of the same cultivar and age,planting conditions affected the changing pattern of SPAD in a day.Each planting condition had merit.Therefore,proper management is needed to compensate SPAD value in pot-planted blueberry(V.darrowii).This study suggests that environmental conditions like planting factors affect the final products.Therefore,to maximize the efficiency at the smart farm,the factors that could affect the final yield should be investigated and accumulated.

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.

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.

Agri-BIGDATA:A smart pathway for crop nitrogen inputs

Big data provide a pathway to lower crop nitrogen inputs from genetic breeding to field production.Moreover,multidisciplinary efforts from plant health sensing,deep machine learning and cloud computing can integrate multi-source data to form information and knowledge.So big data analysis as a prospective optimal method,will make leaps towards addressing future issues of sustainable agriculture.

P recision L ivestock F arming: A n international review of scientific and commercial aspects

Precision Livestock Farming(PLF)is potentially one of the most powerful developments amongst a number of interesting new and upcoming technologies that have the potential to revolutionise the livestock farming industries.If properly implemented,PLF or Smart Farming could(1)improve or at least objectively document animal welfare on farms;(2)reduce greenhouse gas(GHG)emission and improve environmental performance of farms;(3)facilitate product segmentation and better marketing of livestock products;(4)reduce illegal trading of livestock products;and(5)improve the economic stability of rural areas.However,there are only a few examples of successful commercialisation of PLF technologies introduced by a small number of commercial companies which are actively involved in the PLF commercialisation process.To ensure that the potential of PLF is taken to the industry,it is recommended to:(1)establish a new service industry;(2)verify,demonstrate and publicise the benefits of PLF;(3)better coordinate the efforts of different industry and academic organisations interested in the development and implementation of PLF technologies on farms;and(4)encourage the commercial sectors to assist with professionally managed product development.

Automation and digitization of agriculture using artificial intelligence and internet of things

The growing population and effect of climate change have put a huge responsibility on the agriculture sector to increase food-grain production and productivity.In most of the countries where the expansion of cropland is merely impossible,agriculture automation has become the only option and is the need of the hour.Internet of things and Artificial intelligence have already started capitalizing across all the industries including agriculture.Advancement in these digital technologies has made revolutionary changes in agriculture by providing smart systems that can monitor,control,and visualize various farmoperations in real-time andwith comparable intelligence of human experts.The potential applications of IoT and AI in the development of smart farmmachinery,irrigation systems,weed and pest control,fertilizer application,greenhouse cultivation,storage structures,drones for plant protection,crop health monitoring,etc.are discussed in the paper.The main objective of the paper is to provide an overview of recent research in the area of digital technology-driven agriculture and identification of the most prominent applications in the field of agriculture engineering using artificial intelligence and internet of things.The research work done in the areas during the last 10 years has been reviewed from the scientific databases including PubMed,Web of Science,and Scopus.It has been observed that the digitization of agriculture using AI and IoT hasmatured fromtheir nascent conceptual stage and reached the execution phase.The technical details of artificial intelligence,IoT,and challenges related to the adoption of these digital technologies are also discussed.This will help in understanding how digital technologies can be integrated into agriculture practices and pave the way for the implementation of AI and IoT-based solutions in the farms.

Paradigm change in Indian agricultural practices using Big Data: Challenges and opportunities from field to plate

Agriculture is the backbone of the Indian Economy.However,statistics show that the rural population and arable land per person is declining.This is an ominous development for a country with a population of more than one billion,with over sixty-six percent living in rural areas.This paper aims to review current studies and research in agriculture,employing the recent practice of Big Data analysis,to address various problems in this sector.To execute this review,this article outline a framework for Big Data analytics in agriculture and present ways in which they can be applied to solve problems in the present agricultural domain.Another goal of this review is to gain insight into state-of-the-art Big Data applications in agriculture and to use a structural approach to identify challenges to be addressed in this area.This review of Big Data applications in the agricultural sector has also revealed several collection and analytics tools that may have implications for the power relationships between farmers and large corporations.