A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture
暂无分享,去创建一个
Sultan H. Almotiri | Mazliham Mohd Su’ud | Usman Illahi | Mohammed A. Al Ghamdi | Muhammad Mansoor Alam | Syeda Iqra Hassan | M. M. Su’ud | U. Illahi | M. Alam
[1] S. Husted,et al. The Biochemical Properties of Manganese in Plants , 2019, Plants.
[2] E. Pires,et al. Path Planning for ground robots in agriculture: a short review , 2020, 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).
[3] Iván Francisco García-Tejero,et al. Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies , 2018, Agricultural Water Management.
[4] Joanne C. White,et al. Remote Sensing Technologies for Enhancing Forest Inventories: A Review , 2016 .
[5] Junying He,et al. Diagnosis of Nitrogen Nutrition in Sugar Beet Based on the Characteristics of Scanned Leaf Images , 2020 .
[6] Christophe Bobda,et al. Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems , 2013, Sensors.
[7] Simin Nadjm-Tehrani,et al. Attitudes and Perceptions of IoT Security in Critical Societal Services , 2016, IEEE Access.
[8] Frank Veroustraete,et al. The Rise of the Drones in Agriculture , 2015 .
[9] J. Poesen. Soil erosion in the Anthropocene: Research needs , 2018 .
[10] Dimitrios Moshou,et al. Contribution of Remote Sensing on Crop Models: A Review , 2018, J. Imaging.
[11] S. S. Ray,et al. DETECTION OF BACTERIAL WILT DISEASE (PSEUDOMONAS SOLANCEARUM) IN BRINJAL USING HYPERSPECTRAL REMOTE SENSING , 2019, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
[12] Antonio Barrientos,et al. Monitoring Plant Status and Fertilization Strategy through Multispectral Images , 2020, Sensors.
[13] Ben Niu,et al. Design of smart agriculture based on big data and Internet of things , 2020, Int. J. Distributed Sens. Networks.
[14] Lalit Kumar,et al. Detecting Dubas bug infestations using high resolution multispectral satellite data in Oman , 2019, Comput. Electron. Agric..
[15] V. Loganathan,et al. Cloud Enabled Water Contamination Detection System , 2017, 2017 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS).
[16] A. von Tiedemann,et al. Plant pathogens, insect pests and weeds in a changing global climate: a review of approaches, challenges, research gaps, key studies and concepts , 2012, The Journal of Agricultural Science.
[17] Yiannis Ampatzidis,et al. Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence , 2019, Comput. Electron. Agric..
[18] Qiang Cao,et al. Comparison RGB Digital Camera with Active Canopy Sensor Based on UAV for Rice Nitrogen Status Monitoring , 2018, 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics).
[19] Singh Vijendra,et al. Smart Irrigation and Intrusions Detection in Agricultural Fields Using I.o.T. , 2020 .
[20] L. Caturegli,et al. Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses , 2016, PloS one.
[21] Herriyance,et al. Automatic Nutrition Detection System (ANDES) for Hydroponic Monitoring by using Micro controller and Smartphone Android , 2019, 2019 Fourth International Conference on Informatics and Computing (ICIC).
[22] V. Dharmaraj,et al. Artificial Intelligence (AI) in Agriculture , 2018, International Journal of Current Microbiology and Applied Sciences.
[23] Hassina Ait Issad,et al. A comprehensive review of Data Mining techniques in smart agriculture , 2019, Engineering in Agriculture, Environment and Food.
[24] Jean-Marie Bonnin,et al. Wireless sensor networks: a survey on recent developments and potential synergies , 2013, The Journal of Supercomputing.
[25] John Smith,et al. Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery , 2017, Remote. Sens..
[26] M. Weiss,et al. Remote sensing for agricultural applications: A meta-review , 2020 .
[27] K. Karthick,et al. Detection of pH value and Pest control for eco-friendly agriculture , 2019, 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS).
[28] Anup Vibhute,et al. Applications of Image Processing in Agriculture: A Survey , 2012 .
[29] Nello Cristianini,et al. Supervised and Unsupervised Learning , 2004 .
[30] R. P,et al. Sensor Based Waste Water Monitoring for Agriculture Using IoT , 2020, 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS).
[31] C. Rama Krishna,et al. An IoT based smart irrigation management system using Machine learning and open source technologies , 2018, Computers and Electronics in Agriculture.
[32] E. R. Davies,et al. Image processing in agriculture , 2000 .
[33] Chee Yen Leow,et al. An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges , 2018, IEEE Internet of Things Journal.
[34] N. Nankongnab,et al. Glyphosate and Paraquat in Maternal and Fetal Serums in Thai Women , 2017, Journal of agromedicine.
[35] Marc Schut,et al. Institutional Perspectives of Climate-Smart Agriculture: A Systematic Literature Review , 2018, Sustainability.
[36] Trevor Darrell,et al. Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] M. Tibbett,et al. Delimiting soil chemistry thresholds for nickel hyperaccumulator plants in Sabah (Malaysia) , 2016, Chemoecology.
[38] A. Srivastav. Chemical fertilizers and pesticides: role in groundwater contamination , 2020 .
[39] Kamran Abid,et al. A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming , 2019, IEEE Access.
[40] Guoxiang Sun,et al. Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants , 2019, Sensors.
[41] Salinda Buyamin,et al. A review on monitoring and advanced control strategies for precision irrigation , 2020, Comput. Electron. Agric..
[42] Derek Long,et al. Row and water front detection from UAV thermal-infrared imagery for furrow irrigation monitoring , 2016, 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).
[43] Khairul Salleh Mohamed Sahari,et al. Review of agriculture robotics: Practicality and feasibility , 2016, 2016 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS).
[44] P. Brownell. Sodium as an Essential Micronutrient Element for Plants and its Possible Role in Metabolism , 1980 .
[45] Andrea Gasparri,et al. A novel Observer-based Architecture for Water Management in Large-Scale (Hazelnut) Orchards , 2019, IFAC-PapersOnLine.
[46] M. Rahman,et al. Climate Change Impacts and Adaptation Strategies for Agronomic Crops , 2019, Climate Change and Agriculture.
[47] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[48] Liu Tianqi,et al. Study on Anomaly Data Detection Method for Automatic Soil Moisture Observation , 2019, 2019 International Conference on Meteorology Observations (ICMO).
[49] Franco Montalto,et al. Automated detection of unusual soil moisture probe response patterns with association rule learning , 2018, Environ. Model. Softw..
[50] Chao-Cheng Wu,et al. Quality Inspection of Phalaenopsis Hybrids Using Hyperspectral Band Selection Techniques , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.
[51] Konstantinos Kamnitsas,et al. Deep learning: Generative adversarial networks and adversarial methods , 2019 .
[52] E. Schrevens,et al. A methodological approach to assess canopy NDVI–based tomato dynamics under irrigation treatments , 2020 .
[53] D. Rodríguez,et al. Planting date and yield benefits from conservation agriculture practices across Southern Africa , 2017 .
[54] Johanna Link,et al. A Programmable Aerial Multispectral Camera System for In-Season Crop Biomass and Nitrogen Content Estimation , 2016 .
[55] N Gobalakrishnan,et al. A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases , 2020, 2020 International Conference on Communication and Signal Processing (ICCSP).
[56] Elena Simona Lohan,et al. Robustness, Security and Privacy in Location-Based Services for Future IoT: A Survey , 2017, IEEE Access.
[57] L. Marini,et al. Mitigating the impacts of the decline of traditional farming on mountain landscapes and biodiversity: a case study in the , 2011 .
[58] Tharek Abdul Rahman,et al. Enabling smart agriculture in Nigeria: Application of IoT and data analytics , 2017, 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON).
[59] Pute Wu,et al. The impacts of interannual climate variability and agricultural inputs on water footprint of crop production in an irrigation district of China. , 2013, The Science of the total environment.
[60] Angelo Basile,et al. LCIS DSS—An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study , 2019, Agricultural Systems.
[61] José Manuel Moreno,et al. Assessing the Crop-Water Status in Almond (Prunus dulcis Mill.) Trees via Thermal Imaging Camera Connected to Smartphone , 2018, Sensors.
[62] M. Saleem,et al. Role of Zinc in Plant Nutrition- A Review , 2013 .
[63] Weixing Cao,et al. Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery , 2019, Front. Plant Sci..
[64] Frédéric Baup,et al. Detection of Soil Moisture Variations Using GPS and GLONASS SNR Data for Elevation Angles Ranging From 2° to 70° , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[65] Ciprian-Radu Rad,et al. Smart Monitoring of Potato Crop: A Cyber-Physical System Architecture Model in the Field of Precision Agriculture , 2015 .
[66] Maria Balota,et al. Exploratory use of a UAV platform for variety selection in peanut , 2016, SPIE Commercial + Scientific Sensing and Imaging.
[67] J. D. dela Cruz,et al. Soil pH and nutrient (Nitrogen, Phosphorus and Potassium) analyzer using colorimetry , 2016, 2016 IEEE Region 10 Conference (TENCON).
[68] J. Knight,et al. Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles , 2019, Journal of Economic Entomology.
[69] Rin-ichiro Taniguchi,et al. Affordable field environmental monitoring and plant growth measurement system for smart agriculture , 2017, 2017 Eleventh International Conference on Sensing Technology (ICST).
[70] Pál Varga,et al. Security threats and issues in automation IoT , 2017, 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS).
[71] Taohidul Islam,et al. A Faster Technique on Rice Disease Detectionusing Image Processing of Affected Area in Agro-Field , 2018, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT).
[72] Thomas Bartzanas,et al. Internet of Things in agriculture, recent advances and future challenges , 2017 .
[73] Giulio Reina,et al. A Survey of Ranging and Imaging Techniques for Precision Agriculture Phenotyping , 2017, IEEE/ASME Transactions on Mechatronics.
[74] John P. Fulton,et al. An overview of current and potential applications of thermal remote sensing in precision agriculture , 2017, Comput. Electron. Agric..
[75] Ye Sun,et al. Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging , 2019, Sensors.
[76] Lu Wang,et al. Application of Non-Orthogonal Multiple Access in Wireless Sensor Networks for Smart Agriculture , 2019, IEEE Access.
[77] Kaveh Mollazade,et al. LightScatter : A comprehensive software package for non-destructive monitoring of horti-food products by monochromatic imaging-based spatially-resolved light scattering technology , 2017 .
[78] Weixing Cao,et al. Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice , 2018, Front. Plant Sci..
[79] Wilfried Philips,et al. Morphological Analysis for Banana Disease Detection in Close Range Hyperspectral Remote Sensing Images , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.
[80] F. Tei,et al. RELIABILITY OF NDVI DERIVED BY HIGH RESOLUTION SATELLITE AND UAV COMPARED TO IN-FIELD METHODS FOR THE EVALUATION OF EARLY CROP N STATUS AND GRAIN YIELD IN WHEAT , 2017, Experimental Agriculture.
[81] Nikesh Gondchawar,et al. IOT BASED SMART AGRICULTURE , 2021, Journal of Manufacturing Engineering.
[82] P. Jamieson,et al. Using infrared thermometry to improve irrigation scheduling on variable soils , 2020 .
[83] Dongfeng Yang,et al. Magnesium deficiency in plants: An urgent problem , 2016 .
[84] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[85] V. Pawar,et al. Machine learning regression technique for cotton leaf disease detection and controlling using IoT , 2017, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA).
[86] Hemerson Pistori,et al. Identification of Soybean Foliar Diseases Using Unmanned Aerial Vehicle Images , 2017, IEEE Geoscience and Remote Sensing Letters.
[87] Monika Jhuria,et al. Image processing for smart farming: Detection of disease and fruit grading , 2013, 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013).
[88] M. Naveed,et al. RESIDUAL IMPACT OF PESTICIDES ON ENVIRONMENT AND HEALTH OF SUGARCANE FARMERS IN PUNJAB WITH SPECIAL REFERENCE TO INTEGRATED PEST MANAGEMENT , 2019, Journal of Global Innovations in Agricultural and Social Sciences.
[89] YangQuan Chen,et al. Intelligent Bugs Mapping and Wiping (iBMW): An Affordable Robot-Driven Robot for Farmers , 2019, 2019 IEEE International Conference on Mechatronics and Automation (ICMA).
[90] Prasad S. Thenkabail,et al. Improving Water Productivity for Agriculture - Predicting and Preventing Crisis in Irrigated Water Use in a Changing Climate , 2011, 2011 IEEE Global Humanitarian Technology Conference.
[91] S. Sankaran,et al. High-Resolution Aerial Imaging Based Estimation of Crop Emergence in Potatoes , 2017, American Journal of Potato Research.
[92] Imran Ali Lakhiar,et al. Monitoring and Control Systems in Agriculture Using Intelligent Sensor Techniques: A Review of the Aeroponic System , 2018, J. Sensors.
[93] Tao Cheng,et al. Exploiting the Textural Information of UAV Multispectral Imagery to Monitor Nitrogen Status in Rice , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.
[94] Brandon Stark,et al. A detailed field study of direct correlations between ground truth crop water stress and normalized difference vegetation index (NDVI) from small unmanned aerial system (sUAS) , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).
[95] A. Good. Toward nitrogen-fixing plants , 2018, Science.
[96] J. Thornton,et al. Airborne Observations of Reactive Inorganic Chlorine and Bromine Species in the Exhaust of Coal‐Fired Power Plants , 2018, Journal of geophysical research. Atmospheres : JGR.
[97] Lin Li,et al. Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System , 2017, Sensors.
[98] M. Sibanda,et al. An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing , 2020 .
[99] Ribana Roscher,et al. Hyperspectral Plant Disease Forecasting Using Generative Adversarial Networks , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.
[100] C. Fankhauser,et al. Plant Strategies for Enhancing Access to Sunlight , 2017, Current Biology.
[101] M. Hadi,et al. THE ROLE OF CALCIUM IN PLANTS' SALT TOLERANCE , 2012 .
[102] P. Saccon,et al. Water for agriculture, irrigation management , 2017 .
[103] Igor Savin,et al. Influence of Soil Background on Spectral Reflectance of Winter Wheat Crop Canopy , 2019, Remote. Sens..
[104] Hyuk Park,et al. Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[105] David G. Long,et al. High-Resolution Soil Moisture Retrieval With ASCAT , 2016, IEEE Geoscience and Remote Sensing Letters.
[106] Ruben Van De Kerchove,et al. Detecting nutrient deficiency in spruce forests using multispectral satellite imagery , 2020, Int. J. Appl. Earth Obs. Geoinformation.
[107] W. Wan,et al. Land surface characterization using BeiDou signal-to-noise ratio observations , 2019, GPS Solutions.
[108] Linesh Raja,et al. Agriculture drones: A modern breakthrough in precision agriculture , 2017 .
[109] V. Hernandez-Santana,et al. Assessing the Water-Stress Baselines by Thermal Imaging for Irrigation Management in Almond Plantations under Water Scarcity Conditions , 2020, Water.
[110] Jean-Michel Roger,et al. Early detection of the fungal disease "apple scab" using SWIR hyperspectral imaging , 2019, 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS).
[111] Yousef E. M. Hamouda,et al. Optimally Heterogeneous Irrigation for Precision Agriculture Using Wireless Sensor Networks , 2018, Arabian Journal for Science and Engineering.
[112] D. Popescu,et al. Flooded and vegetation areas detection from UAV images using multiple descriptors , 2017, 2017 21st International Conference on System Theory, Control and Computing (ICSTCC).
[113] S. Sankaran,et al. UAS imaging-based decision tools for arid winter wheat and irrigated potato production management , 2016 .
[114] Arun Kumar Sangaiah,et al. UAV based wilt detection system via convolutional neural networks , 2020, Sustain. Comput. Informatics Syst..
[115] S. Reed,et al. Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities , 2019, Remote Sensing of Environment.
[116] Anil Kumar Singh,et al. Effect of Sulphur and Zinc on Rice Performance and Nutrient Dynamics in Plants and Soil of Indo Gangetic Plains , 2012 .
[117] Minzan Li,et al. Diagnosis of Plant Cold Damage Based on Hyperspectral Imaging and Convolutional Neural Network , 2019, IEEE Access.
[118] Vijay Kumar,et al. Towards autonomous phytopathology: Outcomes and challenges of citrus greening disease detection through close-range remote sensing , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[119] Pratik. B. Kamble,et al. Based Smart Agriculture And Soil Nutrient Detection System , 2018 .
[120] N. Iyer,et al. Nutrient Detection for Maize Plant Using Noninvasive Technique , 2018, 2018 International Conference On Advances in Communication and Computing Technology (ICACCT).
[121] T. Thiruvaran,et al. Arduino based soil moisture analyzer as an effective way for irrigation scheduling , 2018, 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS).
[122] Jyoti Singh,et al. Biosorption of metal toxicants and other water pollutants by Corn (Maize) plant: A comprehensive review , 2019 .
[123] 宇 王. Design of Wireless Soil Moisture Detection System , 2019, Open Journal of Circuits and Systems.
[124] Akira Inoue,et al. Early detection of plant faults by using machine learning , 2016, 2016 International Conference on Advanced Mechatronic Systems (ICAMechS).
[125] Sébastien Lambot,et al. A New Drone-Borne GPR for Soil Moisture Mapping , 2019, 10th International Workshop on Advanced Ground Penetrating Radar.
[126] Changying Li,et al. High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging , 2017, IEEE Instrumentation & Measurement Magazine.
[127] L. S. Pereira,et al. Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach , 2020 .
[128] E. Davidson,et al. Managing nitrogen for sustainable development , 2015, Nature.
[129] Paul Rad,et al. Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging , 2017, IEEE Cloud Computing.
[130] R. Prasad. Major Sulphur Compounds in Plants and their Role in Human Nutrition and Health - An Overview , 2014 .
[131] N. H. Ravindranath,et al. Agriculture, Forestry and Other Land Use (AFOLU) , 2014 .
[132] T. Janda,et al. The potential health risks and environmental pollution associated with the application of plant growth regulators in vegetable production in several suburban areas of Hanoi, Vietnam , 2020, Biologia Futura.
[133] David A. Widmar,et al. FARMER PERCEPTIONS OF PRECISION AGRICULTURE TECHNOLOGY BENEFITS , 2018, Journal of Agricultural and Applied Economics.
[134] M. Simonnot,et al. Combustion of nickel hyperaccumulator plants investigated by experimental and thermodynamic approaches , 2020, Chemical Engineering Research and Design.
[135] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[136] W. Khan,et al. Contribution of agriculture in economic growth: A case study of West Bengal (India) , 2020, Journal of Public Affairs.
[137] Massimo Satler,et al. Towards Smart Farming and Sustainable Agriculture with Drones , 2015, 2015 International Conference on Intelligent Environments.
[138] S. Foteinis,et al. Life cycle assessment of organic versus conventional agriculture. A case study of lettuce cultivation in Greece , 2016 .
[139] Luc Hens,et al. Chemical Pesticides and Human Health: The Urgent Need for a New Concept in Agriculture , 2016, Front. Public Health.
[140] Jiancheng Luo,et al. Fine mapping of key soil nutrient content using high resolution remote sensing image to support precision agriculture in Northwest China , 2019, 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics).
[141] Li Liu,et al. Monitoring Locusta migratoria manilensis damage using ground level hyperspectral data , 2019, 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics).
[142] Mohamad M. Awad,et al. Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques , 2019, Agriculture.
[143] C. Kremen,et al. Synthesis, part of a Special Feature on A Social-Ecological Analysis of Diversified Farming Systems: Benefits, Costs, Obstacles, and Enabling Policy Frameworks Ecosystem Services in Biologically Diversified versus Conventional Farming Systems: Benefits, Externalities, and Trade-Offs , 2012 .
[144] W. S. Lee,et al. A noninvasive, machine learning–based method for monitoring anthocyanin accumulation in plants using digital color imaging , 2019, Applications in plant sciences.
[145] Sezai Tokat,et al. A Novel Distributed CDS Algorithm for Extending Lifetime of WSNs With Solar Energy Harvester Nodes for Smart Agriculture Applications , 2020, IEEE Access.
[146] Debashis De,et al. Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas , 2018, IEEE Internet of Things Journal.
[147] William Donnelly,et al. Precision Farming: Sensor Analytics , 2015, IEEE Intelligent Systems.