[1]

Firbank LG, Attwood S, Eory V, Gadanakis Y, Lynch JM, et al. 2018. Grand challenges in sustainable intensification and ecosystem services. Frontiers in Sustainable Food Systems 2:7

doi: 10.3389/fsufs.2018.00007
[2]

Das S, Chapman S, Christopher J, Roy Choudhury M, Menzies NW, et al. 2021. UAV-thermal imaging: a technological breakthrough for monitoring and quantifying crop abiotic stress to help sustain productivity on sodic soils – a case review on wheat. Remote Sensing Applications: Society and Environment 23:100583

doi: 10.1016/j.rsase.2021.100583
[3]

Maity A, Paul D, Lamichaney A, Sarkar A, Babbar N, et al. 2023. Climate change impacts on seed production and quality: current knowledge, implications, and mitigation strategies. Seed Science and Technology 51(1):65−96

doi: 10.15258/sst.2023.51.1.07
[4]

Wire M. 2007. Global agricultural issues take center stage at world agricultural forum's 2003 world congress. www.ots.at/presseaussendung/OTE_20030521_OTE0001/global-agricultural-issues-take-center-stage-at-world-agricultural-forums-2003-world-congress

[5]

Roy T, George KJ. 2020. Precision farming: a step towards sustainable, climate-smart agriculture. Global Climate Change: Resilient and Smart Agriculture, eds Venkatramanan V, Shah S, Prasad R. Singapore: Springer. pp. 199–220. doi: 10.1007/978-981-32-9856-9_10

[6]

Sharma V, Tripathi AK, Mittal H. 2022. Technological revolutions in smart farming: current trends, challenges & future directions. Computers and Electronics in Agriculture 201:107217

doi: 10.1016/j.compag.2022.107217
[7]

De Clercq M, Vats A, Biel A. 2018. Agriculture 4.0: The future of farming technology. Proceedings of the World Government Summit, Dubai, UAE, 2013. pp. 11−13

[8]

Li F, Mistele B, Hu Y, Chen X, Schmidhalter U. 2014. Reflectance estimation of canopy nitrogen content in winter wheat using optimized hyperspectral spectral indices and partial least squares regression. European Journal of Agronomy 52:198−209

doi: 10.1016/j.eja.2013.09.006
[9]

Bareth G, Aasen H, Bendig J, Gnyp ML, Bolten A, et al. 2015. Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: spectral comparison with portable spectroradiometer measurements. Photogrammetrie - Fernerkundung - Geoinformation Jahrgang 2015:69−79

doi: 10.1127/pfg/2015/0256
[10]

Floreano D, Wood RJ. 2015. Science, technology and the future of small autonomous drones. Nature 521:460−66

doi: 10.1038/nature14542
[11]

Dutta S, Singh AK, Mondal BP, Paul D, Patra K. 2023. Digital inclusion of the farming sector using drone technology. In Human-Robot Interaction - Perspectives and Applications, ed. Vinjamuri R. UK: IntechOpen. doi: 10.5772/intechopen.108740

[12]

Lottes P, Khanna R, Pfeifer J, Siegwart R, Stachniss C. 2017. UAV-based crop and weed classification for smart farming. 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017,US: IEEE. pp. 3024−31. doi: 10.1109/ICRA.2017.7989347

[13]

Roy Choudhury M, Christopher J, Das S, Apan A, Menzies NW, et al. 2022. Detection of calcium, magnesium, and chlorophyll variations of wheat genotypes on sodic soils using hyperspectral red edge parameters. Environmental Technology & Innovation 27:102469

doi: 10.1016/j.eti.2022.102469
[14]

Das S, Christopher J, Apan A, Roy Choudhury M, Chapman S, et al. 2020. UAV-thermal imaging: a robust technology to evaluate in-field crop water stress and yield variation of wheat genotypes. IEEE India Geoscience and Remote Sensing Symposium (InGARSS), Ahmedabad, India, 2020. Piscataway, NJ, United States: IEEE. pp. 138−41. doi: 10.1109/ingarss48198.2020.9358955

[15]

Das S, Christopher J, Roy Choudhury M, Apan A, Chapman S, et al. 2022. Evaluation of drought tolerance of wheat genotypes in rain-fed sodic soil environments using high-resolution UAV remote sensing techniques. Biosystems Engineering 217:68−82

doi: 10.1016/j.biosystemseng.2022.03.004
[16]

Das S, Christopher J, Apan A, Roy Choudhury M, Chapman S, et al. 2021. UAV-thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil. ISPRS Journal of Photogrammetry and Remote Sensing 173:221−37

doi: 10.1016/j.isprsjprs.2021.01.014
[17]

Wahabzada M, Mahlein AK, Bauckhage C, Steiner U, Oerke EC, et al. 2016. Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Scientific Reports 6(1):22482

doi: 10.1038/srep22482
[18]

Das S, Christopher J, Apan A, Roy Choudhury M, Chapman S, et al. 2021. Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learning. Agricultural and Forest Meteorology 307:108477

doi: 10.1016/j.agrformet.2021.108477
[19]

Roy Choudhury M, Das S, Christopher J, Apan A, Chapman S, et al. 2021. Improving biomass and grain yield prediction of wheat genotypes on sodic soil using integrated high-resolution multispectral, hyperspectral, 3D point cloud, and machine learning techniques. Remote Sensing 13(17):3482

doi: 10.3390/rs13173482
[20]

Roy Choudhury M, Mellor V, Das S, Christopher J, Apan A, et al. 2021. Improving estimation of in-season crop water use and health of wheat genotypes on sodic soils using spatial interpolation techniques and multicomponent metrics. Agricultural Water Management 255:107007

doi: 10.1016/j.agwat.2021.107007
[21]

Umstatter C. 2011. The evolution of virtual fences: a review. Computers and Electronics in Agriculture 75(1):10−22

doi: 10.1016/j.compag.2010.10.005
[22]

Driessen C, Heutinck LFM. 2015. Cows desiring to be milked? Milking robots and the coevolution of ethics and technology on Dutch dairy farms. Agriculture and Human Values 32:3−20

doi: 10.1007/s10460-014-9515-5
[23]

Fennimore SA. 2017. Automated weed control: new technology to solve an old problem in vegetable crops. In Conference presentation at ASA Section: Agronomic Production Systems, Tampa, FL, 2017. Paper107805

[24]

López ID, Corrales JC. 2018. A smart farming approach in automatic detection of favourable conditions for planting and crop production in the upper basin of Cauca River. In Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change, eds Angelov P, Iglesias J, Corrales J. Volume 687. pp. 223–33. doi: 10.1007/978-3-319-70187-5_17

[25]

Walter A, Finger R, Huber R, Buchmann N. 2017. Smart farming is key to developing sustainable agriculture. Proceedings of the National Academy of Sciences of the United States of America 114(24):6148−50

doi: 10.1073/pnas.1707462114
[26]

Amiri-Zarandi M, Hazrati Fard M, Yousefinaghani S, Kaviani M, Dara R. 2022. A platform approach to smart farm information processing. Agriculture 12(6):838

doi: 10.3390/agriculture12060838
[27]

Rose DC, Chilvers J. 2018. Agriculture 4.0: broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems 2:87

doi: 10.3389/fsufs.2018.00087
[28]

Balaska V, Adamidou Z, Vryzas Z, Gasteratos A. 2023. Sustainable crop protection via robotics and artificial intelligence solutions. Machines 11(8):774

doi: 10.3390/machines11080774
[29]

Alskaf K, Sparkes DL, Mooney SJ, Sjögersten S, Wilson P. 2020. The uptake of different tillage practices in England. Soil Use and Management 36(1):27−44

doi: 10.1111/sum.12542
[30]

Karunathilake EMBM, Le AT, Heo S, Chung YS, Mansoor S. 2023. The path to smart farming: innovations and opportunities in precision agriculture. Agriculture 13(8):1593

doi: 10.3390/agriculture13081593
[31]

Zhai Z, Martínez JF, Beltran V, Martínez NL. 2020. Decision support systems for agriculture 4.0: survey and challenges. Computers and Electronics in Agriculture 170:105256

doi: 10.1016/j.compag.2020.105256
[32]

Saiz-Rubio V, Rovira-Más F. 2020. From smart farming towards agriculture 5.0: a review on crop data management. Agronomy 10(2):207

doi: 10.3390/agronomy10020207
[33]

Zambon I, Cecchini M, Egidi G, Saporito MG, Colantoni A. 2019. Revolution 4.0: industry vs. agriculture in future development for SMEs. Processes 7(1):36

doi: 10.3390/pr7010036
[34]

Goel RK, Yadav CS, Vishnoi S, Rastogi R. 2021. Smart agriculture–urgent need of the day in developing countries. Sustainable Computing: Informatics and Systems 30:100512

doi: 10.1016/j.suscom.2021.100512
[35]

Maddikunta PKR, Hakak S, Alazab M, Bhattacharya S, Gadekallu TR, et al. 2021. Unmanned aerial vehicles in smart agriculture: applications, requirements, and challenges. IEEE Sensors Journal 21(16):17608−19

doi: 10.1109/JSEN.2021.3049471
[36]

Obi Reddy GP, Dwivedi BS, Ravindra Chary G. 2023. Applications of geospatial and big data technologies in smart farming. In Smart Agriculture for Developing Nations: Status, Perspectives and Challenges, ed. Pakeerathan K. Singapore: Springer. pp. 15–31. doi: 10.1007/978-981-19-8738-0_2

[37]

Hoegh-Guldberg O, Jacob D, Taylor M, Guillén Bolaños T, Bindi M, et al. 2019. The human imperative of stabilizing global climate change at 1.5 °C. Science 365(6459):eaaw6974

doi: 10.1126/science.aaw6974
[38]

Mulla S, Singh SK, Singh KK, Praveen B. 2020. Climate change and agriculture: a review of crop models. In Global Climate Change and Environmental Policy, eds Venkatramanan V, Shah S, Prasad R. Singapore: Springer. pp. 423–35. doi: 10.1007/978-981-13-9570-3_15

[39]

Piao S, Liu Q, Chen A, Janssens IA, Fu Y, et al. 2019. Plant phenology and global climate change: current progresses and challenges. Global Change Biology 25(6):1922−40

doi: 10.1111/gcb.14619
[40]

Falkland T, White I. 2020. Freshwater availability under climate change. In Climate Change and Impacts in the Pacific, ed. Kumar L. Cham: Springer. pp. 403–448. doi: 10.1007/978-3-030-32878-8_11

[41]

Van Dijk M, Gramberger M, Laborde D, Mandryk M, Shutes L, et al. 2020. Stakeholder-designed scenarios for global food security assessments. Global Food Security 24:100352

doi: 10.1016/j.gfs.2020.100352
[42]

Bilotta G, Genovese E, Citroni R, Cotroneo F, Meduri GM, et al. 2023. Integration of an innovative atmospheric forecasting simulator and remote sensing data into a geographical information system in the frame of Agriculture 4.0 concept. AgriEngineering 5(3):1280−301

doi: 10.3390/agriengineering5030081
[43]

Farid HU, Mustafa B, Khan ZM, Anjum MN, Ahmad I, et al. 2023. An overview of precision agricultural technologies for crop yield enhancement and environmental sustainability. Climate Change Impacts on Agriculture, eds Jatoi WN, Mubeen M, Hashmi MZ, Ali S, Fahad S, Mahmood K. Cham: Springer. pp. 239–57. doi: 10.1007/978-3-031-26692-8_14

[44]

Lioutas ED, Charatsari C, La Rocca G, De Rosa M. 2019. Key questions on the use of big data in farming: an activity theory approach. NJAS: Wageningen Journal of Life Sciences 90−91:100297

doi: 10.1016/j.njas.2019.04.003
[45]

Klerkx L, Rose D. 2020. Dealing with the game-changing technologies of Agriculture 4.0: how do we manage diversity and responsibility in food system transition pathways? Global Food Security 24:100347

doi: 10.1016/j.gfs.2019.100347
[46]

Hinson R, Lensink R, Mueller A. 2019. Transforming agribusiness in developing countries: SDGs and the role of FinTech. Current Opinion in Environmental Sustainability 41:1−9

doi: 10.1016/j.cosust.2019.07.002
[47]

Phillips PWB, Relf-Eckstein JA, Jobe G, Wixted B. 2019. Configuring the new digital landscape in Western Canadian agriculture. NJAS: Wageningen Journal of Life Sciences 90−91:100295

doi: 10.1016/j.njas.2019.04.001
[48]

Belaud JP, Prioux N, Vialle C, Sablayrolles C. 2019. Big data for agri-food 4.0: application to sustainability management for biproducts supply chain. Computers in Industry 111:41−50

doi: 10.1016/j.compind.2019.06.006
[49]

Lee SG, Yang A, Jeon BH, Park HD. 2019. A structure of scalable and configurable interface for sensor and actuator devices in smart farming system. International Journal of Innovative Technology and Exploring Engineering 8(7):2779−86

[50]

Van der Burg S, Bogaardt MJ, Wolfert S. 2019. Ethics of smart farming: current questions and directions for responsible innovation towards the future. NJAS-Wageningen Journal of Life Sciences 90−91:100289

doi: 10.1016/j.njas.2019.01.001
[51]

Quiroz IA, Alférez GH. 2020. Image recognition of Legacy blueberries in a Chilean smart farm through deep learning. Computers and Electronics in Agriculture 168:105044

doi: 10.1016/j.compag.2019.105044
[52]

Pylianidis C, Osinga S, Athanasiadis IN. 2021. Introducing digital twins to agriculture. Computers and Electronics in Agriculture 184:105942

doi: 10.1016/j.compag.2020.105942
[53]

Abbasi R, Martinez P, Ahmad R. 2022. The digitization of agricultural industry – a systematic literature review on agriculture 4.0. Smart Agricultural Technology 2:100042

doi: 10.1016/j.atech.2022.100042
[54]

Surbiryala J, Rong C. 2019. Cloud computing: history and overview. In IEEE Cloud Summit, Washington, DC, USA, 2019, US: IEEE. pp. 1−7. doi: 10.1109/CloudSummit47114.2019.00007

[55]

Khan WZ, Ahmed E, Hakak S, Yaqoob I, Ahmed A. 2019. Edge computing: a survey. Future Generation Computer Systems 97:219−35

doi: 10.1016/j.future.2019.02.050
[56]

Trankler HR, Kanoun O. 2001. Recent advances in sensor technology. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No. 01CH 37188), Budapest, Hungary, 2001. US: IEEE. Vol. 1. pp. 309−16. doi: 10.1109/IMTC.2001.928831

[57]

Charania I, Li X. 2020. Smart farming: agriculture's shift from a labor intensive to technology native industry. Internet of Things 9:100142

doi: 10.1016/j.iot.2019.100142
[58]

Bannerjee G, Sarkar U, Das S, Ghosh I. 2018. Artificial intelligence in agriculture: a literature survey. International Journal of Scientific Research in Computer Science Applications and Management Studies 7(3):1−6

[59]

Stone ND, Toman TW. 1989. A dynamically linked expert-database system for decision support in Texas cotton production. Computers and Electronics in Agriculture 4(2):139−48

doi: 10.1016/0168-1699(89)90031-8
[60]

Lemmon H. 1990. Comax: an expert system for cotton crop management. Computer Science in Economics and Management 3:177−85

doi: 10.1007/BF00436714
[61]

Talaviya T, Shah D, Patel N, Yagnik H, Shah M. 2020. Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture 4:58−73

doi: 10.1016/j.aiia.2020.04.002
[62]

Antonopoulos VZ, Antonopoulos AV. 2017. Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Computers and Electronics in Agriculture 132:86−96

doi: 10.1016/j.compag.2016.11.011
[63]

Barrero O, Rojas D, Gonzalez C, Perdomo S. 2016. Weed detection in rice fields using aerial images and neural networks. In 2016 XXI symposium on signal processing, images and artificial vision (STSIVA), Bucaramanga, Colombia, 2016 . US: IEEE. pp. 1−4. doi: 10.1109/STSIVA.2016.7743317

[64]

Banerjee G, Ghosh I. 2017. A radial basis function network based classifier for detection of selected tea pests. International Journal of Advanced Research in Computer Science and Software Engineering 7(5):665−69

[65]

Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. 2016. Deep neural networks-based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience 2016:3289801

doi: 10.1155/2016/3289801
[66]

Castañeda-Miranda A, Castaño VM. 2017. Smart frost control in greenhouses by neural networks models. Computers and Electronics in Agriculture 137:102−14

doi: 10.1016/j.compag.2017.03.024
[67]

Nabavi-Pelesaraei A, Abdi R, Rafiee S. 2016. Neural network modelling of energy use and greenhouse gas emissions of watermelon production systems. Journal of the Saudi Society of Agricultural Sciences 15(1):38−47

doi: 10.1016/j.jssas.2014.05.001
[68]

Pasquale F, Selwyn N. 2023. Education and the new laws of robotics. Postdigital Science and Education 5(1):206−19

doi: 10.1007/s42438-022-00325-0
[69]

Reddy NV, Vishnu AV, Reddy AVVV, Pranavadithya S, Kumar JJ. 2016. A critical review on agricultural robots. International Journal of Mechanical Engineering and Technology 7(4):183−88

[70]

Cheng C, Fu J, Su H, Ren L. 2023. Recent advancements in agriculture robots: benefits and challenges. Machines 11(1):48

doi: 10.3390/machines11010048
[71]

Panarin RN, Khvorova LA. 2022. Software development for agricultural tillage robot based on technologies of machine intelligence. In International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production, eds Jordan V, Tarasov I, Faerman V. Cham: Springer. Vol. 1526. pp. 354–67. doi: 10.1007/978-3-030-94141-3_28

[72]

Heidrich J, Gaulke M, Golling M, Alaydin BO, Barh A, et al. 2022. 324-fs pulses from a SESAM modelocked backside-cooled 2-μm VECSEL. IEEE Photonics Technology Letters 34(6):337−40

doi: 10.1109/LPT.2022.3156181
[73]

Bhimanpallewar RN, Narasingarao MR. 2020. AgriRobot: implementation and evaluation of an automatic robot for seeding and fertilizer microdosing in precision agriculture. International Journal of Agricultural Resources, Governance and Ecology 16(1):33−50

doi: 10.1504/IJARGE.2020.107064
[74]

Kumar P, Ashok G. 2021. Design and fabrication of smart seed-sowing robot. Materials Today: Proceedings 39:354−58

doi: 10.1016/j.matpr.2020.07.432
[75]

Deshmukh D, Pratihar DK, Deb AK, Ray H, Bhattacharyya N. 2021. Design and development of intelligent pesticide spraying system for agricultural robot. In Hybrid Intelligent Systems. HIS 2020, eds Abraham A, Hanne T, Castillo O, Gandhi N, Nogueira Rios T, et al. Cham: Springer. pp. 157–70. doi: 10.1007/978-3-030-73050-5_16

[76]

Bayati M, Fotouhi R. 2018. A mobile robotic platform for crop monitoring. Advances in Robotics & Automation 7(1):1000186

doi: 10.4172/2168-9695.1000186
[77]

Geng A, Hu X, Liu J, Mei Z, Zhang Z, et al. 2022. Development and testing of automatic row alignment system for corn harvesters. Applied Sciences 12(12):6221

doi: 10.3390/app12126221
[78]

Pooranam N, Vignesh T. 2021. A swarm robot for harvesting a paddy field. In Nature‐Inspired Algorithms Applications, eds Balamurugan S, Jain A, Sharma S, Goyal D, Duggal S, et al. US: Scrivener Publishing LLC. pp. 137−56. doi: 10.1002/9781119681984.ch5

[79]

Sitkowska B, Piwczyński D, Aerts J, Waśkowicz M. 2015. Changes in milking parameters with robotic milking. Archives Animal Breeding 58(1):137−43

doi: 10.5194/aab-58-137-2015
[80]

Borla N, Kuster F, Langenegger J, Ribera J, Honegger M, et al. 2021. Teat pose estimation via rgbd segmentation for automated milking. arXiv 00:2105.09843

doi: 10.48550/arXiv:2105.09843
[81]

Potgieter AB, Watson JE, Eldridge M, Laws K, George-Jaeggli B, et al. 2018. Determining crop growth dynamics in sorghum breeding trials through remote and proximal sensing technologies. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018. US: IEEE. pp. 8244−47. doi: 10.1109/IGARSS.2018.8519296

[82]

Gonzalez-De-Santos P, Fernández R, Sepúlveda D, Navas E, Armada M. 2020. Unmanned ground vehicles for smart farms. In Agronomy-Climate Change and Food Security, ed. Amanullah. UK: IntechOpen. doi: 10.5772/intechopen.90683

[83]

Srinivasan N, Prabhu P, Smruthi SS, Sivaraman NV, Gladwin SJ, et al. 2016. Design of an autonomous seed planting robot. In 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Agra, India, 2016. US: IEEE. pp. 1−4. doi: 10.1109/R10-HTC.2016.7906789

[84]

Gai J, Tang L, Steward BL. 2020. Automated crop plant detection based on the fusion of color and depth images for robotic weed control. Journal of Field Robotics 37(1):35−52

doi: 10.1002/rob.21897
[85]

Berenstein R, Edan Y. 2017. Automatic adjustable spraying device for site-specific agricultural application. IEEE Transactions on Automation Science and Engineering 15(2):641−50

doi: 10.1109/TASE.2017.2656143
[86]

Nejati M, Ahn HS, MacDonald B. 2020. Design of a sensing module for a kiwifruit flower pollinator robot. arXiv 00:2006.08045

doi: 10.48550/arXiv.2006.08045
[87]

Underwood JP, Calleija M, Taylor Z, Hung C, Nieto J, et al. 2015. Real-time target detection and steerable spray for vegetable crops. In Proceedings of the International Conference on Robotics and Automation: Robotics in Agriculture Workshop, Seattle, 2015, WA, USA: ICRA. pp. 26–30

[88]

Birrell S, Hughes J, Cai JY, Iida F. 2020. A field-tested robotic harvesting system for iceberg lettuce. Journal of Field Robotics 37(2):225−45

doi: 10.1002/rob.21888
[89]

Zhang J, Yue X, Zhang H, Xiao T. 2022. Optimal unmanned ground vehicle—unmanned aerial vehicle formation-maintenance control for air-ground cooperation. Applied Sciences 12(7):3598

doi: 10.3390/app12073598
[90]

Patel PN, Patel MA, Faldu RM, Dave YR. 2013. Quadcopter for agricultural surveillance. Advance in Electronic and Electric Engineering 3(4):427−32

[91]

Das S, Massey-Reed SR, Mahuika J, Watson J, Cordova C, et al. 2022. A high-throughput phenotyping pipeline for rapid evaluation of morphological and physiological crop traits across large fields. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022. US: IEEE. pp. 7783−86. doi: 10.1109/IGARSS46834.2022.9884530

[92]

Ragazou K, Garefalakis A, Zafeiriou E, Passas I. 2022. Agriculture 5.0: a new strategic management mode for a cut cost and an energy efficient agriculture sector. Energies 15(9):3113

doi: 10.3390/en15093113
[93]

Khan MM, Akram MT, Janke R, Qadri RWK, Al-Sadi AM, et al. 2020. Urban horticulture for food secure cities through and beyond COVID-19. Sustainability 12(22):9592

doi: 10.3390/su12229592
[94]

Oh S, Lu C. 2023. Vertical farming-smart urban agriculture for enhancing resilience and sustainability in food security. The Journal of Horticultural Science and Biotechnology 98(2):133−40

doi: 10.1080/14620316.2022.2141666
[95]

Shamshiri R, Kalantari F, Ting KC, Thorp KR, Hameed IA, et al. 2018. Advances in greenhouse automation and controlled environment agriculture: a transition to plant factories and urban agriculture. International Journal of Agricultural and Biological Engineering 11(1):1−22

doi: 10.25165/j.ijabe.20181101.3210
[96]

Hardy K, Orridge T, Heynes X, Gunasena S, Grundy S, et al. 2021. Farming the future: contemporary innovations enhancing sustainability in the agri-sector. Annual Plant Reviews Online 4(2):263−94

doi: 10.1002/9781119312994.apr0728
[97]

Ezzahoui I, Abdelouahid RA, Taji K, Marzak A. 2021. Hydroponic and aquaponic farming: comparative study based on Internet of Things IoT technologies. Procedia Computer Science 191:499−504

doi: 10.1016/j.procs.2021.07.064
[98]

Bakhtar N, Chhabria V, Chougle I, Vidhrani H, Hande R. 2018. IoT based hydroponic farm. In 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2018. US: IEEE. pp. 205−09. doi: 10.1109/ICSSIT.2018.8748447

[99]

Scopa A, Mondal BP, Sekhon BS, Banerjee K, Sharma S, et al. 2023. Variability in soil microbiological properties across various land use systems: a spatial analysis. African Journal of Agriculture Research 20:825−39

doi: 10.2139/ssrn.4540432
[100]

Wu TH, Chang CH, Lin YW, Van LD, Lin YB. 2016. Intelligent plant care hydroponic box was constructed using IoTtalk. In 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, China, 2016. US: IEEE. pp. 398−401. doi: 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.94

[101]

Yanes AR, Martinez P, Ahmad R. 2020. Towards automated aquaponics: a review on monitoring, IoT, and smart systems. Journal of Cleaner Production 263:121571

doi: 10.1016/j.jclepro.2020.121571
[102]

Vernandhes W, Salahuddin NS, Kowanda A, Sari SP. 2017. Smart aquaponic with monitoring and control system based on IoT. In 2017 Second International Conference on Informatics and Computing (ICIC), Jayapura, Indonesia, 2017. US: IEEE. pp. 1−6. doi: 10.1109/IAC.2017.8280590

[103]

Gupta P, Sharma M, Sharma S. 2023. Nanotechnology in agriculture: prospect, opportunities and challenges. Emergent Life Sciences Research 9(1):164−76

doi: 10.31783/elsr.2023.91164176
[104]

Singh G, Singh B. 2018. Nanotechnology in agriculture. International Journal of Scientific Engineering and Research 6(6):36−39

doi: 10.70729/IJSER172643
[105]

Cho EJ, Holback H, Liu KC, Abouelmagd SA, Park J, et al. 2013. Nanoparticle characterization: state of the art, challenges, and emerging technologies. Molecular Pharmaceutics 10(6):2093−110

doi: 10.1021/mp300697h
[106]

Pan K, Zhong Q. 2016. Organic nanoparticles in foods: fabrication, characterization, and utilization. Annual Review of Food Science and Technology 7:245−66

doi: 10.1146/annurev-food-041715-033215
[107]

Srivastava AK, Dev A, Karmakar S. 2018. Nanosensors and nanobiosensors in food and agriculture. Environmental Chemistry Letters 16:161−82

doi: 10.1007/s10311-017-0674-7
[108]

Johnson MS, Sajeev S, Nair RS. 2021. Role of Nanosensors in agriculture. In 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, 2021. US: IEEE. pp. 58−63. doi: 10.1109/ICCIKE51210.2021.9410709

[109]

Singh P, Singh AP. 2021. Nanomaterials in soil health management and crop production: potentials and limitations. In Handbook of Nanomaterials and Nanocomposites for Energy and Environmental Applications, eds Kharissova OV, Torres-Martínez LM, Kharisov BI. Cham: Springer. pp. 1221–45. doi: 10.1007/978-3-030-36268-3_35

[110]

Liu J, Wang X. 2021. Plant diseases and pests detection based on deep learning: a review. Plant Methods 17:22

doi: 10.1186/s13007-021-00722-9
[111]

Tsaftaris SA, Minervini M, Scharr H. 2016. Machine learning for plant phenotyping needs image processing. Trends in Plant Science 21(12):989−91

doi: 10.1016/j.tplants.2016.10.002
[112]

Balaska V, Bampis L, Katsavounis S, Gasteratos A. 2023. Generating graph-inspired descriptors by merging ground-level and satellite data for robot localization. Cybernetics and Systems 54(5):697−715

doi: 10.1080/01969722.2022.2073701
[113]

Balaska V, Bampis L, Kansizoglou I, Gasteratos A. 2021. Enhancing satellite semantic maps with ground-level imagery. Robotics and Autonomous Systems 139:103760

doi: 10.1016/j.robot.2021.103760
[114]

Paul RK, Das T, Yeasin M. 2023. Ensemble of time series and machine learning model for forecasting volatility in agricultural prices. National Academy Science Letters 46:185−88

doi: 10.1007/s40009-023-01218-x
[115]

Kiruthiga C, Dharmarajan K. 2023. Machine learning in soil borne diseases, soil data analysis & crop yielding: a review. In 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), Bengaluru, India, 2023. US: IEEE. pp. 702−06. doi: 10.1109/IITCEE57236.2023.10091016

[116]

Bandaia K, Gunasekaran M. 2022. An efficient model for predicting future price of agricultural commodities using K-Nearest neighbors algorithm compared with support vector machine algorithm. In 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2022. US: IEEE. pp. 858−61. doi: 10.1109/ICOSEC54921.2022.9952132

[117]

Du Z, Yang J, Ou C, Zhang T. 2019. Smallholder crop area mapped with a semantic segmentation deep learning method. Remote Sensing 11(7):888

doi: 10.3390/rs11070888
[118]

Heitkämper K, Reissig L, Bravin E, Glück S, Mann S. 2023. Digital technology adoption for plant protection: assembling the environmental, labour, economic and social pieces of the puzzle. Smart Agricultural Technology 4:100148

doi: 10.1016/j.atech.2022.100148
[119]

Whitfield S, Challinor AJ, Rees RM. 2018. Frontiers in climate-smart food systems: outlining the research space. Frontiers in Sustainable Food Systems 2:2

doi: 10.3389/fsufs.2018.00002
[120]

Klerkx L, Jakku E, Labarthe P. 2019. A review of social science on digital agriculture, smart farming and agriculture 4.0: new contributions and a future research agenda. NJAS: Wageningen journal of life sciences 90−91:100315

doi: 10.1016/j.njas.2019.100315
[121]

Vuran MC, Salam A, Wong R, Irmak S. 2018. Internet of underground things in precision agriculture: Architecture and technology aspects. Ad Hoc Networks 81:160−73

doi: 10.1016/j.adhoc.2018.07.017
[122]

Van Leeuwen T, Dermauw W, Mavridis K, Vontas J. 2020. Significance and interpretation of molecular diagnostics for insecticide resistance management of agricultural pests. Current Opinion in Insect Science 39:69−76

doi: 10.1016/j.cois.2020.03.006
[123]

Hofmann T, Lowry GV, Ghoshal S, Tufenkji N, Brambilla D, et al. 2020. Technology readiness and overcoming barriers to sustainably implement nanotechnology-enabled plant agriculture. Nature Food 1(7):416−25

doi: 10.1038/s43016-020-0110-1
[124]

Lu Y, Young S. 2020. A survey of public datasets for computer vision tasks in precision agriculture. Computers and Electronics in Agriculture 178:105760

doi: 10.1016/j.compag.2020.105760
[125]

Ferrández-Pastor FJ, García-Chamizo JM, Nieto-Hidalgo M, Mora-Pascual J, Mora-Martínez J. 2016. Developing ubiquitous sensor network platform using internet of things: application in precision agriculture. Sensors 16(7):1141

doi: 10.3390/s16071141
[126]

Gonzalez-de-Santos P, Ribeiro A, Fernandez-Quintanilla C. 2012. The RHEA Project: using a robot fleet for a highly effective crop protection. Proceedings of the International Conference of Agricultural Engineering (CIGR-AgEng 2012), Valencia, Spain, 2012 US: CIGR International Commission of Agricultural and Biosystems Engineering

[127]

da Silveira F, Lermen FH, Amaral FG. 2021. An overview of agriculture 4.0 development: systematic review of descriptions, technologies, barriers, advantages, and disadvantages. Computers and Electronics in Agriculture 189:106405

doi: 10.1016/j.compag.2021.106405
[128]

Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, et al. 2017. Deep learning for image-based cassava disease detection. Frontiers in Plant Science 8:1852

doi: 10.3389/fpls.2017.01852
[129]

Holloway L, Bear C. 2017. Bovine and human becomings in histories of dairy technologies: robotic milking systems and remaking animal and human subjectivity. BJHS Themes 2:215−34

doi: 10.1017/bjt.2017.2
[130]

Eastwood C, Klerkx L, Ayre M, Dela Rue B. 2019. Managing socioethical challenges in the development of smart farming: from a fragmented to a comprehensive approach for responsible research and innovation. Journal of Agricultural and Environmental Ethics 32:741−68

doi: 10.1007/s10806-017-9704-5
[131]

Rose DC, Wheeler R, Winter M, Lobley M, Chivers CA. 2021. Agriculture 4.0: making it work for people, production, and the planet. Land Use Policy 100:104933

doi: 10.1016/j.landusepol.2020.104933
[132]

Nordmann A. 2014. Responsible innovation, the art and craft of anticipation. Journal of Responsible Innovation 1(1):87−98

doi: 10.1080/23299460.2014.882064
[133]

Fraser EDG, Campbell M. 2019. Agriculture 5.0: reconciling production with planetary health. One Earth 1(3):278−80

doi: 10.1016/j.oneear.2019.10.022
[134]

Bronson K, Knezevic I. 2019. The digital divide and how it matters for Canadian food system equity. Canadian Journal of Communication 44(2):63

doi: 10.22230/cjc.2019v44n2a3489
[135]

Fraser A. 2020. The digital revolution, data curation, and the new dynamics of food sovereignty construction. The Journal of Peasant Studies 47(1):208−26

doi: 10.1080/03066150.2019.1602522
[136]

Mann L. 2018. Left to other peoples' devices? A political economy perspective on the big data revolution in development. Development and Change 49(1):3−36

doi: 10.1111/dech.12347
[137]

Trendov M, Varas S, Zeng M. 2018. Digital technologies in agriculture and rural areas: status report. Italy: FAO

[138]

Fielke S, Nelson T, Blackett P, Bewsell D, Bayne K, et al. 2017. Hitting the bullseye: learning to become a reflexive monitor in New Zealand. Outlook on Agriculture 46(2):117−24

doi: 10.1177/0030727017708490
[139]

Asveld L, Ganzevles J, Osseweijer P. 2015. Trustworthiness and responsible research and innovation: the case of the bioeconomy. Journal of Agricultural and Environmental Ethics 28:571−88

doi: 10.1007/s10806-015-9542-2
[140]

Stilgoe J, Owen R, Macnaghten P. 2013. Developing a framework for responsible innovation. Research Policy 42(9):1568−80

doi: 10.1016/j.respol.2013.05.008
[141]

Garnett T, Appleby MC, Balmford A, Bateman IJ, Benton TG, et al. 2013. Sustainable intensification in agriculture: premises and policies. Science 341(6141):33−34

doi: 10.1126/science.1234485
[142]

Rotz S, Gravely E, Mosby I, Duncan E, Finnis E, et al. 2019. Automated pastures and the digital divide: how agricultural technologies are shaping labour and rural communities. Journal of Rural Studies 68:112−22

doi: 10.1016/j.jrurstud.2019.01.023
[143]

Krishna Bahadur KC, Haque I, Legwegoh AF, Fraser EDG. 2016. Strategies to reduce food loss in the global south. Sustainability 8(7):595

doi: 10.3390/su8070595
[144]

Willett W, Rockström J, Loken B, Springmann M, Lang T, et al. 2019. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. The Lancet 393(10170):447−92

doi: 10.1016/S0140-6736(18)31788-4
[145]

Castelló Ferrer E, Rye J, Brander G, Savas T, Chambers D, et al. 2019. Personal food computer: a new device for controlled-environment agriculture. In Proceedings of the Future Technologies Conference (FTC) 2018, eds Arai K, Bhatia R, Kapoor S. Cham: Springer. Volume 2. pp. 1077–96. doi: 10.1007/978-3-030-02683-7_79

[146]

MacMillan T. 2018. Learning from farmer-led research. In For whom? Questioning the food and farming research agenda. UK: The Food Ethics Council. pp. 24−25

[147]

Long TB, Blok V. 2018. Integrating the management of socio-ethical factors into industry innovation: towards a concept of Open Innovation 2.0. International Food and Agribusiness Management Review 21(4):463−86

doi: 10.22434/ifamr2017.0040
[148]

Dalhaus T, Finger R. 2016. Can gridded precipitation data and phenological observations reduce basis risk of weather index–based insurance? Weather, Climate, and Society 8(4):409−19

doi: 10.1175/WCAS-D-16-0020.1
[149]

Javaid M, Haleem A, Singh RP, Suman R. 2022. Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks 3:150−64

doi: 10.1016/j.ijin.2022.09.004
[150]

Bernhardt H, Bozkurt M, Brunsch R, Colangelo E, Herrmann A, et al. 2021. Challenges for agriculture through industry 4.0. Agronomy 11(10):1935

doi: 10.3390/agronomy11101935
[151]

Singh G, Yogi KK. 2022. Internet of Things-based devices/robots in agriculture 4.0. In Sustainable Communication Networks and Application, eds Karrupusamy P, Balas VE, Shi Y. Singapore: Springer. Vol 93. pp. 87–102. doi: 10.1007/978-981-16-6605-6_6

[152]

Li D. 2018. Agriculture 4.0, the approaching age of intelligent agriculture. Journal of Agriculture 8(1):215−22

[153]

Kong Q, Kuriyan K, Shah N, Guo M. 2019. Development of a responsive optimization framework for decision-making in precision agriculture. Computers & Chemical Engineering 131:106585

doi: 10.1016/j.compchemeng.2019.106585
[154]

Hrustek L. 2020. Sustainability driven by agriculture through digital transformation. Sustainability 12(20):8596

doi: 10.3390/su12208596
[155]

Chuang JH, Wang JH, Liou YC. 2020. Farmers' knowledge, attitude, and adoption of smart agriculture technology in Taiwan. International Journal of Environmental Research and Public Health 17(19):7236

doi: 10.3390/ijerph17197236
[156]

Almadani B, Mostafa SM. 2021. IoT-based multimodal communication model for agriculture and agro-industries. IEEE Access 9:10070−88

doi: 10.1109/ACCESS.2021.3050391
[157]

Regan Á. 2019. 'Smart farming in Ireland: a risk perception study with key governance actors. NJAS: Wageningen Journal of Life Sciences 90−91:100292

doi: 10.1016/j.njas.2019.02.003
[158]

Pivoto D, Barham B, Waquil PD, Foguesatto CR, Dalla Corte VF, et al. 2019. Factors influencing the adoption of smart farming by Brazilian grain farmers. International Food and Agribusiness Management Review 22(4):571−88

doi: 10.22434/IFAMR2018.0086
[159]

Moon A, Kim J, Zhang J, Son SW. 2018. Evaluating fidelity of lossy compression on spatiotemporal data from an IoT-enabled smart farm. Computers and Electronics in Agriculture 154:304−13

doi: 10.1016/j.compag.2018.08.045
[160]

Aydin S, Aydin MN. 2020. Semantic and syntactic interoperability for agricultural open-data platforms in the context of IoT using crop-specific trait ontologies. Applied Sciences 10(13):4460

doi: 10.3390/app10134460
[161]

Farooq MS, Riaz S, Abid A, Abid K, Naeem, MA. 2019. A survey on the Role of IoT in agriculture for the Implementation of smart farming. IEEE Access 7:156237−71

doi: 10.1109/ACCESS.2019.2949703
[162]

Miranda J, Ponce P, Molina A, Wright P. 2019. Sensing, smart and sustainable technologies for Agri-Food 4.0. Computers in Industry 108:21−36

doi: 10.1016/j.compind.2019.02.002
[163]

Bacco M, Barsocchi P, Ferro E, Gotta A, Ruggeri M. 2019. The digitization of agriculture: a survey of research activities on smart farming. Array 3–4:100009

doi: 10.1016/j.array.2019.100009
[164]

Wang J, Yue H, Zhou Z. 2017. An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network. Food Control 79:363−70

doi: 10.1016/j.foodcont.2017.04.013
[165]

Grieve BD, Duckett T, Collison M, Boyd L, West J, et al. 2019. The challenges posed by global broadacre crops in delivering smart agri-robotic solutions: a fundamental rethink is needed. Global Food Security 23:116−24

doi: 10.1016/j.gfs.2019.04.011