A review on the role of IoT, ai, and blockchain in agriculture & crop diseases detection using a text mining approach
Introduction: This paper is the outcome of a review survey, “Role of IoT, AI and blockchain in agriculture and crop disease detection using a text mining approach,” done at Lovely Professional University in Punjab, India, in 2023.
Problem: Agriculture is a crucial industry that contributes significantly to the economies of many nations. Crop diseases are one of the issues that create a barrier to agricultural development.
Objective: Using machine learning, deep learning, image processing methods, the Internet of Things, and blockchain technology, this study provides a current summary of research done over the past years on disease identification of various crops.
Methodology: The text mining technique is applied to extract the related information from published papers and predict the following futuristic technologies to detect crop diseases early.
Results: This paper also covers the various issues, challenges, and potential solutions. It also emphasizes the wide range of tools available for identifying crop diseases.
Conclusion: This paper helps to extract valuable keywords through a text-mining approach and create a roadmap for another researcher.
Originality: Applied text mining visualization techniques, such as word cloud and word frequency, to extract the keywords.
Limitation: The literature survey only covers literature published prior to February 2023; it can be extended with
more studies published soon.
R. Abbasi, P. Martinez, R. Ahmad, “The digitization of agricultural industry–a systematic literature review on agriculture 4.0,” Smart Agricultural Technology, vol. 100042. doi: https://doi.org/10.1016/j.atech.2022.100042.
A. Rehman, T. Saba, M. Kashif, S.M. Fati, S.A. Bahaj, H. Chaudhry, H. “A revisit of internet of things technologies for monitoring and control strategies in smart agriculture,” Agronomy, vol.12, no. 1, p.127. doi: https://doi.org/10.3390/agronomy12010127.
S. Jiang, J. Zhou, S. Qiu, “Digital agriculture and urbanization: mechanism and empirical research,” Technological Forecasting and Social Change, vol. 180, pp. 121724. doi: https://doi.org/10.1016/j.techfore.2022.121724.
D. Aqel, S. Al-Zubi, A. Mughaid, Y. Jararweh, “Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture,” Cluster Computing, 1-14. doi: https://doi.org/10.1007/s10586-021-03397-y.
P.S. Thakur, P. Khanna, T. Sheorey, A. Ojha, “Trends in vision-based machine learning techniques for plant disease identification: A systematic review,” Expert Systems with Applications, vol.118117. doi: https://doi.org/10.1016/j.eswa.2022.118117.
J.D. Kothari, “Plant Disease Identification using Artificial Intelligence: Machine Learning Approach,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 7, no. 11, pp. 11082-11085. doi: 10.15680/IJIRSET.2019.0711081.
C. Jackulin, S. Murugavalli, “A comprehensive review on detection of plant disease using machine learning and deep learning approaches,” Measurement: Sensors, pp. 100441. doi: https://doi.org/10.1016/j.measen.2022.100441.
V.K. Vishnoi, K. Kumar, B. Kumar, „Plant disease detection using computational intelligence and image processing,” Journal of Plant Diseases and Protection, vol. 128, pp. 19-53. doi: https://doi.org/10.1007/s41348-020-00368-0.
R. Sujatha, J.M. Chatterjee, N.Z., Jhanjhi, S.N. Brohi, “Performance of deep learning vs machie learning in plant leaf disease detection,” Microprocessors and Microsystems, vol. 80, pp. 103615. doi: https://doi.org/10.1016/j.micpro.2020.103615.
H. Pallathadka, P. Ravipati, G.S. Sajja, K. Phasinam, T. Kassanuk, D.T. Sanchez, P. Prabhu, “Application of machine learning techniques in rice leaf disease detection,” Materials Today: Proceedings, vol. 51, pp. 2277-2280. doi: https://doi.org/10.1016/j.matpr.2021.11.398.
L. Li, S. Zhang, B. Wang, „Plant disease detection and classification by deep learning—a review,” IEEE Access, vol. 9, pp. 56683-56698. doi: 10.1109/ACCESS.2021.3069646.
R.I. Hasan, S.M. Yusuf, L. Alzubaidi, “Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion,” Plants, vol. 9, no. 10, p. 1302. doi: https://doi.org/10.3390/plants9101302.
A. Khattab, S.E. Habib, H. Ismail, S. Zayan, Y. Fahmy, M.M. Khairy, “An IoT-based cognitive monitoring system for early plant disease forecast,” Computers and Electronics in Agriculture, vol. 166, pp. 105028. doi: https://doi.org/10.1016/j.compag.2019.105028
H. Tian, T. Wang, Y. Liu, X. Qiao, Y. Li, “Computer vision technology in agricultural automation—A review,” Information Processing in Agriculture, vol. 7, no. 1, pp. 1-19. doi: https://doi.org/10.1016/j.inpa.2019.09.006.
M. Tudi, H. Daniel Ruan, L. Wang, J. Lyu, R. Sadler, D. Connell, D.T. Phung, “Agriculture development, pesticide application and its impact on the environment,” International journal of environmental research and public health, vol. 18, no. 3, pp. 1112. doi: https://doi.org/10.3390/ijerph18031112.
M.S. Bane, M.J. Pocock, C. Gibert, M. Forster, G. Oudoire, S.A. Derocles, D.A. Bohan, „Farmer flexibility concerning future rotation planning is affected by the framing of climate predictions,” Climate Risk Management, vol. 34, pp. 100356. doi: https://doi.org/10.1016/j.crm.2021.100356.
Y. Liu, J. Wang, Y. Shi, Z. He, F. Liu, W. Kong, Y. He, “Unmanned airboat technology and applications in environment and agriculture,” Computers and Electronics in Agriculture, vol. 197, pp. 106920. doi: https://doi.org/10.1016/j.compag.2022.106920
H.H. Khan, M.N. Malik, Z. Konečná, A.G. Chofreh, F.A. Goni, J.J. Klemeš, “Blockchain technology for agricultural supply chains during the COVID-19 pandemic: Benefits and cleaner solutions,” Journal of Cleaner Production, vol. 347, pp. 131268. doi: https://doi.org/10.1016/j.jclepro.2022.131268.
R. Abbasi, P. Martinez, R. Ahmad, “The digitization of agricultural industry–a systematic literature review on agriculture 4.0.” Smart Agricultural Technology, pp. 100042. doi: https://doi.org/10.1016/j.atech.2022.100042.
Z. Liu, R.N. Bashir, S. Iqbal, M.M.A. Shahid, M. Tausif, Q. Umer, “Internet of Things (IoT) and machine learning model of plant disease prediction–blister blight for tea plant,” IEEE Access, vol. 10, pp. 44934-44944. doi: 10.1109/ACCESS.2022.3169147.
Y. Zhao, X. Zhu, X. Chen, J.M. Zhou, „From plant immunity to crop disease resistance,” Journal of Genetics and Genomics. doi: https://doi.org/10.1016/j.jgg.2022.06.003.
C. Kinealy, “A Modest Proposal: New directions in researching and understanding Ireland‘s Great Famine,” History Compass, vol. 20, no. 5, e12726. doi: https://doi.org/10.1111/hic3.12726.
M.B. Riley, M.R. Williamson, O. Maloy, “Plant disease diagnosis,” The plant health instructor, vol.10. doi: https://doi.org/10.1094/PHI-I-2002-1021-01
M.S. Farooq, S. Riaz, A. Abid, K. Abid, M.A. Naeem, “A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming,” IEEE Access, vol. 7, pp. 156237-156271. doi: 10.1109/ ACCESS.2019.2949703.
T. Mizik, “How can precision farming work on a small scale? A systematic literature review,” Precision Agriculture, pp. 1-23. doi: https://doi.org/10.1007/s11119-022-09934-y.
R. Xu, C. Li, “A modular agricultural robotic system (MARS) for precision farming: Concept and implementation,” Journal of Field Robotics, vol. 39, no. 4, pp. 387-409. doi: https://doi.org/10.1002/rob.22056.
S.V. Gaikwad, A.D. Vibhute, K.V. Kale, S.C. Mehrotra, „An innovative IoT based system for precision farming,” Computers and Electronics in Agriculture, vol. 187, pp. 106291. doi: https://doi.org/10.1016/j.compag.2021.106291.
F.J. Mesas-Carrascosa, D.V. Santano, J.E. Meroño, M.S. De La Orden, A. García-Ferrer, “Open source hardware to monitor environmental parameters in precision agriculture,” Biosystems engineering, vol. 137, pp. 73-83. doi: https://doi.org/10.1016/j.biosystemseng.2015.07.005.
R. Akhter, S.A. Sofi, „Precision agriculture using IoT data analytics and machine learning,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 8, pp. 5602-5618. doi:https://doi.org/10.1016/j.jksuci.2021.05.013.
V.R. Pathmudi, N. Khatri, S. Kumar, A.S.H. Abdul-Qawy, A.K. Vyas, “A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications,” Scientific African, e01577. doi: https://doi.org/10.1016/j.sciaf.2023.e01577.
A. Sood, R.K. Sharma, A.K. Bhardwaj, “Artificial intelligence research in agriculture: a review,” Online Information Review, vol. 46, no. 6, pp. 1054-1075. doi: https://doi.org/10.1108/OIR-10-2020-0448.
B. Ramteke, S. Dongre, IoT Based Smart Automated Poultry Farm Management System. In 2022 10th International Conference on Emerging Trends in Engineering and Technology Signal and Information Processing (ICETET-SIP-22) (pp. 1-4). IEEE. doi: https://doi.org/10.1109/ICETET-SIP-2254415.2022.9791653.
A. Vijay, T. Garg, V. Goyal, A. Yadav, R. Mukherjee, A Low-Cost Edge-IoT Based Smart Poultry Farm. In 2023 15th International Conference on COMmunication Systems & NETworkS (COMSNETS) (pp. 397-399). IEEE. doi: 10.1109/COMSNETS56262.2023.10041317.
V.R. Pathmudi, N. Khatri, S. Kumar, A.S.H. Abdul-Qawy, A.K. Vyas, “A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications,” Scientific African, e01577. doi: https://doi.org/10.1016/j.sciaf.2023.e01577
A. Sood, R.K. Sharma, A.K. Bhardwaj, “Artificial intelligence research in agriculture: a review,” Online Information Review, vol. 46, no. 6, pp. 1054-1075. doi: https://doi.org/10.1108/OIR-10-2020-0448.
M.S. Farooq, S. Riaz, A. Abid, T. Umer, Y.B. Zikria, “Role of IoT technology in agriculture: A systematic literature review,” Electronics, vol. 9, no. 2, pp. 319. doi: https://doi.org/10.3390/electronics9020319.
E.M. Ouafiq, R. Saadane, A. Chehri, „Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge,” Agriculture, vol. 12, no. 3, p. 329. doi:
https://doi.org/10.3390/agriculture12030329.
K. Zkik, A. Belhadi, S.A. Rehman Khan, S.S. Kamble, M. Oudani, F.E. Touriki, “Exploration of barriers and enablers of blockchain adoption for sustainable performance: implications for e-enabled agriculture supply chains,” International Journal of Logistics Research and Applications, pp. 1-38. doi: https://doi.org/10.1080/13675567.2022.2088707.
N.K. Jadav, T. Rathod, R. Gupta, S. Tanwar, N. Kumar, A. Alkhayyat, “Blockchain and artificial intelligence-empowered smart agriculture framework for maximizing human life expectancy,” Computers and Electrical Engineering, vol. 105, pp. 108486. doi: https://doi.org/10.1016/j.compeleceng.2022.108486.
K. Shahzad, Q. Zhang, A.U. Zafar, M. Ashfaq, S.U. Rehman, “The role of blockchain-enabled traceability, task technology fit, and user self efficacy in mobile food delivery applications,” Journal of Retailing and Consumer Services, vol. 73, pp. 103331. doi: https://doi.org/10.1016/j.jretconser.2023.103331.
O. Jouini, K. Sethom, AgriBIoT: A Blockchain-Based IoT Architecture for Crop Insurance. In Advanced Information Networking and Applications: Proceedings of the 37th International Conference on Advanced Information Networking and Applications (AINA-2023), Volume 3 (pp. 340-350). Cham: Springer International Publishing. doi: https://doi.org/10.1007/978-3-031-28694-0_32.
R. Abbasi, P. Martinez, R. Ahmad, “The digitization of agricultural industry–a systematic literature review on agriculture 4.0,” Smart Agricultural Technology, pp. 100042. doi: https://doi.org/10.1016/j.atech.2022.100042.
M. Javaid, A. Haleem, I.H. Khan, R. Suman, “Understanding the potential applications of Artificial Intelligence in Agriculture Sector,” Advanced Agrochem, vol. 2, no. 1, pp.15-30. doi: https://doi.org/10.1016/j.aac.2022.10.001.
A. Subeesh, C.R. Mehta, “Automation and digitization of agriculture using artificial intelligence and internet of things,” Artificial Intelligence in Agriculture, vol. 5, pp. 278-291. doi: https://doi.org/10.1016/j.aiia.2021.11.004.
E.F.I. Raj, M. Appadurai, K. Athiappan, Precision farming in modern agriculture. In Smart Agriculture Automation Using Advanced Technologies: Data Analytics and Machine Learning, Cloud Architecture, Automation and IoT (pp. 61-87). Singapore: Springer Singapore. doi: https://doi.org/10.1007/978-981-16-6124-2_4.
D. Shadrin, A. Menshchikov, A. Somov, G. Bornemann, J. Hauslage, M. Fedorov, „Enabling precision agriculture through embedded sensing with artificial intelligence,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 4103-4113. doi: https://doi.org/10.1109/TIM.2019.2947125.
L. Jia, J. Wang, Q. Liu, Q. Yan, Application research of artificial intelligence technology in intelligent agriculture. In The 10th International Conference on Computer Engineering and Networks (pp. 219-225). Springer Singapore. doi: https://doi.org/10.1007/978-981-15-8462-6_25.
V.G. Dhanya, A. Subeesh, N.L. Kushwaha, D.K. Vishwakarma, T.N. Kumar, G. Ritika, A.N. Singh, “Deep learning based computer vision approaches for smart agricultural applications,” Artificial Intelligence in Agriculture. doi: https://doi.org/10.1016/j.aiia.2022.09.007.
J. Xu, B. Gu, G. Tian, “Review of agricultural IoT technology,” Artificial Intelligence in Agriculture. doi: https://doi.org/10.1016/j.aiia.2022.01.001.
N.K. Jadav, T. Rathod, R. Gupta, S. Tanwar, N. Kumar, A. Alkhayyat, “Blockchain and artificial intelligence-empowered smart agriculture framework for maximizing human life expectancy,” Computers and Electrical Engineering, vol. 105, pp. 108486. doi: https://doi.org/10.1016/j.compeleceng.2022.108486.
Y. Zhao, Q. Li, W. Yi, H. Xiong, “Agricultural IoT Data Storage Optimization and Information Security Method Based on Blockchain,” Agriculture, vol. 13, no. 2, p. 274. doi: https://doi.org/10.3390/agriculture13020274.
M. Senthilmurugan, R. Chinnaiyan, IoT and machine learning based peer to peer platform for crop growth and disease monitoring system using blockchain. In 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE. doi: https://doi.org/10.1109/ICCCI50826.2021.9402435.
A.S. Paymode, V.B. Malode, “Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG,” Artificial Intelligence in Agriculture, vol. 6, 23-33. doi: https://doi.org/10.1016/j.aiia.2021.12.002.
P. Bedi, P. Gole, “Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network,” Artificial Intelligence in Agriculture, vol. 5, pp. 90-101. doi: https://doi.org/10.1016/j.aiia.2021.05.002.
H. Orchi, M. Sadik, M. Khaldoun, “On using artificial intelligence and the internet of things for crop disease detection: A contemporary survey,” Agriculture, vol. 12, no. 1, pp. 9. doi: https://doi.org/10.3390/agriculture12010009.
V. Hassija, S. Batra, V. Chamola, T. Anand, P. Goyal, N. Goyal, M. Guizani, “A blockchain and deep neural networks-based secure framework for enhanced crop protection,” Ad Hoc Networks, vol. 119, pp. 102537. doi: https://doi.org/10.1016/j.adhoc.2021.102537.
K.M. Krishna, Y.D. Borole, S. Rout, P. Negi, M. Deivakani, R. Dilip, Inclusion of Cloud, Blockchain and IoT Based Technologies in Agriculture Sector. In 2021 9th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-8). IEEE. doi: https://doi.org/10.1109/CITSM52892.2021.9588894.
M. Junaid, A. Shaikh, M.U. Hassan, A. Alghamdi, K. Rajab, M.S. Al Reshan, M. Alkinani, “Smart agriculture cloud using AI based techniques,” Energies, vol. 14, no. 16, 5129. doi: https://doi.org/10.3390/en14165129.
T.A. Shaikh, R. Tabasum, R.L. Faisal, “Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming,” Computers and Electronics in Agriculture, vol. 198, pp. 107119. doi: https://doi.org/10.1016/j.compag.2022.107119.
S.H. Awan, et al. “A Combo Smart Model of Blockchain with the Internet of Things (IoT) for the Transformation of Agriculture Sector,” Wireless Personal Communications, vol. 121, no. 3, pp. 2233-2249. doi: https://doi.org/10.1007/s11277-021-08820-6.
A. Kamilaris, F. Agusti, X. Francesc, “Prenafeta-Boldύ. “The rise of blockchain technology in agriculture and food supply chains,” Trends in Food Science & Technology, vol.91, pp. 640-652. doi: https://doi.org/10.1016/j.tifs.2019.07.034.
J. Yang, et al., “A comparative evaluation of convolutional neural networks, training image sizes, and deep learning optimizers for weed detection in Alfalfa,” Weed Technology, vol. 36, no. 4, pp. 512-522. doi:10.1017/wet.2022.46.
E. Paradis, B. O’Brien, L. Nimmon, G. Bandiera, M.A. Martimianakis, “Design: Selection of data collection methods,” Journal of graduate medical education, vol. 8, no. 2, pp. 263-264. doi: https://doi.org/10.4300/JGME-D-16-00098.1.
V. Puri, S. Mondal, S. Das, V.G. Vrana, “Blockchain Propels Tourism Industry—An Attempt to Explore Topics and Information in Smart Tourism Management through Text Mining and Machine Learning,” Informatics, vol. 10, no. 1, p. 9. doi: https://doi.org/10.3390/informatics10010009.
D.W. Otter, J.R. Medina, J.K. Kalita, „A survey of the usages of deep learning for natural language processing,” IEEE transactions on neural networks and learning systems, vol. 32, no. 2, pp. 604-624. doi: https://doi.org/10.1109/TNNLS.2020.2979670.
D. Antons, E. Grünwald, P. Cichy, T.O. Salge, „The application of text mining methods in innovation research: current state, evolution patterns, and development priorities,” R&D Management, vol.50, no. 3, pp. 329-351. doi: https://doi.org/10.1111/radm.12408.
L. Obasuyi, O.A. Okwilagwe, “Institutional factors influencing utilisation of Research4Life databases by National Agricultural Research Institutes scientists in Nigeria,” Information Development, vol. 34, no. 2, pp. 122-138. doi: https://doi.org/10.1177/0266666916679218.
I. Priyadarshini, P. Mohanty, R. Kumar, R. Sharma, V. Puri, P.K. Singh, “A study on the sentiments and psychology of twitter users during COVID-19 lockdown period,” Multimedia Tools and Applications, vol. 81, no. 19, pp. 27009-27031. doi: https://doi.org/10.1007/s11042-021-11004-w.
F. da Silveira, F.H. Lermen, F.G. Amaral, “An overview of agriculture 4.0 development: Systematic review of descriptions, technologies, barriers, advantages, and disadvantages,” Computers and electronics in agriculture, vol. 189, pp. 106405. doi: https://doi.org/10.1016/j.compag.2021.106405
Y. Liu, L. Wang, T. Shi, J. Li, „Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM,” Information Systems, vol. 103, pp. 101865. doi: https://doi.org/10.1016/j.is.2021.101865.
S.P. Mohanty, D.P. Hughes, M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in plant science, vol. 7, pp. 1419. doi: https://doi.org/10.3389/fpls.2016.01419.
M. Xu, S. Yoon, Y. Jeong, D.S. Park, “Transfer learning for versatile plant disease recognition with limited data,” Frontiers in Plant Science, vol. 13, pp. 4506. doi: https://doi.org/10.3389/fpls.2022.1010981.
V. Singh, N. Sharma, S. Singh, “A review of imaging techniques for plant disease detection,” Artificial Intelligence in Agriculture, vol. 4, pp. 229-242. doi: https://doi.org/10.1016/j.aiia.2020.10.002.
R. Chen, H. Qi, Y. Liang, M. Yang, „Identification of plant leaf diseases by deep learning based on channel attention and channel pruning,” Frontiers in Plant Science, vol. 13. doi: 10.3389/fpls.2022.1023515.
A. Musa, M. Hassan, M. Hamada, F. Aliyu, “Low-Power Deep Learning Model for Plant Disease Detection for Smart-Hydroponics Using Knowledge Distillation Techniques,” Journal of Low Power Electronics and Applications, vol. 12, no. 2, p. 24. doi: https://doi.org/10.3390/jlpea12020024.
H.F. Pardede, E. Suryawati, V. Zilvan, A. Ramdan, R.B.S. Kusumo, A. Heryana, V.P. Rahadi, “Plant diseases detection with low resolution data using nested skip connections,” Journal of Big Data, vol. 7, pp. 1-21. doi: https://doi.org/10.1186/s40537-020-00332-7.
D.D. Leal-Lara, J. Barón-Velandia, C.E. Rocha-Calderón, “Interpretability in the Field of Plant Disease Detection: A Review,” Revista Facultad de Ingeniería, vol. 30, no. 58. doi: https://doi.org/10.19053/01211129.v30.n58.2021.13495.
D. Shah, V. Trivedi, V. Sheth, A. Shah, U. Chauhan, “ResTS: Residual deep interpretable architecture for plant disease detection,” Information Processing in Agriculture, vol. 9, no. 2, pp. 212-223. doi: https://doi.org/10.1016/j.inpa.2021.06.001.
J. Xu, B. Gu, G. Tian, “Review of agricultural IoT technology,” Artificial Intelligence in Agriculture. doi: https://doi.org/10.1016/j.aiia.2022.01.001.
S. Awan, S. Ahmed, F. Ullah, A. Nawaz, A. Khan, M.I. Uddin, H. Alyami, “IoT with blockchain: A futuristic approach in agriculture and food supply chain,” Wireless Communications and Mobile Computing, pp. 1-14. doi: https://doi.org/10.1155/2021/5580179.
N. Kalatzis, N. Marianos, F. Chatzipapadopoulos, IoT and data interoperability in agriculture: A case study on the gaiasense TM smart farming solution. In 2019 Global IoT Summit (GIoTS) (pp. 1-6). IEEE. doi: 10.1109/GIOTS.2019.8766423.
K. Tržec, M. Kušek, I.P. Žarko, Building an Interoperable IoT Ecosystem for Data-Driven Agriculture. In 2022 International Conference on Smart Systems and Technologies (SST) (pp. 341-347). IEEE. doi: https://doi.org/10.1109/SST55530.2022.9954641.
M.A.U. Rehman, R. Ullah, C.W. Park, D.H. Kim, B.S. Kim, „Improving resource-constrained IoT device lifetimes by mitigating redundant transmissions across heterogeneous wireless multimedia of things,” Digital Communications and Networks, vol. 8, no.5, pp. 778-790. doi: https://doi.org/10.1016/j.dcan.2021.09.004.
M.S. Farooq, S. Riaz, A. Abid, T. Umer, Y.B. Zikria, “Role of IoT technology in agriculture: A systematic literature review,” Electronics, vol. 9, no. 2, p. 319. doi: https://doi.org/10.3390/electronics9020319.
M. Bacco, P. Barsocchi, E. Ferro, A. Gotta, M. Ruggeri, “The digitisation of agriculture: a survey of research activities on smart farming,” Array, vol. 3, pp. 100009. doi: https://doi.org/10.1016/j.array.2019.100009.
M. Amiri-Zarandi, R.A. Dara, E. Duncan, E.D. Fraser, “Big data privacy in smart farming: a review,” Sustainability, vol. 14, no. 15, pp. 9120. doi: https://doi.org/10.3390/su14159120.
V.S. Narwane, A. Gunasekaran, B.B. Gardas, „Unlocking adoption challenges of IoT in Indian agricultural and food supply chain,” Smart Agricultural Technology, vol. 2, pp. 100035. doi: https://doi.org/10.1016/j.atech.2022.100035.
A.U. Mentsiev, E.F. Amirova, IoT and mechanization in agriculture: problems, solutions, and prospects. In IOP conference series: earth and environmental science, vol. 548, no. 3, pp. 032035. IOP Publishing. doi: 10.1088/1755-1315/548/3/032035.
A.P. Antony, K. Leith, C. Jolley, J. Lu, D.J. Sweeney, “A review of practice and implementation of the internet of things (IoT) for smallholder agriculture,” Sustainability, vol. 12, no. 9, pp. 3750. doi: https://doi.org/10.3390/su12093750.
Y. Ampatzidis, L. De Bellis, A. Luvisi, “iPathology: robotic applications and management of plants and plant diseases,” Sustainability, vol. 9, no. 6, pp. 1010. doi: https://doi.org/10.3390/su9061010.
H. Yuan, G. Li, L. Feng, J. Sun, Y. Han, Automatic view generation with deep learning and reinforcement learning. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 1501-1512). IEEE. doi: https://doi.org/10.1109/ICDE51399.2021.00217
R. Reedha, E. Dericquebourg, R. Canals, A. Hafiane, “Transformer neural network for weed and crop classification of high resolution UAV images,” Remote Sensing, vol. 14, no. 3, p. 592. doi: https://doi.org/10.3390/rs14030592.
D. Gao, Q. Sun, B. Hu, S. Zhang, „A framework for agricultural pest and disease monitoring based on internet-of-things and unmanned aerial vehicles,” Sensors, vol. 20, no. 5, p. 1487. doi: https:// doi.org/10.3390/s20051487.
L.N. Thalluri, S.D. Adapa, D. Priyanka, A.V.N. Sarma, S.N. Venkat, Drone Technology Enabled Leaf Disease Detection and Analysis system for Agriculture Applications. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1079-1085). IEEE. doi: https://doi.org/10.1109/ICOSEC51865.2021.9591837.
J.B. Ristaino, P.K. Anderson, D.P. Bebber, K.A. Brauman, N.J. Cunniffe, N.V. Fedoroff, Q. Wei, “The persistent threat of emerging plant disease pandemics to global food security,” Proceedings of the National Academy of Sciences, vol. 118, no. 23, e2022239118. doi: https://doi.org/10.1073/pnas.2022239118
B. Hari Hara Suthan, S.M. Jagannath, M. Hari Narasimhan, T. Sasikala, Detection of Crop Diseases Using Agricultural Drone. In Advances in Power Systems and Energy Management: Select Proceedings of ETAEERE 2020 (pp. 509-517). Springer Singapore. doi: https://doi.org/10.1007/978-981-15-7504-4_50.
P. Johri, J.N. Singh, S.K. Khatri, A. Bagchi, E. Rajesh, “Role of Satellites in Agriculture,” Smart IoT for Research and Industry, pp. 109-120. doi: https://doi.org/10.1007/978-3-030-71485-7_6.
R. Dainelli, F. Saracco, “Bibliometric and social network analysis on the use of satellite imagery in agriculture: an entropy-based approach,” Agronomy, vol. 13, no. 2, p. 576. doi: https://doi.org/10.3390/agronomy13020576.
V.G. Bhujade, V. Sambhe, “Role of digital, hyper spectral, and SAR images in detection of plant disease with deep learning network,” Multimedia Tools and Applications, vol. 81, no. 23, pp. 33645-33670. doi: https://doi.org/10.1007/s11042-022-13055-z.
R. Rayhana, G. Xiao, Z. Liu, “RFID sensing technologies for smart agriculture,” IEEE Instrumentation & Measurement Magazine, vol. 24, no, 3, pp. 50-60. doi: https://doi.org/10.1109/MIM.2021.9436094.
G. Chakaravarthi, “RFID technology and its diverse applications: A brief exposition with a proposed Machine Learning approach,” Measurement, pp. 111197. doi: https://doi.org/10.1016/j.measurement.2022.111197.
L. Ruiz-Garcia, L. Lunadei, “The role of RFID in agriculture: Applications, limitations and challenges,” Computers and electronics in agriculture, vol. 79, no. 1, pp. 42-50. doi: https://doi.org/10.1016/j.compag.2011.08.010.
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