Artificial Intelligence in Smart Agriculture: Applications and Challenges
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Abstract
Artificial intelligence has been categorized as a subfield of computer science wherein machines perform smart learning tasks with the help of data and statical methods. Agriculture is one of the oldest social activities performed by humans. It provides many crucial things like raw materials, food, and employment. Due to the increasing population, it is the need of the hour that the agriculture sector should increase production of resources to match actual demand. Many agronomic factors such as weeds, pests, water condition and availability, and climate conditions impact overall yield. At present, methods used by farmers for management are traditional and insufficient to meet increased demand. To match future demand, new innovative agriculture methos need to be adopted. Artificial intelligence techniques in smart farm monitoring can enhance the quality and quantity of yield. This paper surveys different areas in agriculture where artificial intelligence is applicable. Artificial intelligence enables farmers to access farm-related data and analytical methods that will foster better agronomy, reduce waste, and improve efficiencies with minimum environmental impact. Various artificial intelligence techniques that make agriculture smarter than its previous forms are discussed. In this paper, the implementation of various artificial intelligence techniques in smart agriculture is studied. The aim of this study is to present different key applications and associated challenges to open up new future opportunities.
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References
Ahmed, A.N., De, D. and Hussain, I.M., 2018. Internet of things (IoT) for smart precision agriculture and farming in rural areas. IEEE Internet Things Journal, 5(6), 4890-4899, https://doi.org/10.1109/JIOT.2018.2879579.
Tilman, D., Balzer, C., Hill, J. and Befort, B.L., 2011. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America, 108(50), 20260-20264, https://doi.org/10.1073/pnas.1116437108.
Godfray, H.C. and Garnett, T., 2014. Food security and sustainable intensification. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 369(1639), https://doi.org/10.1098/rstb.2012.0273.
Godfray, H.C., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M. and Toulmin, C., 2010. Food security: the challenge of feeding 9 billion people. Science, 327(5967), 812-818, https://doi.org/10.1126/science.1185383.
Somov, A., Shadrin, D., Fastovets, I., Nikitin, A., Matveev, S. Seledets, I. and Hrinchuk, O., 2018. Pervasive agriculture: IoT-enabled greenhouse for plant growth control. IEEE Pervasive Computing, 17(4), 65-75, https://doi.org/10.1109/MPRV.2018.2873849.
Tombe, R., 2020. Computer vision for smart farming and sustainable agriculture. IST-Africa Conference (IST-Africa), Kampala, Uganda, May 18-22, 2020, pp. 1-8.
Mojumdar, M.U. and Chakraborty, N.R., 2020. A computer vision technique to detect scab on Malabar Nightshade. 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, July 1-3, 2020, pp. 1-4.
Puranik, V., Sharmila, Ranjan, A. and Kumari, A., 2019. Automation in agriculture and IoT. 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), Ghaziabad, India, April 18-19, 2019, pp. 1-6.
Abrishambaf, O., Faria, P. and Vale, Z., 2019. Energy resource scheduling in an agriculture system using a decision tree approach. 20th International Conference on Intelligent System Application to Power Systems (ISAP), New Delhi, India, December 10-14, 2019, pp. 1-5.
Li, X. and Zhang, R., 2020. Integrated multi-dimensional technology of data sensing method in smart agriculture. IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, December 11-13, 2020, pp. 2146-2149.
Sood, K., Singh, S, Singh R., Rana, A., Kalia, V. and Kaushal, A., 2015. Application of GIS in precision agriculture. National Seminar on Precision Farming Technologies for High Himalayas, Jammu-Kashmir, India, October 4-5, 2015, pp. 8-16.
Keswani, B., Mohapatra, A.G., Mohanty, A., Khanna, A., Rodrigues, J., Gupta, D. and Albuquerque, V.H., 2018. Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Computing and Applications, 31, 277-292, https://doi.org/10.1007/s00521-018-3737-1.
Waleed, M., Um, T.W., Kamal, T., Khan, A. and Iqbal, A., 2020. Determining the precise work area of agriculture machinery using Internet of things and artificial intelligence. Applied Sciences, 10(10), https://doi.org/10.3390/app10103365.
Zude-Sasse, M., Fountas, S., Gemtos, T.A. and Abu-Khalaf, N., 2016. Applications of precision agriculture in horticultural crops. European Journal of Horticultural Science, 81(2), 78-90, https://doi.org/10.17660/eJHS.2016/81.2.2.
Martínez, R., Pastor, J.A., Alvarez, B. and Iborra, A., 2016. A testbed to evaluate the fiware-based iot platform in the domain of precision agriculture. Sensors, 16(11), https://doi.org/ 10.3390/s16111979.
Javaid, M., Haleem, A., Khan, I.H. and Suman, R., 2023. Understanding the potential applications of artificial intelligence in agriculture sector. Advanced Agrochem, 2, 15-30, https://doi.org/10.1016/j.aac.2022.10.001.
Javaid, M., Haleem, A., Singh, R.P. and Suman, R., 2023. Enhancing smart farming through the applications of agriculture 4.0 technologies. International Journal of Intelligent Networks, 3, 150-164, https://doi.org/10.1016/j.ijin.2022.09.004.
Yang, X., Shu, L., Chen, J., Ferrag, M., Wu, J., Nurellari, E. and Huang, K., 2021, A Survey on smart Agriculture: Development modes Technologies, and security and Privacy Challenges. IEEE/CAA Journal of Automatica Sinica, 8(2), 273-302, https://doi.org/ 10.1109/JAS.2020.1003536.
Mkrttchain, V., 2021. Artificial and natural intelligence techniques as IoP- and IoT-based technologies for sustainable farming and smart agriculture. In: P. Tomar and G. Kaur, eds. Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture. Pennsylvania, IGI Global, pp. 40-53.
Reynolds, D., Ball, J., Bauer, A., Davey, R., Grififths, S. and Zhou, J., 2019. Cropsight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management. Gigascience, 8(3), https://doi.org/10.1093/gigascience/giz009.
Xie, B.X., Chung, S.C. and Chang, C.L., 2020. Design and implementation of a modular AI-enabled shovel weeder. International Symposium on Computer, Consumer and Control (IS3C), Taichung City, Taiwan, November 13-16, pp. 236-239.
Dhingra, S., Dhingra, A.K. and Gupta, S.B., 2022. Smart farming: An IOT based automation. In: Y.-C. Hu, S. Tiwari, M.C. Trivedi and K.K. Mishra, eds. Ambient Communications and Computer Systems. Singapore: Springer, pp. 79-88.
Brilhador, A., Gutoski, M. Hattori, L.T., Inácio, A.D.S., Lazzaretti, A.E. and Lopes, H.S., 2019. Classification of weeds and crops at the pixel-level using convolutional neural networks and data augmentation. 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Guayaquil, Ecuador, November 11-15, 2019, pp. 1-6.
Nicolas, C., Naila, B. and Amar, R.C., 2022. TinyML smart sensor for energy saving in internet of things precision agriculture platform. Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), Barcelona, Spain, July 5-8, 2022, pp. 256-259.
Stevanato, L., Baroni, G., Cohen, Y., Fontana, C.F., Gatto, S., Lunardon, M., Marinello, F., Moretto, S. and Morselli, L., 2019. A novel cosmic-ray neutron sensor for soil moisture estimation over large areas. Agriculture, 9(9), https://doi.org/10.3390/agriculture9090202.
Reiser, D., Sehsah, E.S., Bumann, O., Morhard, J. and Griepentrog, H.W., 2019. Development of an autonomous electric robot implement for intra-row weeding in vineyards. Agriculture, 9(1), https://doi.org/10.3390/agriculture9010018.
Bourodimos, G., Koutsiaras, M., Psiroukis, V., Balafoutis, A. and Fountas, S., 2019. Development and field evaluation of a spray drift risk assessment tool for vineyard spraying application. Agriculture, 9(8), https://doi.org/10.3390/agriculture9080181.
Kim, T., Lee, J., Sun, G., Park, B., Park, H., Choi, D. and Ye, S., 2022. Comparison of X-ray computed tomography and magnetic resonance imaging to detect pest-infested fruits: A pilot study. Nuclear Engineering and Technology, 54(2), 514-522, https://doi.org/ 10.1016/j.net.2021.07.015.
Juan, M., Jurado, López, A., Pádua, L. and Sousa, J., 2022. Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry. International Journal of Applied Earth Observation and Geoinformation, 112, https://doi.org/10.1016/j.jag.2022.102856.
Anastasiou, E., Balafoutis, A.T. and Fountas, S., 2023. Trends in remote sensing technologies in olive cultivation. Smart Agricultural Technology, 3, https://doi.org/10.1016/j.atech.2022.100103.
Khoddamzadeh, A.A. and Costa, B.N., 2023. Best nitrogen management practices using sensor-based smart agriculture in nursery production of cacao. Horticulturae, 9, https://doi.org/10.3390/horticulturae9040454.
Maddikunta P.K., Hakak, S., Alazab, M., Bhattacharya, S., Gadekallu, T.R., Khan, W. and Pham, Q., 2021. Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges. IEEE Sensors Journal, 21(16), 17608-17619, https://doi.org/10.1109/JSEN.2021.3049471.
Elijah, O., Rahman, T.A., Orikumhi, I., Leow, C.Y. and Hindia, M.N., 2018. An overview of internet of things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of Things Journal, 5(5), https://doi.org/10.1109/JIOT.2018.2844296.
Villa-Henriksen, A., Edwards, G., Pesonen, L.A., Green, O. and Sørensen, C.A., 2020. Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosystems Engineering, 191, 60-84, https://doi.org/10.1016/j.biosystemseng.2019.12.013.
Pathmudi, V.R., Khatri, N., Kumar, S., Abdul-Qawy, A.S.H. and Vyas, A.K., 2023. A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications. Scientific African, 19, https://doi.org/10.1016/j.sciaf.2023.e01577.
Imam, S.A., Choudhary, A. and Sachan, V.K., 2015. Design issues for wireless sensor networks and smart humidity sensors for precision agriculture: A review. 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), Faridabad, India, October 8-10, 2015, pp. 181-187.
Hu, Z., Xu, L., Cao, L., Liu, S., Luo, Z., Wang, J., Li, X. and Wang, L., 2019. Application of Non-orthogonal multiple access in wireless sensor networks for smart agriculture. IEEE Access, 7, 87582-87592, https://doi.org/10.1109/ACCESS.2019.2924917.
Junior, A.A., Silva, T.J. and Andrade, S.P., 2023. Smart IoT lysimetry system by weighing with automatic cloud data storage. Smart Agricultural Technology, 4, https://doi.org/10.1016/j.atech.2023.100177.
Debauche, O., Mahmoudi, S., Manneback, P. and Lebeau, F., 2022. Cloud and distributed architectures for data management in agriculture 4.0: Review and future trends, Journal of King Saud University - Computer and Information Sciences, 34(9), 7494-7514, https://doi.org/10.1016/j.jksuci.2021.09.015.
Cravero, A., Pardo, S., Sepúlveda, S. and Muñoz, L., 2022. Challenges to use machine learning in agricultural big data: A systematic literature review. Agronomy, 12(3), https://doi.org/10.3390/agronomy12030748.
Mi, C., Peng, X., Peng, L., Zhao, C. and Deng, X., 2021. Research on crop disaster stress risk mapping system based on agriculture big data. 2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA), Huaihua, China, December 10-12, 2021, pp. 525-530.
Cravero, S. and Sepulveda, A., 2021. Use and adaptations of machine learning in big data- applications in real cases in agriculture. Electronics, 10(5), https://doi.org/10.3390/electronics10050552.
Dhanya, V.G., Subeesh, A., Kushwaha, N.L., Vishwakarma, D.K., Kumar, T.N., Ritika, G., Singh and Dinesh, K., 2022. Deep learning based computer vision approaches for smart agricultural applications, Artificial Intelligence in Agriculture, 6, 211-229, https://doi.org/10.1016/j.aiia.2022.09.007.
Muniasamy, A., 2020. Machine learning for smart farming: A focus on desert agriculture. 2020 International Conference on Computing and Information Technology (ICCIT-1441), Tabuk, Saudi Arabia, September 9-10, 2020, pp. 1-5.
Liu, F. and Yan, X., 2022. The application of machine learning and IOT in smart agriculture. 2022 Global Conference on Robotics, Artificial Intelligence and Information Technology (GCRAIT), Chicago, USA, July 30-31, 2022, pp. 50-53.
Devi, M.A., Suresh, D., Jeyakumar, D., Swamydoss, D. and Florence, M.L., 2022. Agriculture crop selection and yield prediction using machine learning algorithms. Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, February 23-25, 2022, pp. 510-517.
Jin, X., Zhang, J., Kong, J., Su, T. and Bai, Y.A., 2022. A reversible automatic selection normalization (RASN) deep network for predicting in the smart agriculture system. Agronomy, 12, https://doi.org/10.3390/agronomy12030591.
Zhai, Z., Martinez, J.F., Beltran, V. and Martinez, N.L., 2020. Decision support system for agriculture 4.0: Survey and challenges. Computer and Electronics in Agriculture, 170, https://doi.org/10.1016/j.compag.2020.105256.
Recio, B., Rubio, F. and Criado, J.A., 2003. A decision support system for farm planning using AgriSupport II. Decision Support System, 36, 189-203, https://doi.org/10.1016/S0167-9236(02)00134-3.
Conesa-Munoz, J., Valente, J., Cerro, J.D., Barrientos, A. and Ribeiro, A., 2016, A multi-robot sense-act approach to lead to a proper acting in environmental incidents, Sensors, 16(8), https://doi.org/10.3390/s16081269.
Zhang, X., Zhang, J., Li, L., Zhang, Y., and Yang, G., 2017. Monitoring citrus soil moisture and nutrients using an IoT-based system. Sensors, 17(3), https://doi.org/10.3390/s17030447.
Chen, Q., Li, L., Chong, C. and Wang, X., 2021. AI enhanced soil management and smart farming. Soil Use and Management, 38(1), 7-13, https://doi.org/10.1111/sum.12771.
Falkenmark, M. and Rockström, J., 2004. Balancing Water for Humans and Nature: The New Approach in Ecohydrology. Sterling: Earthscan.
Aggarwal, S. and Kumar, A., 2019. A smart irrigation system to automate irrigation process using IOT and artificial neural network. 2nd International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India, March 29-30, 2019, pp. 310-314.
Mohanraj, I., Gokul, V., Ezhilarasie, R. and Umamakeswari, A., 2017. Intelligent drip irrigation and fertigation using wireless sensor networks. IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Chennai, India, April 7-8, 2017, pp.36-41.
Nojavan, M., 2001. Principles of Weed Control. Urmia: University of Urmia Press.
Alam, M., Alam, S., Roman, M., Tufail, M., Khan, M.U. and Khan, M.T., 2020. Real-time machine-learning based crop/weed detection and classification for variable-rate spraying in precision agriculture. 7th International Conference on Electrical and Electronics Engineering, Antalya, Turkey, April 14-16, 2020, pp. 273-280.
Tejeda, A.J. and Castro, R., 2019. Algorithm of weed detection in crops by computational vision. International Conference on Electronics, Communications and Computers (CONIELECOMP), Cholula, Mexico, March 25, 2019, pp. 124-128.
Liu, T., Chen, W., Wu, W., Sun, C., Guo, W. and Zhu, X., 2016. Detection of aphids in wheat fields using a computer vision technique, Biosystems Engineering, 141, 82-93, https://doi.org/10.1016/j.biosystemseng.2015.11.005.
Singh, K.K., 2018. An artificial intelligence and cloud based collaborative platform for plant disease identification, tracking and forecasting for farmers. IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), Banglore, India, November 23-24, 2018, pp. 49-56.
Bahtiar, A.R., Pranowo, Santoso, A.J. and Juhariah, J., 2020. Deep learning detected nutrient deficiency in chili plant. 8th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia, August 13, 2020, pp. 1-4.
Priyanka, T., Soni, P. and Malathy, C., 2018. Agricultural crop yield prediction using artificial intelligence and satellite imagery. Eurasian Journal of Analytical Chemistry, 13, 6-12.
Xu, X., Gao, P., Zhu, X., Guo, W., Ding, J., Li, C. and Wu, X., 2019. Design of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province. Ecological Indicators, 101, 943-953, https://doi.org/10.1016/j.ecolind.2019.01.059.
Medar, R.A., Rajpurohit, V.S., and Shweta, S., 2019. Crop yield prediction using machine learning techniques. IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India, March 29-31, 2019, pp.1-5.
Oikonomidis, A., Catal, C. and Kassahun, A., 2022. Deep learning for crop yield prediction: a systematic literature review. New Zealand Journal of Crop and Horticultural Science, 51(1), 1-26, https://doi.org/10.1080/01140671.2022.2032213.
De-An, Z., Jidong, L., Wei, J., Ying, Z. and Yu, C., 2011. Design and control of an apple harvesting robot. Biosystem Engineering, 110(2), 112-122, https://doi.org/10.1016/j.biosystemseng.2011.07.005.
Li, J., Tang, Y, Zou, X, Lin, G. and Wang, H., 2020. Detection of fruit-bearing branches and localization of litchi clusters for vision-based harvesting robots. IEEE Access, 8, 117746-117758, https://doi.org/10.1109/ACCESS.2020.3005386.
Awan, S., Ahmed, S., Ullah, F., Nawaz, A., Khan, A., Uddin M., Alharbi, A., Alosaimi, W. and Alyami, H., 2021. IoT with blockchain: A futuristic approach in agriculture and food supply chain. Wireless Communications and Mobile Computing, 14, https://doi.org/10.1155/2021/5580179.
Xiong, H., Dalhaus, T., Wang, P. and Huang, J., 2020. Blockchain technology for agriculture: Applications and rationale. Frontiers in Blockchain, 3, https://doi.org/10.3389/fbloc.2020.00007.
Kumar, S.A., Vealey, T. and Srivastava, H., 2016. Security in internet of things: challenges, solutions and future directions. In: Proceedings of the Annual Hawaii International Conference on System Sciences, Koloa, USA, January 5-8, 2016, pp. 5772-5781.
Silver, D.L. and Monga, T., 2019. In vino veritas: Estimating vineyard grape yield from images using deep learning. In: M.-J. Meurs and F. Rudzicz, eds. Advances in Artificial Intelligence. Cham: Springer International Publishing, pp. 212-224.
Sharma, V., Tripathi, K.A. and Mittal, H., 2022. Technological revolutions in smart farming: Current trends, challenges and future directions. Computers and Electronics in Agriculture, 201, https://doi.org/10.1016/j.compag.2022.107217.
Chu, H., Yang, Y., Li, Q., Xu, Y. and Wei, H, 2016. A scalable clinical intelligent decision support system Telematics. Proceedings of 14th International Conference on Smart Homes and Health Telematics, Wuhan, China, May 25-27, 2016, pp. 159-165.
Vasumathi, M.T. and Kamarasan, M., 2019. Fruit disease prediction using machine learning over big data. International Journal of Recent Technology and Engineering, 7(5), 556-559.
Barbedo, J.G., 2018. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computer and Electronic in Agriculture, 153, 46-53, https://doi.org/10.1016/j.compag.2018.08.013.
Alibabaei, K., Gaspar, P.D. and Lima, T.M., 2021. Modeling soil water content and reference evapotranspiration from climate data using deep learning method. Applied Sciences, 11(11), https://doi.org/10.3390/app11115029.
Zoph, B. and Le, Q.V., 2016. Neural Architecture Search with Reinforcement Learning. [online] Available at: https://arxiv.org/pdf/1611.01578.pdf.
Devi, M.S., Suguna, R., Joshi, A.S. and Bagate, R.A., 2019. Design of IoT blockchain based smart agriculture for enlightening safety and security. In: A. Somani, S. Ramakrishna, A. Chaudhary, C. Choudhary and B. Agarwal, eds. Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics. Singapore: Springer, pp. 8-19.