Precision Farming Utilizing Internet of Things, Artificial Intelligence and Automation: An Overview
Keywords:
Artificial intelligence, Automation, Irrigation, Herbicide, Pesticide, Precision farmingAbstract
The food demand is rising enormously due to the increasing population worldwide. The farmers’ conventional practices need to be revised to meet the dietary needs of the global masses. Moreover, the excess use of chemical fertilizers, pesticides, herbicides, and water degrades the environment and increases the production cost of cultivation. To satisfy the requirements of a rapidly rising global population, farming requires modern scientific and technological implementations like the Internet of Things (IoT), Artificial Intelligence (AI), and Automation. Technology-based precision farming has started to play an essential role in soil, insect, weed and irrigation management, eventually boosting agricultural output. AI-powered automation in precision farming can save water, pesticides, and herbicides, maintain soil fertility, and boost the productivity and quality of agricultural products by drastically lowering waste. Before AI-based automation can be extensively embraced by all types of farmers worldwide, several issues need to be resolved, such as the unequal distribution of mechanization, algorithms’ capacity to reliably handle enormous volumes of data, and data security and privacy. In the study, we reviewed several scientific papers on the potential applications of IoT, AI, and some other emerging innovations for creating automated innovative farm machinery, irrigation systems and drones for the protection of plants, nutrient, pesticide and herbicide management and crop health monitoring.
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Ampatzidis, Y., De Bellis, L., and Luvisi, A. (2017). Ipathology: Robotic applications and management of plants and plant diseases. Sustainability, 9(6), 1010. https://doi.org/10.3390/su9061010.
Arvind, G., Athira, V. G., Haripriya, H., Akshaya Rani, R., and Aravind, S. (2017). Automated irrigation with advanced seed germination and pest control. In 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), pages 64–67. https://api.semanticscholar.org/CorpusID:9078853.
Ashapure, A., Jung, J., Yeom, J., Chang, A., Maeda, M., Maeda, A., and Landivar, J. (2019). A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 49–64. https://doi.org/10.1016/j.isprsjprs.2019.04.003.
Avatade, S. S. and P, D. S. (2015). Irrigation system using a wireless sensor network and GPRS. IJARCCE, 4(5), 521–524. https://doi.org/10.17148/ijarcce.2015.45111.
Bulanon, D., Kataoka, T., Okamoto, H., and Hata, S. (2004). Development of a real-time machine vision system for the apple harvesting robot. Society of Instrument and Control Engineers of Japan, 1, 595–598. https://doi.org/10.11499/sicep.2004.0108_5.
Campos, E. V., Proença, P. L., Oliveira, J. L., Bakshi, M., Abhilash, P., and Fraceto, L. F. (2018). Use of botanical insecticides for sustainable agriculture: Future perspectives. Ecological Indicators, 105, 483–495. https://doi.org/10.1016/j.ecolind.2018.04.038.
Chang, A., Jung, J., Maeda, M. M., and Landivar, J. (2017). Crop height monitoring with digital imagery from unmanned aerial system (UAS). Computers and Electronics in Agriculture, 141, 232–237. https://doi.org/10.1016/j.compag.2017.07.008.
Chang, C. and Lin, K. (2018). Smart agricultural machine with a computer vision-based weeding and variable-rate irrigation scheme. Robotics, 7(3), 38. https://doi.org/10.3390/robotics7030038.
Das, U. (2018). Precision farming: A promising technology in horticulture: A review. International Journal of Pure & Applied Bioscience, 6(1), 1596–1606. https://doi.org/10.18782/2320-7051.3088.
Dawn, N., Ghosh, T., Ghosh, S., Saha, A., Mukherjee, P., Sarkar, S., Guha, S., and Sanyal, T. (2023). Implementation of artificial intelligence, machine learning, and internet of things (IoT) in revolutionizing agriculture: A review on recent trends and challenges. International Journal of Experimental Research and Review, 30, 190–218. https://doi.org/10.52756/ijerr.2023.v30.018.
Duan, B., Fang, S., Zhu, R., Wu, X., Wang, S., Gong, Y., and Peng, Y. (2019). Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis. Frontiers in Plant Science, 10, 204. https://doi.org/10.3389/fpls.2019.00204.
Evenson, R. E. and Gollin, D. (2003). Assessing the impact of the green revolution, 1960 to 2000. Science, 300(5620), 758–762. https://doi.org/10.1126/science.1078710.
FAO (2017). The future of food and agriculture: Trends and challenges. https://www.fao.org/3/i6583e/ i6583e.pdf.
Gandhi, M., Kamdar, J., and Shah, M. (2019). Preprocessing of non-symmetrical images for edge detection. Augmented Human Research, 5, 10. https://doi.org/10.1007/s41133-019-0030-5.
Gao, D., Sun, Q., Hu, B., and Zhang, S. (2020). A framework for agricultural pest and disease monitoring based on internet-of-things and unmanned aerial vehicles. Sensors, 20(5), 1487. https://doi.org/10.3390/s20051487.
Gašparović, M., Zrinjski, M., Barković, , and Radočaj, D. (2020). An automatic method for weed mapping in oat fields based on UAV imagery.Computers and Electronics in Agriculture, 173, 105385. https://doi.org/10.1016/j.compag.2020.105385.
Gracia-Romero, A., Kefauver, S. C., Fernandez-Gallego, J. A., Vergara-Díaz, O., Nieto-Taladriz, M. T., and Araus, J. L. (2019). UAV and ground image-based phenotyping: A proof of concept with durum wheat. Remote Sensing, 11(10), 1244. https://doi.org/10.3390/rs11101244.
Haghverdi, A., Yonts, C. D., Reichert, D. L., and Irmak, S. (2016). Impact of irrigation, surface residue cover and plant population on sugarbeet growth and yield, irrigation water use efficiency and soil water dynamics. Agricultural Water Management, 180, 1–12. https://doi.org/10.1016/j.agwat.2016.10.018.
Hoummaidi, L. E., Larabi, A., and Alam, K. (2021). Using unmanned aerial systems and deep learning for agriculture mapping in Dubai. Heliyon, 7(10), e08154. https://doi.org/10.1016/j.heliyon.2021.e08154.
Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., and Zhang, L. (2018). A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PLoS ONE, 13(4), e0196302. https://doi.org/10.1371/journal.pone.0196302.
Hunt, E. R., Hively, W. D., Fujikawa, S., Linden, D., Daughtry, C. S., and McCarty, G. (2010). Acquisition of nir-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing, 2(1), 290–305. https://doi.org/10.3390/rs2010290.
Jain, N., Awasthi, Y., and Jain, R. K. (2023). An IoT-based soil analysis system using optical sensors and multivariate regression. International Journal of Experimental Research and Review, 31(Spl Volume), 23–32. https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.003.
Jiménez-Brenes, F. M., López-Granados, F., De Castro, A. I., Torres-Sánchez, J., Serrano, N., and Peña, J. M. (2017). Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling. Plant Methods, 13, 55. https://doi.org/10.1186/s13007-017-0205-3.
Jung, J., Maeda, M., Chang, A., Landivar, J., Yeom, J., and McGinty, J. (2018). Unmanned aerial system assisted framework for the selection of high yielding cotton genotypes. Computers and Electronics in Agriculture, 152, 74–81. https://doi.org/10.1016/j.compag.2018.06.051.
Kundalia, K., Patel, Y., and Shah, M. (2019). Multi-label movie genre detection from a movie poster using knowledge transfer learning. Augmented Human Research, 5(1), 11. https://doi.org/10.1007/s41133-019-0029- y.
Kurtulmus, F., Lee, W. S., and Vardar, A. (2013). Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network. Precision Agriculture, 15(1), 57–79. https://doi.org/10.1007/s11119-013-9323-8.
Lemmon, H. (1986). Comax: An expert system for cotton crop management. Science, 233(4759), 29–33. https://doi.org/10.1126/science.233.4759.29.
Madli, R., Hebbar, S., Heddoori, V., and Prasad, G. V. (2016). Intelligent irrigation control system using wireless sensors and android application. https://api.semanticscholar.org/CorpusID:31222168.
Moeckel, T., Dayananda, S., Nidamanuri, R., Nautiyal, S., Hanumaiah, N., Buerkert, A., and Wachendorf, M. (2018). Estimation of vegetable crop parameter by multi-temporal UAV-borne images. Remote Sensing, 10(5), 805. https://doi.org/10.3390/rs10050805.
Mogili, U. R. and Deepak, B. B. V. L. (2018). Review on application of drone systems in precision agriculture. Procedia Computer Science, 133, 502–509. https://doi.org/10.1016/j.procs.2018.07.063.
Mu, Y., Fujii, Y., Takata, D., Zheng, B., Noshita, K., Honda, K., Ninomiya, S., and Guo, W. (2018). Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle. Horticulture Research, 5, 74. https://doi.org/10.1038/s41438-018-0097-z.
Mulla, D. J. (2012). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009.
Murugan, D., Garg, A., and Singh, D. (2017). Development of an adaptive approach for precision agriculture monitoring with drone and satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12), 5322–5328. https://doi.org/10.1109/jstars.2017.2746185.
Niu, Y., Zhang, L., Zhang, H., Han, W., and Peng, X. (2019). Estimating above-ground biomass of maize using features derived from UAV-based RGB imagery. Remote Sensing, 11(11), 1261. https://doi.org/10.3390/rs11111261.
Olson, D., Chatterjee, A., Franzen, D. W., and Day, S. S. (2019). Relationship of drone- based vegetation indices with corn and sugarbeet yields. Agronomy Journal, 111(5), 2545–2557. https://doi.org/10.2134/agronj2019.04.0260.
Onishi, Y., Yoshida, T., Kurita, H., Fukao, T., Arihara, H., and Iwai, A. (2019). An automated fruit harvesting robot by using deep learning. ROBOMECH Journal, 6(1). https://doi.org/10.1186/s40648-019-0141-2.
Pajares, G. (2015). Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 81(4), 281–330. https://doi.org/10.14358/pers.81.4.281.
Pingali, P. L. (2012). Green revolution: Impacts, limits, and the path ahead. Proceedings of the National Academy of Sciences, 109(31), 12302–12308. https://doi.org/10.1073/pnas.0912953109.
Pramanik, A., Sarkar, S., and Maiti, J. (2021a). A real-time video surveillance system for traffic pre-events detection. Accident Analysis & Prevention, 154(5), 106019. https://doi.org/10.1016/j.aap.2021.106019 .
Pramanik, M., Khanna, M., Singh, M., Singh, D., Sudhishri, S., Bhatia, A., and Ranjan, R. (2021b). Automation of soil moisture sensor-based basin irrigation system. Smart Agricultural Technology, 2, 100032. https://doi.org/10.1016/j.atech.2021.100032.
Qualls, R. J., Scott, J. M., and DeOreo, W. B. (2001). Soil moisture sensors for urban landscape irrigation: Effectiveness and reliability. JAWRA Journal of the American Water Resources Association, 37(3), 547–559. https://doi.org/10.1111/j.1752-1688.2001.tb05492.x.
Rakun, J., Stajnko, D., and Zazula, D. (2011). Detecting fruits in natural scenes by using spatial-frequency based texture analysis and multiview geometry. Computers and Electronics in Agriculture, 76(1), 80–88. https://doi.org/10.1016/j.compag.2011.01.007.
Salamí, E., Gallardo, A., Skorobogatov, G., and Barrado, C. (2019). On-the-fly olive tree counting using a UAS and cloud services. Remote Sensing, 11(3), 316. https://doi.org/10.3390/rs11030316.
Samborski, S. M., Tremblay, N., and Fallon, E. (2009). Strategies to make use of plant sensors- based diagnostic information for nitrogen recommendations. Agronomy Journal, 101(4), 800–816. https://doi.org/10.2134/agronj2008.0162rx.
Santesteban, L., Di Gennaro, S., Herrero-Langreo, A., Miranda, C., Royo, J., and Matese, A. (2016). High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management, 183, 49–59. https://doi.org/10.1016/j.agwat.2016.08.026.
Sarkar, S., Pramanik, A., Maiti, J., and Reniers, G. (2020). Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data. Safety science, 125, 104616. https://doi.org/10.1016/j.ssci.2020.104616 .
Sarkar, S., Raj, R., Sammangi, V., Maiti, J., and Pratihar, D. (2019a). An optimization-based decision tree approach for predicting slip-trip-fall accidents at work. Safety Science, 118, 57–69. https://doi.org/10.1016/j.ssci.2019.05.009 .
Sarkar, S., Sammangi, V., Raj, R., Maiti, J., and Mitra, P. (2019b). Application of optimized machine learning techniques for prediction of occupational accidents. Computers & Operations Research, 106, 210–224. https://doi.org/10.1016/j.cor.2018.02.021 .
Sarkar, S., Vinay, S., Djeddi, C., and Maiti, J. (2022). Classification and pattern extraction of incidents: a deep learning-based approach. Neural Computing and Applications, 34, 14253–14274. https://doi.org/10.1007/s00521-021-06780-3.
Sarron, J., Malézieux, , Sané, C. A. B., and Faye, (2018). Mango yield mapping at the orchard scale based on tree structure and land cover assessed by UAV. Remote Sensing, 10(12), 1900. https://doi.org/10.3390/rs10121900.
Scharf, P. C., Shannon, D. K., Palm, H. L., Sudduth, K. A., Drummond, S. T., Kitchen, N. R., Mueller, L. J., Hubbard, V. C., and Oliveira, L. F. (2011). Sensor-based nitrogen applications out-performed producer-chosen rates for corn in on-farm demonstrations. Agronomy Journal, 103(6), 1683–1691. https://doi.org/10.2134/agronj2011.0164.
Shakoor, N., Lee, S., and Mockler, T. C. (2017). High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current Opinion in Plant Biology, 38, 184–192. https://doi.org/10.1016/j.pbi.2017.05.006.
Singh, A., Ganapathysubramanian, B., Singh, A. K., and Sarkar, S. (2015). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21(2), 110–124. https://doi.org/10.1016/j.tplants.2015.10.015.
Slaughter, D., Giles, D., and Downey, D. (2007). Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61(1), 63–78. https://doi.org/10.1016/j.compag.2007.05.008.
Sripada, R. P., Schmidt, J. P., Dellinger, A. E., and Beegle, D. B. (2008). Evaluating multiple indices from a canopy reflectance sensor to estimate corn N requirements. Agronomy Journal, 100(6), 1553–1561. https://doi.org/10.2134/agronj2008.0017.
Swaminathan (2007). Can science and technology feed the world in 2025? Field Crops Research, 104(1–3), 3–9. https://doi.org/10.1016/j.fcr.2007.02.004.
Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., and Polasky, S. (2002). Agricultural sustainability and intensive production practices. Nature, 418(6898), 671–677. https://doi.org/10.1038/nature01014.
Verma, A., Agrawal, M., Gupta, K., Jamshed, A., Mishra, A., Khatter, H., Gupta, G., and Neupane, S. C. (2022). Plantosphere: Next generation adaptive and smart agriculture system. Journal of Sensors, 2022, 1–10. https://doi.org/10.1155/2022/5421312.
Yallappa, D., Veerangouda, M., Maski, D., Palled, V., and Bheemanna, M. (2017). Development and evaluation of drone mounted sprayer for pesticide applications to crops. In 2017 IEEE Global Humanitarian Technology Conference (GHTC), pages 1–7. https://doi.org/10.1109/GHTC.2017.8239330.
Yeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A., and Landivar, J. (2019). Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture. Remote Sensing, 11(13), 1548. https://doi.org/10.3390/rs11131548.
Zhang, J., Maleski, J., Jespersen, D., Waltz, F. C., Rains, G., and Schwartz, B. (2021). Unmanned aerial system- based weed mapping in sod production using a convolutional neural network. Frontiers in Plant Science, 12, 702626. https://doi.org/10.3389/fpls.2021.702626.
Zhou, X., Zheng, H., Xu, X., He, J., Ge, X., Yao, X., Cheng, T., Zhu, Y., Cao, W., and Tian, Y. (2017). Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 246–255. https://doi.org/10.1016/j.isprsjprs.2017.05.003.
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