Remote Sensing, Artificial Intelligence and Smart Agriculture Technology Trends of the Future

Year 2024, , 646 - 656, 15.11.2024

Hatice Dilaver , Kamil Fatih Dilaver

https://doi.org/10.47115/bsagriculture.1536744

Abstract

The efficient and sustainable operation of the agricultural sector has become increasingly important in light of the transformations brought about by the third and fourth industrial revolutions. Population growth, increasing food demand, rising input costs, and environmental pressures necessitate innovative approaches not only to ensure food security but also to mitigate the effects of climate change. The European Union (EU) emphasizes the role of digital technologies in supporting agricultural productivity and resilience by promoting a bio-based economy. Strategies such as Farm to Fork (F2F) initiative aim to reduce pesticide and nutrient inputs, thus preserving biodiversity and supporting ecosystem health. Artificial intelligence (AI) and predictive analytics, along with connected sensors, offer opportunities to optimize water and nutrient usage and increase crop yields. By utilizing AI, combining remote sensing technologies, and monitoring changes in land use, it is possible to reduce environmental risks associated with agricultural practices. Although there are challenges such as high investment costs and data control for the integration of digital technologies, ongoing research and development efforts promise to overcome these obstacles. In conclusion, the integration of digital technologies into agriculture presents unique opportunities to address urgent issues and achieve sustainability goals. This review discusses the applicability of fundamental technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Precision Agriculture (PA), and Machine Learning (ML) in making agriculture more efficient and sustainable, by enabling the perception, monitoring, collection, analysis, and extraction of meaningful insights from agricultural data.

Keywords

Artificial Intelligence, Machine learning, Internet of things, Agricultural remote sensing, Precision agriculture

References

• Abioye EA, Abidin MSZ, Mahmud MSA, Buyamin S, Ishak MHI, Rahman MK, Otuoze AO, Onotu P, Ramli MSA. 2020. A review on monitoring and advanced control strategies for precision irrigation. Comp Electron Agri, 173: 105441.
• Ahirwar S, Swarnkar R, Bhukya S, Namwade G. 2019. Application of drone in agriculture. Int J Curr Microbiol Appl Sci, 8(1): 2500-2505.
• Ahmad A, Ordoñez J, Cartujo P, Martos V. 2021. Remotely piloted aircraft (RPA) in agriculture: A pursuit of sustainability. Agronomy, 11(1): 7.
• Alreshidi E. 2019. Smart sustainable agriculture (SSA) solution underpinned by Internet of Things (IoT) and artificial intelligence (AI). Int J Adv Comp Sci Appl, 10(5): 93-102.
• An J, Li W, Li M, Cui S, Yue H. 2019. Identification and classification of maize drought stress using deep convolutional neural network. Symmetry, 11(2): 256.
• Apolo-Apolo OE, Martínez-Guanter J, Egea G, Raja P, Pérez-Ruiz M. 2020. Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. Eur J Agron, 115: 126030.
• Araújo SO, Peres RS, Barata J, Lidon F, Ramalho JC. 2021. Characterising the agriculture 4.0 landscape—emerging trends, challenges, and opportunities. Agronomy, 11(4): 667.
• Barrero O, Perdomo SA. 2018. RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Prec Agri, 19(5): 809-822.
• Berckmans D. 2014. Precision livestock farming technologies for welfare management in intensive livestock systems. Revue Sci Tech, 33: 189-196.
• Bhagat PR, Naz F, Magda R. 2022. Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis. PLoS One, 17(6): e0268989.
• Boursianis AD, Papadopoulou MS, Diamantoulakis P. Liopa-Tsakalidi A, Barouchas P. Salahas G, Karagiannidis G, Wan S, Goudos SK. 2022. Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18: 100187.
• Bu F, Wang X. 2019. A smart agriculture IoT system based on deep reinforcement learning. Future GenerComput Syst, 99: 500-507.
• Çakmakçı R, Salık MA, Çakmakçı S. 2023. Assessment and principles of environmentally sustainable food and agriculture systems. Agri, 13: 1073.
• Çakmakçı R. 2019. A review of biological fertilizers current use, new approaches, and future perspectives. Int J Innov Stud Sci Eng Technol, 5(7): 83-92.
• Castaldi F, Pelosi F, Pascucci S, Casa R. 2017. Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agri, 18(1): 76-94.
• Chang A, Jung J, Maeda MM, Landivar J. 2017. Crop height monitoring with digital imagery from unmanned aerial system (UAS). Comput Electronics Agri, 141: 232-237.
• Chen Q, Li L Chong C, Wang X. 2022. AI-enhanced soil management and smart farming. Soil Use Manag, 38(1): 7-13.
• Chen Y, Lee, WS, Gan H, Peres N, Fraisse C, Zhang Y, He Y. 2019. Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages. Remote Sens, 11(13): 1584.
• Cheng H, Damerow L, Sun Y, Blanke M. 2017. Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks. J Imaging, 3(1): 6.
• Cruz A, Ampatzidis Y, Pierro R, Materazzi A, Panattoni A, De Bellis, L. Luvisi A. 2019. Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence. Computers and Electronics in Agri, 157: 63–76.
• Das JV, Sharma S, Kaushik A. 2019. Views of Irish farmers on smart farming technologies: An observational study. Agri Eng, 1(2): 164-187.
• Dayıoğlu MA, Türker U. 2021. Digital transformation for sustainable future—agriculture 4.0: A review. J Agri Sci, 27(4): 373-399.
• DeChant C, Wiesner-Hanks T, Chen S, Stewart EL, Yosinski J, Gore MA, Nelson RJ, Lipson H. 2017. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology, 107: 1426-1432.
• Devi G, Sowmiya N, Yasoda K, Muthulakshmi K, Balasubramanian K. 2020. Review on application of drones for crop health monitoring and spraying pesticides and fertilizer. J Crit Rev, 7: 667-672.
• Dhanaraju M, Chenniappan P, Ramalingam K, Pazhanivelan S, Kaliaperumal R. 2022. Smart farming: Internet of things (IoT)-based sustainable agriculture. Agri, 12(10): 1745.
• Diwate S, Nitnaware V, Argulwar K. 2018. Design and development of application-specific drone machine for seed sowing. Int Res J Eng Technol, 5(5): 4003-4007.
• Elahi E, Weijun C, Zhang H, Nazeer M. 2019. Agricultural intensification and damages to human health in relation to agrochemicals: Application of artificial intelligence. Land Use Pol, 83: 461-474.
• Escalante HJ, Rodríguez-Sánchez S, Jiménez-Lizárraga M, Morales-Reyes A, Calleja JDL, Vázquez R. 2019. Barley yield and fertilization analysis from UAV imagery: A deep learning approach. Int J Remote Sens, 40(7): 2493-2516.
• Esgario JG, Krohling RA, Ventura JA. 2020. Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput Electron Agric, 169: 105162.
• Faiçal BS, Freitas H, Gomes PH, Mano LY, Pessin G, de Carvalho ACPFL, Krishnamachari B, Ueyama J. 2017. An adaptive approach for UAV-based pesticide spraying in dynamic environments. Comput Electron Agric, 138: 210-223.
• Ferreira B, Iten M, Silva RG. 2020. Monitoring sustainable development by means of earth observation data and machine learning: A review. Environ Sci Eur, 32: 120.
• Fu Z, Jiang J, Gao Y, Krienke B, Wang M, Zhong K, Cao Q, Tian Y, Zhu Y, Cao W, Liu X. 2020. Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sens, 12(3): 508.
• Fuentes A, Yoon S, Kim SC, Park DS. 2017. A robust deep-learning-based detector for real-time tomato plant diseases and pests’ recognition. Sensors, 17: 2022.
• García R, Aguilar J, Toro M, Pinto A, Rodríguez P. 2020. A systematic literature review on the use of machine learning in precision livestock farming. Comput Electron Agric, 179: 105826.
• Goedde L, Katz J, Ménard A, Revellat J. 2020. Agriculture’s connected future: How technology can yield new growth. McKinsey and Company. URL= https://www.mckinsey.com/industries/agriculture (accessed date: August 22, 2024).
• Gómez JE, Marcillo FR, Triana FL, Gallo VT, Oviedo BW, Hernández VL. 2017. IoT for environmental variables in urban areas. Procedia Comput Sci, 109: 67-74.
• Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J. 2018. Recent advances in convolutional neural networks. Pattern Recognit, 77: 354-377.
• Hassan MA, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, Xiao Y, He Z. 2019. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Sci, 282: 95-103.
• Hemming S, de Zwart F, Elings A, Righini I, Petropoulou A. 2019. Remote control of greenhouse vegetable production with artificial intelligence—greenhouse climate, irrigation, and crop production. Sensors, 19(8): 1807.
• Hodgson JC, Mott R, Baylis SM, Pham TT, Wotherspoon S, Kilpatrick AD, Segaran RR, Reid L, Terauds A, Koh LP. 2018. Drones count wildlife more accurately and precisely than humans. Methods Ecol Evol, 9(5): 1160-1167.
• Huang H, Deng J, Lan Y, Yang A, Deng X, Zhang L. 2018. A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PLoS One, 13(4): e0196302.
• Ivushkin K, Bartholomeus H, Bregt AK, Pulatov A, Franceschini MHD, Kramer H, van Loo EN, Roman VJ, Finkers R. 2019. UAV based soil salinity assessment of cropland. Geoderma, 338: 502-512. Javaid M, Haleem A, Singh RP, Suman R. 2022. Enhancing smart farming through the applications of Agriculture 4.0 technologies. Int J Intell Networks, 3: 150-164.
• Ji J, Zhu X, Ma H, Wang H, Jin X, Zhao K. 2021. Apple fruit recognition based on a deep learning algorithm using an improved lightweight network. Appl Eng Agric, 37(1): 123-134.
• Jung J, Maeda M, Chang A, Bhandari M, Ashapure A, Landivar-Bowles J. 2021. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Curr Opin Biotechnol, 70: 15-22.
• Kale SS, Patil PS. 2019. Data mining technology with fuzzy logic, neural networks and machine learning for agriculture. In: Balas V, Sharma N, Chakrabarti A, editors. Data Management, Analytics and Innovation. Springer, Singapore, pp: 79-87.
• Kamilaris A, Prenafeta-Boldú FX. 2018. Deep learning in agriculture: A survey. Comput Electron Agri, 147: 70-90.
• Kavianand M, Nivas VM, Kiruthika R, Lalitha S. 2016. Smart drip irrigation system for sustainable agriculture. In: 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Chennai, India, pp: 19-22.
• Keswani B, Mohapatra AG, Mohanty A, Khanna A, Rodrigues JJPC, Gupta D, de Albuquerque VHC. 2019. Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Comput Appl, 31: 277-292.
• Liu Y, Zhang L, Zhou H, Liu S. 2021. Monitoring crop diseases using a deep learning model with UAV imagery. Remote Sens, 13(15): 2936.
• Mizukami S, Saito Y, Nakayama A, Nagaoka T. 2019. Real-time monitoring of crop conditions using an integrated UAV and satellite system. Comput Electron Agric, 162: 248-258.
• Park D, Jung J, Kim S. 2020. Advances in precision agriculture using remote sensing and machine learning techniques. Int J Appl Earth Obs Geoinf, 89: 102072.