Arseam

International Journal of Advances in Engineering & Scientific Research

SENSOR BASED TECHNOLOGY DEVELOPMENT: ADVANCEMENTS IN PRECISION AGRICULTURE

Sanjoy Kumar Bordolui

Sanjoy Kumar Bordolui

Department of Seed Science and Technology, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal, India ORCID: https://orcid.org/0000-0003-2087-2968

DOI : https://doi.org/10.53882/IJAESR.2023.1001004 Page No : 50-64

Published Online : 2024-06-30

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Advanced sensor technology integration in agriculture has become critical in reaction to rising worldwide need for food and environmental issues. In order to revolutionize agricultural methods for increased sustainability, efficiency, and productivity, this paper thoroughly examines cutting-edge sensor-based technology like machine learning algorithms, remote sensing, and Internet of Things applications.

Objective: This investigation examines a variety among sensors employed in precision farming, including sensors for crop health, light, humidity, soil, and weather. The effects of these sensors on farming operations are analyzed, as well as the challenges in implementing them.

Findings: This study highlights the crucial role that sensor networks with an Internet of Things foundation play in making it possible to acquire data in real time and, consequently, effective making choices. Emerging technology such as wireless communication protocols and the narrowband Internet of things such as Bluetooth, Wi-Fi, ZigBee, and fifth-generation (5G) are explored for data exchange in intelligent.

Pratical Implications: The document emphasizes the importance of compatibility across different sensing technology and offers a comprehensive examination of data management and analytics strategies suitable for the large amounts of data produced through these mechanisms.

Originality: In its examination of possible future paths, the report emphasizes how 5G and AI-powered predictive modeling could improve sensor capabilities and speed up data processing systems. Potential solutions and mitigation strategies are suggested, along with a detailed discussion of the difficulties in implementing these technologies based on sensors, including cost, energy management, system compatibility, and data privacy.

Key words: Precision Agriculture, Internet of Things, Sensor Technology, Data Analytics, Sustainable Farming.

REFRENCES

Anand, T., Sinha, S., Mandal, M., Chamola, V. & Yu, F.R. (2021). AgriSegNet: deep aerial semantic segmentation framework for IoT-assisted precision agriculture. IEEE Sensor. J., 21 (16) (2021), pp. 17581-17590, DOI: https://ui.adsabs.harvard.edu/link_gateway/2021ISenJ..2117581A/doi:10.1109/JSEN.2021.3071290
Anbumozhi, A., & Shanthini, A. (2023, January 1). Adoption of Novel Technologies to Boost Precision Agriculture (BPA) using Internet of Things (IOT). EDP Sciences, 56, 05019-05019. DOI: https://doi.org/10.1051/itmconf/20235605019
Antolini, L. S., Scare, R. F. & Dias, A. (2015). Adoption of precision agriculture technologies by farmers: a systematic literature review and proposition of an integrated conceptual framework. Retrieved From: https://tapipedia.org/content/adoption-precision-agriculture-technologies-farmers-systematic-literature-review-and
Barnes, A., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B. & GómezBarbero, M. (2019). Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy, 80, 163-174. DOI: https://doi.org/10.1016/j.landusepol.2018.10.004
Bordolui, S. K., Chattopadhyay, P. & Chandra, P. (2006). Comparative performance of low land indigenous rice genotypes in Gangetic Alluvial Zone. J. Crop and Weed, 2: 33-36. Retrieved From: https://www.cropandweed.com/vol2issue1/pdf2005/10.pdf
Bordolui, S. K., Sadhukhan, R. & Chattopadhyay, P. (2015). Participatory evaluation of some folk rice genotypes. J. Crop and Weed, 11(2):59-62. Retrieved From: https://www.cabidigitallibrary.org/doi/pdf/10.5555/20163364420
European Parliament. (2014). Precision agriculture: an opportunity for farmers -potential support with the cap 2014-2020.
Gagliardi, G., Lupia, M., Cario, G., Gaccio, F C., D’Angelo, V., Cosma, A I M., & Casavola, A. (2021, October 26). An Internet of Things Solution for Smart Agriculture. Multidisciplinary Digital Publishing Institute, 11(11), 2140-2140. DOI: https://doi.org/10.3390/agronomy11112140
Giustarini, L., Lamprecht, S., Retzlaff, R., Udelhoven, T., Rossellò, N.B., Garone, E. & Gasparri, A. (2022). PANTHEON: SCADA for precision agriculture. Handbook of Real-Time Computing, Springer Nature Singapore, Singapore (2022), pp. 641-679, https://doi.org/10.1007/978-981-287-251-7_42
Haneklaus, S., Lilienthal, H. & Schnug, E. (2016). 25 years precision agriculture in Germany–a retrospective. 13th International Conference on Precision Agriculture. Retrieved From: https://www.researchgate.net/publication/305317531_25_years_Precision_Agriculture_in_Germany_-_a_retrospective
Islam, Z., Bagchi, B. & Hossain, M. (2007). Adoption of leaf color chart for nitrogen use efficiency in rice: Impact assessment of a farmer-participatory experiment in West Bengal, India. Field Crops Research, 70-75. DOI: https://doi.org/doi.org/10.1016/j.fcr.2007.04.012
Jain, S., Bhujel, S., Shrivastava, U., Mohapatra, A. & Mishra, G. (2023). Advancements in drone technology for fruit crop management: a comprehensive review. Int. J. Envir. Clim. Change, 13 (11, pp. 4367-4378, DOI: https://doi.org/10.9734/ijecc/2023/v13i113617
Karunathilake, E M B M., Le, A T., Heo, S., Chung, Y S., & Mansoor, S. (2023, August 11). The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture. Multidisciplinary Digital Publishing Institute, 13(8), 1593-1593. DOI: https://doi.org/10.3390/agriculture13081593
Khardia, N., Sharma, S. & Kumawat, H. (2022). Precision farming. A Monthly Peer Reviewed Magazine for Agriculture and Allied Sciences, 63, https://doi.org/10.60151/envec/zblx2641
Khosla, R. (2010). Precision agriculture: challenges and opportunities in a flat world. Brisbane: International Union of Soil Sciences (IUSS), c/o Institut für Bodenforschung, Universität für Bodenkultur. Retrieved: https://old.iuss.org/19th%20WCSS/Symposium/pdf/0779.pdf
Kritikos, M. (2017). Precision agriculture in Europe: Legal, social and ethical considerations. Scientific Foresight Unit (STOA). DOI: https://doi.org/10.2861/278
Maheswari, R., Ashok, K. R. & Prahadeeswaran, M. (2008). Precision Farming Technology, Adoption Decisions and Productivity of Vegetables in Resource-Poor Environments. Agricultural Economics Research Review, 415-424. Retrieved From: https://ageconsearch.umn.edu/record/47892/?ln=en&v=pdf
Mavridou, E., Vrochidou, E., Papakostas, G.A., Pachidis, T. & Kaburlasos, V.G. (2019). Machine vision systems in precision agriculture for crop farming. J. Imaging, 5 (12), p. 89, 10.3390/jimaging5120089 DOI: https://doi.org/10.3390/jimaging5120089
Mcbratney, A. B., Whelan, B. M., Ancev, T., & Bouma, J. (2005). Future Directions of Precision Agriculture. Precision Agriculture, 6(1), 7-23. DOI: https://doi.org/10.1007/s11119-005- 0681-8
Mitchell, S., Weersink, A., & Erickson, B. (2018). Adoption of Precision Agriculture Technologies in Ontario Crop Production. Canadian Journal of Plant Science, 1-5.DOI: https://doi.org/10.1139/CJPS-2017-0342
Mordor Intelligence. (2018). PRECISION FARMING MARKET - GROWTH, TRENDS, AND FORECASTS (2019 - 2024). Mordor Intelligence.
Narvaez, F.Y., Reina, G., Torres-Torriti, M., Kantor, G. & Cheein, F.A. (2017). A survey of ranging and imaging techniques for precision agriculture phenotyping. IEEE ASME Trans. Mechatron., 22 (6), pp. 2428-2439, DOI: https://doi.org/10.1109/TMECH.2017.2760866
NAS. (1997). Precision Agriculture in the 21st Century: Geospatial and Information Technologies in Crop Management. Washinton, D.C: National Academies. Retrieved From: https://nap.nationalacademies.org/catalog/5491/precision-agriculture-in-the-21st-century-geospatial-and-information-technologies
Paustian, M. & Theuvsen, L. (2017). Adoption of precision agriculture technologies by German crop farmers. Precision Agriculture, 18, 701-716. DOI: https://doi.org/10.1007/s11119-016-9482-5
Pedersen, S. & Lind, K. (2017). Precision Agriculture – From Mapping to Site-Specifc Application. În S. M. Pedersen, and K. M. Lind (Ed.), Precision Agriculture: Technology and Economic Perspectives (pg. 1-19). Cham, Switzerland: Springer International Publishing AG. DOI: http://dx.doi.org/10.1007/978-3-319-68715-5_1
POST (2015). Precision Farming. London: The Parliamentary Office of Science and Technology.
Rosell- Polo, J. R., Cheein, F.A., Gregorio, E., Andújar, D., Puigdomènech, L., Masip, J. and Escolà, A. (2015). Advances in structured light sensors applications in precision agriculture and livestock farming. Adv. Agron., 133, pp. 71-112, Retrieved From: https://researchportal.hw.ac.uk/en/publications/advances-in-structured-light-sensors-applications-in-precision-ag
Say, S.M., Keskin, M., Sehri, M. & Sekerli, Y.E. (2018). Adoption of precision agriculture technologies in developed and developing countries. The Online J. Sci. Technol- January, 8 (1), pp. 7-15, 10.1080/00103624.2021.1900862
Schrijver, R., Poppe, K. & Daheim, C. (2016). Precision agriculture and the future of farming in Europe. Brussels: European Parliament. DOI: http://dx.doi.org/10.2861/020809
Statista (2018). The market value of smart agriculture worldwide 2016-2025. Statista.
Tey, Y. S. & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precision Agriculture, 13, 713– 730. DOI : http://dx.doi.org/10.1007/s11119-012-9273-6
Ullo, S L., & Sinha, G R. (2021, July 1). Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications. Multidisciplinary Digital Publishing Institute, 13(13), 2585-2585. DOI: https://doi.org/10.3390/rs13132585
Vidoni, R., Gallo, R., Ristorto, G., Carabin, G., Mazzetto, F., Scalera, L. & Gasparetto, A. (2017). ByeLab: an agricultural mobile robot prototype for proximal sensing and precision farming. ASME International Mechanical Engineering Congress and Exposition, vol. 58370, American Society of Mechanical Engineers, Article V04AT05A057, DOI: http://dx.doi.org/10.1115/IMECE2017-71216
Whelan, B. & Taylor, J. (2013). Precision Agriculture for Grain Production Systems (ed. 1st Edition). Australia: Csiro Publishing. Retrieved From: https://catalogue.nla.gov.au/catalog/6104025
World Bank (2017). Agriculture, forestry, and fishing, value added (% of GDP). The World Bank.
Zhou, X., English, B. C., Larson, J. A., Lambert, D. M., Roberts, R. K., Boyer, C. N. & Martin, S. W. (2017). Precision Farming Adoption Trends in the Southern U.S. The Journal of Cotton Science, 21(2), 143–155.

Sanjay K (2024), “Sensor Based Technology Development: Advancements In Precision Agriculture”, International Journal of Advances in Engineering & Scientific Research, Volume 11, Issue 1, 2024, pp 50-64. DOI : https://doi.org/10.53882/IJAESR.2023.1001004

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REFRENCES

Anand, T., Sinha, S., Mandal, M., Chamola, V. & Yu, F.R. (2021). AgriSegNet: deep aerial semantic segmentation framework for IoT-assisted precision agriculture. IEEE Sensor. J., 21 (16) (2021), pp. 17581-17590, DOI: https://ui.adsabs.harvard.edu/link_gateway/2021ISenJ..2117581A/doi:10.1109/JSEN.2021.3071290
Anbumozhi, A., & Shanthini, A. (2023, January 1). Adoption of Novel Technologies to Boost Precision Agriculture (BPA) using Internet of Things (IOT). EDP Sciences, 56, 05019-05019. DOI: https://doi.org/10.1051/itmconf/20235605019
Antolini, L. S., Scare, R. F. & Dias, A. (2015). Adoption of precision agriculture technologies by farmers: a systematic literature review and proposition of an integrated conceptual framework. Retrieved From: https://tapipedia.org/content/adoption-precision-agriculture-technologies-farmers-systematic-literature-review-and
Barnes, A., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B. & GómezBarbero, M. (2019). Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy, 80, 163-174. DOI: https://doi.org/10.1016/j.landusepol.2018.10.004
Bordolui, S. K., Chattopadhyay, P. & Chandra, P. (2006). Comparative performance of low land indigenous rice genotypes in Gangetic Alluvial Zone. J. Crop and Weed, 2: 33-36. Retrieved From: https://www.cropandweed.com/vol2issue1/pdf2005/10.pdf
Bordolui, S. K., Sadhukhan, R. & Chattopadhyay, P. (2015). Participatory evaluation of some folk rice genotypes. J. Crop and Weed, 11(2):59-62. Retrieved From: https://www.cabidigitallibrary.org/doi/pdf/10.5555/20163364420
European Parliament. (2014). Precision agriculture: an opportunity for farmers -potential support with the cap 2014-2020.
Gagliardi, G., Lupia, M., Cario, G., Gaccio, F C., D’Angelo, V., Cosma, A I M., & Casavola, A. (2021, October 26). An Internet of Things Solution for Smart Agriculture. Multidisciplinary Digital Publishing Institute, 11(11), 2140-2140. DOI: https://doi.org/10.3390/agronomy11112140
Giustarini, L., Lamprecht, S., Retzlaff, R., Udelhoven, T., Rossellò, N.B., Garone, E. & Gasparri, A. (2022). PANTHEON: SCADA for precision agriculture. Handbook of Real-Time Computing, Springer Nature Singapore, Singapore (2022), pp. 641-679, https://doi.org/10.1007/978-981-287-251-7_42
Haneklaus, S., Lilienthal, H. & Schnug, E. (2016). 25 years precision agriculture in Germany–a retrospective. 13th International Conference on Precision Agriculture. Retrieved From: https://www.researchgate.net/publication/305317531_25_years_Precision_Agriculture_in_Germany_-_a_retrospective
Islam, Z., Bagchi, B. & Hossain, M. (2007). Adoption of leaf color chart for nitrogen use efficiency in rice: Impact assessment of a farmer-participatory experiment in West Bengal, India. Field Crops Research, 70-75. DOI: https://doi.org/doi.org/10.1016/j.fcr.2007.04.012
Jain, S., Bhujel, S., Shrivastava, U., Mohapatra, A. & Mishra, G. (2023). Advancements in drone technology for fruit crop management: a comprehensive review. Int. J. Envir. Clim. Change, 13 (11, pp. 4367-4378, DOI: https://doi.org/10.9734/ijecc/2023/v13i113617
Karunathilake, E M B M., Le, A T., Heo, S., Chung, Y S., & Mansoor, S. (2023, August 11). The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture. Multidisciplinary Digital Publishing Institute, 13(8), 1593-1593. DOI: https://doi.org/10.3390/agriculture13081593
Khardia, N., Sharma, S. & Kumawat, H. (2022). Precision farming. A Monthly Peer Reviewed Magazine for Agriculture and Allied Sciences, 63, https://doi.org/10.60151/envec/zblx2641
Khosla, R. (2010). Precision agriculture: challenges and opportunities in a flat world. Brisbane: International Union of Soil Sciences (IUSS), c/o Institut für Bodenforschung, Universität für Bodenkultur. Retrieved: https://old.iuss.org/19th%20WCSS/Symposium/pdf/0779.pdf
Kritikos, M. (2017). Precision agriculture in Europe: Legal, social and ethical considerations. Scientific Foresight Unit (STOA). DOI: https://doi.org/10.2861/278
Maheswari, R., Ashok, K. R. & Prahadeeswaran, M. (2008). Precision Farming Technology, Adoption Decisions and Productivity of Vegetables in Resource-Poor Environments. Agricultural Economics Research Review, 415-424. Retrieved From: https://ageconsearch.umn.edu/record/47892/?ln=en&v=pdf
Mavridou, E., Vrochidou, E., Papakostas, G.A., Pachidis, T. & Kaburlasos, V.G. (2019). Machine vision systems in precision agriculture for crop farming. J. Imaging, 5 (12), p. 89, 10.3390/jimaging5120089 DOI: https://doi.org/10.3390/jimaging5120089
Mcbratney, A. B., Whelan, B. M., Ancev, T., & Bouma, J. (2005). Future Directions of Precision Agriculture. Precision Agriculture, 6(1), 7-23. DOI: https://doi.org/10.1007/s11119-005- 0681-8
Mitchell, S., Weersink, A., & Erickson, B. (2018). Adoption of Precision Agriculture Technologies in Ontario Crop Production. Canadian Journal of Plant Science, 1-5.DOI: https://doi.org/10.1139/CJPS-2017-0342
Mordor Intelligence. (2018). PRECISION FARMING MARKET - GROWTH, TRENDS, AND FORECASTS (2019 - 2024). Mordor Intelligence.
Narvaez, F.Y., Reina, G., Torres-Torriti, M., Kantor, G. & Cheein, F.A. (2017). A survey of ranging and imaging techniques for precision agriculture phenotyping. IEEE ASME Trans. Mechatron., 22 (6), pp. 2428-2439, DOI: https://doi.org/10.1109/TMECH.2017.2760866
NAS. (1997). Precision Agriculture in the 21st Century: Geospatial and Information Technologies in Crop Management. Washinton, D.C: National Academies. Retrieved From: https://nap.nationalacademies.org/catalog/5491/precision-agriculture-in-the-21st-century-geospatial-and-information-technologies
Paustian, M. & Theuvsen, L. (2017). Adoption of precision agriculture technologies by German crop farmers. Precision Agriculture, 18, 701-716. DOI: https://doi.org/10.1007/s11119-016-9482-5
Pedersen, S. & Lind, K. (2017). Precision Agriculture – From Mapping to Site-Specifc Application. În S. M. Pedersen, and K. M. Lind (Ed.), Precision Agriculture: Technology and Economic Perspectives (pg. 1-19). Cham, Switzerland: Springer International Publishing AG. DOI: http://dx.doi.org/10.1007/978-3-319-68715-5_1
POST (2015). Precision Farming. London: The Parliamentary Office of Science and Technology.
Rosell- Polo, J. R., Cheein, F.A., Gregorio, E., Andújar, D., Puigdomènech, L., Masip, J. and Escolà, A. (2015). Advances in structured light sensors applications in precision agriculture and livestock farming. Adv. Agron., 133, pp. 71-112, Retrieved From: https://researchportal.hw.ac.uk/en/publications/advances-in-structured-light-sensors-applications-in-precision-ag
Say, S.M., Keskin, M., Sehri, M. & Sekerli, Y.E. (2018). Adoption of precision agriculture technologies in developed and developing countries. The Online J. Sci. Technol- January, 8 (1), pp. 7-15, 10.1080/00103624.2021.1900862
Schrijver, R., Poppe, K. & Daheim, C. (2016). Precision agriculture and the future of farming in Europe. Brussels: European Parliament. DOI: http://dx.doi.org/10.2861/020809
Statista (2018). The market value of smart agriculture worldwide 2016-2025. Statista.
Tey, Y. S. & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precision Agriculture, 13, 713– 730. DOI : http://dx.doi.org/10.1007/s11119-012-9273-6
Ullo, S L., & Sinha, G R. (2021, July 1). Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications. Multidisciplinary Digital Publishing Institute, 13(13), 2585-2585. DOI: https://doi.org/10.3390/rs13132585
Vidoni, R., Gallo, R., Ristorto, G., Carabin, G., Mazzetto, F., Scalera, L. & Gasparetto, A. (2017). ByeLab: an agricultural mobile robot prototype for proximal sensing and precision farming. ASME International Mechanical Engineering Congress and Exposition, vol. 58370, American Society of Mechanical Engineers, Article V04AT05A057, DOI: http://dx.doi.org/10.1115/IMECE2017-71216
Whelan, B. & Taylor, J. (2013). Precision Agriculture for Grain Production Systems (ed. 1st Edition). Australia: Csiro Publishing. Retrieved From: https://catalogue.nla.gov.au/catalog/6104025
World Bank (2017). Agriculture, forestry, and fishing, value added (% of GDP). The World Bank.
Zhou, X., English, B. C., Larson, J. A., Lambert, D. M., Roberts, R. K., Boyer, C. N. & Martin, S. W. (2017). Precision Farming Adoption Trends in the Southern U.S. The Journal of Cotton Science, 21(2), 143–155.

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