Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 2842 2023-06-13 11:36:17 |
2 layout Meta information modification 2842 2023-06-14 05:31:05 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Yadav, A.; Yadav, K.; Ahmad, R.; Abd-Elsalam, K. Nanotechnology for Precision Agriculture. Encyclopedia. Available online: (accessed on 22 June 2024).
Yadav A, Yadav K, Ahmad R, Abd-Elsalam K. Nanotechnology for Precision Agriculture. Encyclopedia. Available at: Accessed June 22, 2024.
Yadav, Anurag, Kusum Yadav, Rumana Ahmad, Kamel Abd-Elsalam. "Nanotechnology for Precision Agriculture" Encyclopedia, (accessed June 22, 2024).
Yadav, A., Yadav, K., Ahmad, R., & Abd-Elsalam, K. (2023, June 13). Nanotechnology for Precision Agriculture. In Encyclopedia.
Yadav, Anurag, et al. "Nanotechnology for Precision Agriculture." Encyclopedia. Web. 13 June, 2023.
Nanotechnology for Precision Agriculture

By adopting nanotechnology-based precision agricultural practices, the farming community can reduce agrochemicals while maintaining high crop productivity, protecting soil and water health, and contributing to a cleaner environment.

nanotechnology precision agriculture nanobiosensors nanofertilizers agro-waste reduction

1. Nanobiosensors in Diagnostics and Precision Agriculture

The unprecedented increase in the use of agrochemicals and fertilizers has led to an accumulation of nutrients and toxins in ground and surface waters. These toxic concentrations are responsible for higher costs of water purification, reduced fisheries, and decreased recreational activities [1]. Conventional agricultural practices are deteriorating soil quality and are responsible for the eutrophication of water bodies. In addition, bad farming practices damage the ecosystems of beneficial insects and other wild organisms and, therefore, must be replaced by precision agricultural methods.
Precision agriculture includes wireless field networking and nanosensors for observing and controlling farming practices. It manages site-specific crops and pre- and post-harvesting aspects [2]. Under precision agriculture, exploring the fascinating properties of functional materials from which nanobiosensors are built could help accurately analyze soil humidity, water, nutrients, and phytopathogens [3] (Figure 1). Biosensors are now available for detecting odors in food spoilage, and such sensors [4] are called “electronic noses”, followed by the development of other sensor types. The electronic nose uses an array of gas sensors to identify various kinds of odors. The gas sensors are composed of NPs like ZnO nanowires [5] and nanorods [6], which could detect impurities in vapor mixtures [7]. Such sensors work on the principle of change in their resistance with the passage of different gases resulting in variation in the generated electrical signals, which are used as a fingerprint for gas detection. A typical biosensor consists of four units: (1) a sensor, (2) a signal conditioning block, (3) a microprocessor chip, and (4) a radio module for wireless communications between the sensor and the monitoring station [8].
Figure 1. Functional representation of nanosensors in precision agriculture.
Recent nanotechnological leaps have enabled us to study biochemical interactions in plant cells and tissues due to various pathogens. The method uses a probe inserted in the xylem vessel at the root base. The probe measures xylem pressure, radial electrical gradients, and ionic activity [9][10]. Such tools help better understand pathogenicity mechanisms to improve crop disease treatment strategies [11][12]. However, the previous approach relied on the destructive sampling of pathogenic bacteria colonizing the xylem, which failed to provide helpful information about colorization patterns, biofilm development, movement, and re-colonization of bacterial pathogens in new tissues. However, implementing microfabricated xylem vessels containing nano-sized features lets us understand the features that were impossible with conventional methods [13].

1.1. Monitoring of Soil Quality Parameters

Biomonitoring is a technique used to collect and analyze organisms, tissues, or fluids to determine their exposure to natural and synthetic chemicals. The information gleaned from these observations is valuable, as it provides insight into the number of chemicals that have entered the organism and led to corresponding changes. Biomonitoring is also an effective method for estimating the total dose absorbed by the organism, which can provide indirect access to monitor target site concentrations. The advancement of sensor technology has improved its sensitivity and reduced its size compared to conventional biosensors. Such biosensors are used to monitor fertilizers, herbicides, pesticides, insecticides, pathogens, soil moisture, and pH [14]. An ideal nanobiosensor should be stable over long storage periods and possess a lower reaction time. In addition, it should be small, biocompatible, non-toxic, non-antigenic, inexpensive, portable, accurate, and capable of producing repeatable findings [15]. Nanobiosensors are ultrasensitive devices and can detect viruses at ultra-low concentrations as they operate at the atomic scale with the highest efficiency and accuracy.

1.2. Monitoring Soil Pesticides/Herbicides

The insects are cosmopolitan in distribution and hold the highest population among pests. They infest all plants and products by injuring their parts or attack storage products to incur heavy crop losses. The regular use of pesticides in fields to combat pests can lead to the development of resistance among pest groups [16]. Additionally, pesticide chemicals degrade in the environment over time, which reduces their effectiveness for agricultural use. NM use in pesticide formulations could aid in reducing usage and attaining agricultural sustainability. NM includes C nanotubes, quantum dots, gold NPs, carbon black, and nanocomposites. Many nanostructured biosensors have been developed for pesticide detection in water and food [17]. Based on consumption rates, toxicological information, and environmental residual levels, the U.S. Environmental Protection Agency (EPA) proposed a limit of 0.9 mg/L glyphosate in drinking water and an acceptable daily intake of 0.3 mg/kg/day [18].

1.3. Monitoring Soil Nutrients

Nanosensors are being developed as a promising real-time technology for monitoring soil nutrients. These sensors detect and quantify nutrients such as nitrogen, phosphorus, and potassium in soil samples. They use nanomaterials, such as carbon nanotubes, graphene, and nanoclays, to detect and bind with specific nutrients in the soil [19]. Nanosensors can provide farmers with accurate and timely information about soil nutrient levels, which can help them make more informed decisions about fertilizer application and crop management. This technology can assist in decreasing fertilizer waste, boost fertilizer efficiency, and lessen the potentially negative environmental implications of typical fertilizer application methods. One example of a nanosensor for soil nutrient monitoring is a graphene-based sensor that can detect nitrogen levels in soil [20]. The sensor is designed to be integrated into a wireless sensor network that can provide real-time data on soil nutrient levels to farmers.

1.4. Monitoring Soil Humidity

To ensure successful crop production, it is necessary to regularly analyze soil texture and moisture content. Relative humidity measurements determine the amount of water vapor in a gas mixture at a specified temperature. Standard-level deviations in soil moisture can significantly impact agricultural yields since these parameters vary spatially and temporally. Although conventional methods are available for estimating soil moisture levels, their accuracy is often low. Such methods require frequent calibration, reducing their stability and making them less preferable for use in agricultural settings.
Humidity-based nanosensors are increasingly replacing conventional methods for measuring soil moisture [21]. These sensors utilize electrical transduction with a hygroscopic probe, which changes its dielectric properties upon water absorption. Nanosensors fabricated from polymers, ceramics, and composites provide several benefits, such as increased stability, prolonged chemical and thermal durability, and enhanced environmental adaptability [22]. The widespread use of nanosensors in agriculture could significantly improve the precision of soil temperature and moisture measurements. Many of these devices are equipped with wireless communications systems that are economical, user-friendly, and can provide real-time data. Examples of nanosensors commonly used for soil measurements include carbon nanotube and graphene-based nanosensors [23]. For instance, a graphene oxide-based sensor is a type of humidity-based nanosensor that can detect changes in humidity levels from 0.1% to 90% [24].

1.5. Monitoring Plant Disease and Stress

Plant stress and nutrient deficiency are detected by monitoring plant physiology through imaging, spectroscopy, and fluorescence [25][26]. The described remote sensing methods provide vital information about leaf area, chlorophyll content, stomatal conductance [27], transpiration rate [28], water potential [29], and leaf temperature [30]. However, the methods are not helpful for the early diagnosis of plant stress and nutrient deficiency and are not economical for installation in individual plants [25]. NPs-based sensors are now being utilized to monitor plant disease and stress by providing an early detection system for plants. These systems measure the volatile organic compounds (VOCs) released by plants during biotic and abiotic stress or disease conditions. Nanoparticle based sensors can detect these VOCs by analyzing their physical and chemical properties, allowing for the identification of the specific stress or disease affecting the plant.
One example of a nanoparticle-based system for plant disease detection is a gold nanoparticle-based sensor that can detect the presence of bacterial pathogens in plants [31]. The sensor works by detecting the VOCs released by the bacteria, allowing for early detection of the disease before visible symptoms appear. Similarly, NPs have been used to monitor abiotic stress in plants, such as drought stress, by detecting changes in VOC emissions. Carbon nanotubes have been utilized in a sensor that can detect changes in VOCs associated with drought stress in plants [32][33]. The sensor can detect VOCs with high sensitivity and specificity, allowing for early detection of drought stress in plants.

1.6. Monitoring Irrigation

Due to the uncertainties posed by climate change, land water availability has reduced globally, and droughts and erratic monsoon patterns are becoming more frequent [34]. The current decade is facing a challenge in getting clean and needed water for human use, industrial purposes, and agriculture. The escalating use of agrochemicals in agriculture has exacerbated groundwater pollution. Our water resources are getting contaminated with microbial pathogens, salts, metals, agrochemicals, pharmaceutical compounds, personal care products, and radioactive elements [8]. A specific type of contaminant in water bodies is primarily due to anthropogenic activities like oil and gas production, mining, or natural processes like leaching [35], which require thorough treatment procedures for water recycling. Water treatment requires novel and sustainable technologies for recycling purposes.
Precision and site-specific irrigation management have emerged as potential solutions to enhance crop productivity under adverse climate change conditions [36]. The concept has appeared as a possible solution for improved crop productivity under adverse climate change. The method uses advanced technologies such as GPS, GIS, and automated machine guidance to apply water judiciously. This approach can be complemented with low-flying drones or sensitive satellites with high-resolution imaging capabilities to determine the water content of soil or plants and induce precise irrigation at the site of need. As a result, water consumption for irrigation can be reduced. However, several bottlenecks, such as cloud interference and high data processing requirements, still need to be addressed. Integration of crop simulation models with remote sensing technology enhances the efficacy of agricultural management and decision-making processes. The application of nanotechnology to microirrigation can enhance water quality and filtering techniques. Nanoparticle-based biosensors can detect and measure water-based contaminants in real-time and remove them using nanofiltration membranes [37]. Nanoparticle-based membranes can also desalinate water, reducing the likelihood of clogging on the filters and membranes.

1.7. Summary of Biosensors in Precision Agriculture

The section discussed the role of nanobiosensors in diagnostics and precision agriculture. These sensors monitor soil parameters such as quality, pesticide/herbicide levels, nutrient content, and humidity. They also play a crucial role in monitoring plant disease and stress and managing irrigation. The summary of biosensors’ application in precision agriculture is also mentioned, highlighting their importance in achieving more efficient and sustainable farming practices (Table 1).
Table 1. Nanobiosensors in diagnostics and precision agriculture.
Type of Biosensors Function Material Type Reference
Environmental biosensors, chemiresistor sensors monitoring of soil quality parameters polymers, metal oxides [38]
Pesticide biosensors, electrochemical biosensors monitoring soil pesticides/herbicides enzymes, conducting polymers [39][40]
Nutrient biosensors, potentiometric biosensors monitoring soil nutrients ion-selective electrodes, polymers [41][42]
Moisture sensors, capacitive humidity sensors monitoring soil humidity ceramics, polymers [43][44]
Plant disease biosensors, fluorescence-based biosensors monitoring plant disease and stress quantum dots, fluorescent proteins [45][46]
Irrigation biosensors, soil moisture sensors monitoring irrigation ceramics, metal oxides [47][48]


  1. Mukhopadhyay, S. Nanotechnology in agriculture: Prospects and constraints. Nanotechnol. Sci. Appl. 2014, 7, 63–71.
  2. Burrell, J.; Brooke, T.; Beckwith, R. Sensor and actuator networks—Vineyard computing: Sensor networks in agricultural production. IEEE Pervasive Comput. 2004, 3, 38–45.
  3. Antonacci, A.; Arduini, F.; Moscone, D.; Palleschi, G.; Scognamiglio, V. Nanostructured (Bio)sensors for smart agriculture. TrAC Trends Anal. Chem. 2018, 98, 95–103.
  4. Compagnone, D.; McNeil, C.; Athey, D.; Di Ilio, C.; Guilbault, G. An amperometric NADH biosensor based on NADH oxidase from Thermus aquaticus. Enzym. Microb. Technol. 1995, 17, 472–476.
  5. Hossain, M.; Ghosh, S.; Boontongkong, Y.; Thanachayanont, C.; Dutta, J. Growth of Zinc Oxide Nanowires and Nanobelts for Gas Sensing Applications. J. Metastable Nanocrystalline Mater. 2005, 23, 27–30.
  6. Huang, H.; Lee, Y.C.; Tan, O.K.; Zhou, W.; Peng, N.; Zhang, Q. High sensitivity SnO2 single-nanorod sensors for the detection of H2 gas at low temperature. Nanotechnology 2009, 20, 115501.
  7. Ko, W.; Jung, N.; Lee, M.; Yun, M.; Jeon, S. Electronic Nose Based on Multipatterns of ZnO Nanorods on a Quartz Resonator with Remote Electrodes. ACS Nano 2013, 7, 6685–6690.
  8. Dasgupta, N.; Ranjan, S.; Ramalingam, C. Applications of nanotechnology in agriculture and water quality management. Environ. Chem. Lett. 2017, 15, 591–605.
  9. Wegner, L.H. Using the Multifunctional Xylem Probe for in situ Studies of Plant Water and Ion Relations Under Saline Conditions. Methods Mol. Biol. 2012, 913, 35–66.
  10. Bandyopadhyay, S.; Peralta-Videa, J.R.; Gardea-Torresdey, J.L. Advanced analytical techniques for the measurement of na-nomaterials in food and agricultural samples: A review. Environ. Eng. Sci. 2013, 30, 118–125.
  11. Cursino, L.; Li, Y.; Zaini, P.A.; De La Fuente, L.; Hoch, H.C.; Burr, T.J. Twitching motility and biofilm formation are associated with tonB1 in Xylella fastidiosa. FEMS Microbiol. Lett. 2009, 299, 193–199.
  12. Chen, H.; Yada, R. Nanotechnologies in agriculture: New tools for sustainable development. Trends Food Sci. Technol. 2011, 22, 585–594.
  13. Ditta, A. How helpful is nanotechnology in agriculture? Adv. Nat. Sci. Nanosci. Nanotechnol. 2012, 3, 033002.
  14. Omanović-Mikličanina, E.; Maksimović, M. Nanosensors applications in agriculture and food industry. Bull Chem. Technol. Bosnia. Herzegovina 2016, 47, 59–70.
  15. Huang, X.; Zhu, Y.; Kianfar, E. Nano biosensors: Properties, applications and electrochemical techniques. J. Mater. Res. Technol. 2021, 12, 1649–1672.
  16. Bhattacharyya, A.; Duraisamy, P.; Govindarajan, M.; Buhroo, A.A.; Prasad, R. Nano-biofungicides: Emerging trend in insect pest control. In Advances and Applications Through Fungal Nanobiotechnology; Springer: Cham, Switzerland, 2016; pp. 307–319.
  17. Karimi-Maleh, H.; Karimi, F.; Fu, L.; Sanati, A.L.; Alizadeh, M.; Karaman, C.; Orooji, Y. Cyanazine herbicide monitoring as a hazardous substance by a DNA nanostructure biosensor. J. Hazard. Mater. 2022, 423, 127058.
  18. Baer, K.N.; Marcel, B.J. Glyphosate. In Encyclopedia of Toxicology; Wexler, P., Ed.; Elsevier: San Diego, CA, USA, 2015; pp. 767–769.
  19. Kim, D.Y.; Kadam, A.; Shinde, S.; Saratale, R.G.; Patra, J.; Ghodake, G. Recent developments in nanotechnology transforming the agricultural sector: A transition replete with opportunities. J. Sci. Food Agric. 2018, 98, 849–864.
  20. Garland, N.T.; McLamore, E.S.; Cavallaro, N.D.; Mendivelso-Perez, D.; Smith, E.A.; Jing, D.; Claussen, J.C. Flexible Laser-Induced Graphene for Nitrogen Sensing in Soil. ACS Appl. Mater. Inter. 2018, 10, 39124–39133.
  21. Fiol, D.F.; Terrile, M.C.; Frik, J.; Mesas, F.A.; Álvarez, V.A.; Casalongué, C.A. Nanotechnology in plants: Recent advances and challenges. J. Chem. Technol. Biotechnol. 2021, 96, 2095–2108.
  22. Wang, F.; Jian, J.; Geng, X.; Gou, G.; Cui, W.; Cui, J.; Qiao, Y.; Fu, J.; Yang, Y.; Ren, T.-L. A miniaturized integrated SAW sensing system for relative humidity based on graphene oxide film. IEEE Sens. J. 2020, 20, 9733–9739.
  23. Mahdizadeh, M.; Najafi, N. Application of nano-sensors in the determination of soil moisture and temperature. Land Manag. J. 2019, 6, 169–178.
  24. Azzuhri, S.; Amiri, I.; Zulkhairi, A.; Salim, M.; Razak, M.; Khyasudeen, M.; Ahmad, H.; Zakaria, R.; Yupapin, P. Application of graphene oxide based Microfiber-Knot resonator for relative humidity sensing. Results Phys. 2018, 9, 1572–1577.
  25. Li, L.; Zhang, Q.; Huang, D. A Review of Imaging Techniques for Plant Phenotyping. Sensors 2014, 14, 20078–20111.
  26. Humplík, J.F.; Lazár, D.; Husičková, A.; Spíchal, L. Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses—A review. Plant Methods 2015, 11, 29.
  27. Leinonen, I.; Grant, O.M.; Tagliavia, C.P.P.; Chaves, M.M.; Jones, H. Estimating stomatal conductance with thermal imagery. Plant Cell Environ. 2006, 29, 1508–1518.
  28. Al-Tamimi, N.; Brien, C.; Oakey, H.; Berger, B.; Saade, S.; Ho, Y.S.; Schmöckel, S.M.; Tester, M.; Negrão, S. Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping. Nat. Commun. 2016, 7, 13342.
  29. Cohen, Y.; Alchanatis, V.; Meron, M.; Saranga, Y.; Tsipris, J. Estimation of leaf water potential by thermal imagery and spatial analysis. J. Exp. Bot. 2005, 56, 1843–1852.
  30. Munns, R.; James, R.; Sirault, X.; Furbank, R.; Jones, H. New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J. Exp. Bot. 2010, 61, 3499–3507.
  31. Hegde, M.; Pai, P.; Shetty, M.G.; Babitha, K.S. Gold nanoparticle based biosensors for rapid pathogen detection: A review. Environ. Nanotechnol. Monit. Manag. 2022, 18, 100756.
  32. Penza, M.; Cassano, G.; Aversa, P.; Antolini, F.; Cusano, A.; Consales, M.; Giordano, M.; Nicolais, L. Carbon nanotubes-coated multi-transducing sensors for VOCs detection. Sens. Actuators B Chem. 2005, 111–112, 171–180.
  33. Hafaiedh, I.; Elleuch, W.; Clement, P.; Llobet, E.; Abdelghani, A. Multi-walled carbon nanotubes for volatile organic compound detection. Sens. Actuators B Chem. 2013, 182, 344–350.
  34. Mehta, L.; Srivastava, S.; Adam, H.N.; Bose, S.; Ghosh, U.; Kumar, V.V. Climate change and uncertainty from ‘above’and ‘below’: Perspectives from India. Reg. Environ. Chang. 2019, 19, 1533–1547.
  35. Jasra, R.; Bajaj, H.; Mody, H. Clay as a versatile material for catalysts and adsorbents. Bull. Catal. Soc. India 1999, 9, 113–121.
  36. Cassman, K.G. Ecological intensification of cereal production systems: Yield potential, soil quality, and precision agriculture. Proc. Natl. Acad. Sci. USA 1999, 96, 5952–5959.
  37. Sharma, R.; Verma, N.; Lugani, Y.; Kumar, S.; Asadnia, M. Conventional and Advanced Techniques of Wastewater Monitoring and Treatment. In Green Sustainable Process for Chemical and Environmental Engineering and Science; Elsevier: Amsterdam, The Netherlands, 2021; pp. 1–48.
  38. John, A.T.; Murugappan, K.; Nisbet, D.R.; Tricoli, A. An Outlook of Recent Advances in Chemiresistive Sensor-Based Electronic Nose Systems for Food Quality and Environmental Monitoring. Sensors 2021, 21, 2271.
  39. Virutkar, P.D.; Mahajan, A.P.; Meshram, B.H.; Kondawar, S.B. Conductive polymer nanocomposite enzyme immobilized biosensor for pesticide detection. J. Mater. NanoScience 2019, 6, 7–12.
  40. Akdag, A.; Işık, M.; Göktaş, H. Conducting polymer-based electrochemical biosensor for the detection of acetylthiocholine and pesticide via acetylcholinesterase. Biotechnol. Appl. Biochem. 2021, 68, 1113–1119.
  41. Chen, M.; Zhang, M.; Wang, X.; Yang, Q.; Wang, M.; Liu, G.; Yao, L. An All-Solid-State Nitrate Ion-Selective Electrode with Nanohybrids Composite Films for In-Situ Soil Nutrient Monitoring. Sensors 2020, 20, 2270.
  42. Huang, S.-F.; Shih, W.-L.; Chen, Y.-Y.; Wu, Y.-M.; Chen, L.-C. Ion composition profiling and pattern recognition of vegetable sap using a solid-contact ion-selective electrode array. Biosens. Bioelectron. X 2021, 9, 100088.
  43. Chen, Z.; Lu, C. Humidity Sensors: A Review of Materials and Mechanisms. Sens. Lett. 2005, 3, 274–295.
  44. Hashim, A.; Al-Khafaji, Y.; Hadi, A. Synthesis and Characterization of Flexible Resistive Humidity Sensors Based on PVA/PEO/CuO Nanocomposites. Trans. Electr. Electron. Mater. 2019, 20, 530–536.
  45. Garrido-Maestu, A.; Azinheiro, S.; Carvalho, J.; Abalde-Cela, S.; Carbo-Argibay, E.; Diéguez, L.; Piotrowski, M.; Kolen’Ko, Y.; Prado, M. Combination of Microfluidic Loop-Mediated Isothermal Amplification with Gold Nanoparticles for Rapid Detection of Salmonella spp. in Food Samples. Front. Microbiol. 2017, 8, 2159.
  46. Fang, Y.; Ramasamy, R.P. Current and Prospective Methods for Plant Disease Detection. Biosensors 2015, 5, 537–561.
  47. Khasim, S.; Pasha, A.; Dastager, S.G.; Panneerselvam, C.; Hamdalla, T.A.; Al-Ghamdi, S.; Alfadhli, S.; Makandar, M.B.; Albalawi, J.B.; Darwish, A. Design and development of multi-functional graphitic carbon nitride heterostructures embedded with copper and iron oxide nanoparticles as versatile sensing platforms for environmental and agricultural applications. Ceram. Int. 2023, 49, 20688–20698.
  48. Kashyap, B.; Kumar, R. Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring. IEEE Access 2021, 9, 14095–14121.
  49. Dey, T.; Bhattacharjee, T.; Nag, P.; Ritika; Ghati, A.; Kuila, A. Valorization of agro-waste into value added products for sustainable development. Bioresour. Technol. Rep. 2021, 16, 100834.
  50. Javourez, U.; O’donohue, M.; Hamelin, L. Waste-to-nutrition: A review of current and emerging conversion pathways. Biotechnol. Adv. 2021, 53, 107857.
  51. Rai, S.; Solanki, M.K.; Anal, A.K.D.; Sagar, A.; Solanki, A.C.; Kashyap, B.K.; Pandey, A.K. Emerging frontiers of microbes as agro-waste recycler. In Waste to Energy: Prospects and Applications; Springer: Cham, Switzerland, 2020; pp. 3–27.
  52. Sadh, P.K.; Duhan, S.; Duhan, J.S. Agro-industrial wastes and their utilization using solid state fermentation: A review. Bioresour. Bioprocess. 2018, 5, 1.
  53. Liuzzi, S.; Rubino, C.; Stefanizzi, P.; Martellotta, F. The Agro-Waste Production in Selected EUSAIR Regions and Its Potential Use for Building Applications: A Review. Sustainability 2022, 14, 670.
  54. El-Ramady, H.; Brevik, E.C.; Bayoumi, Y.; Shalaby, T.A.; El-Mahrouk, M.E.; Taha, N.; Elbasiouny, H.; Elbehiry, F.; Amer, M.; Abdalla, N.; et al. An Overview of Agro-Waste Management in Light of the Water-Energy-Waste Nexus. Sustainability 2022, 14, 15717.
  55. Bala, S.; Garg, D.; Sridhar, K.; Inbaraj, B.S.; Singh, R.; Kamma, S.; Tripathi, M.; Sharma, M. Transformation of Agro-Waste into Value-Added Bioproducts and Bioactive Compounds: Micro/Nano Formu-lations and Application in the Agri-Food-Pharma Sector. Bioengineering 2023, 10, 152.
  56. Anal, A.K.; Sadiq, M.B.; Singh, M. Emerging trends in traceability techniques in food systems. In Food Traceability and Authenticity, 1st ed.; CRC Press: Boca Raton, FL, USA, 2017; pp. 66–89.
  57. Mosadegh Sedghy, B. Evolution of radio frequency identification (RFID) in agricultural cold chain monitoring: A literature review. J. Agric. Sci. 2018, 11, 43–58.
  58. Kuzma, J. Nanotechnology in animal production—Upstream assessment of applications. Livest. Sci. 2010, 130, 14–24.
  59. Caon, T.; Martelli, S.M.; Fakhouri, F.M. New Trends in the Food Industry: Application of Nanosensors in Food Packaging. In Nanobiosensors; Elsevier: Amsterdam, The Netherlands, 2017; pp. 773–804.
  60. Tongrod, N.; Tuantranont, A.; Kerdcharoen, T. Adoption of precision agriculture in vineyard. In Proceedings of the 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Chonburi, Thailand, 6–9 May 2009; IEEE: Piscataway, NJ, USA, 2009.
  61. Popp, J.; Griffin, T. Adoption trends of early adopters of precision farming in Arkansas. In Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MI, USA, 16–19 July 2000.
  62. Pivoto, D.; Waquil, P.D.; Talamini, E.; Finocchio, C.P.; Dalla Corte, V.F.; de Vargas Mores, G. Scientific development of smart farming technologies and their application in Brazil. Inf. Process. Agric. 2018, 5, 21–32.
  63. Li, J. Nanotechnology Based Cell-All Phone-Sensors for Extended Network Chemical Sensing. In Proceedings of the Electrochemical Society Meeting Abstracts 225, Orlando, FL, USA, 11–16 May 2014; The Electrochemical Society, Inc.: Pennington, NJ, USA, 2014; p. 456.
  64. Pongnumkul, S.; Chaovalit, P.; Surasvadi, N. Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research. J. Sens. 2015, 2015, 195308.
  65. Mu, T.; Wang, S.; Li, T.; Wang, B.; Ma, X.; Huang, B.; Zhu, L.; Guo, J. Detection of Pesticide Residues Using Nano-SERS Chip and a Smartphone-Based Raman Sensor. IEEE J. Sel. Top. Quantum Electron. 2018, 25, 5200206.
  66. Maksimović, M.; Omanović-Mikličanin, E. Green internet of things and green nanotechnology role in realizing smart and sustainable agriculture. In Proceedings of the VIII International Scientific Agriculture Symposium “AGROSYM 2017”, Jahorina, Bosnia and Herzegovina, 5–8 October 2017.
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , , ,
View Times: 598
Revisions: 2 times (View History)
Update Date: 14 Jun 2023
Video Production Service