11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Glyphosate Separating and Sensing for Precision Agriculture and Environmental Protection in the Era of Smart Materials

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The present article critically and comprehensively reviews the most recent reports on smart sensors for determining glyphosate (GLP), an active agent of GLP-based herbicides (GBHs) traditionally used in agriculture over the past decades. Commercialized in 1974, GBHs have now reached 350 million hectares of crops in over 140 countries with an annual turnover of 11 billion USD worldwide. However, rolling exploitation of GLP and GBHs in the last decades has led to environmental pollution, animal intoxication, bacterial resistance, and sustained occupational exposure of the herbicide of farm and companies’ workers. Intoxication with these herbicides dysregulates the microbiome-gut-brain axis, cholinergic neurotransmission, and endocrine system, causing paralytic ileus, hyperkalemia, oliguria, pulmonary edema, and cardiogenic shock. Precision agriculture, i.e., an (information technology)-enhanced approach to crop management, including a site-specific determination of agrochemicals, derives from the benefits of smart materials (SMs), data science, and nanosensors. Those typically feature fluorescent molecularly imprinted polymers or immunochemical aptamer artificial receptors integrated with electrochemical transducers. Fabricated as portable or wearable lab-on-chips, smartphones, and soft robotics and connected with SM-based devices that provide machine learning algorithms and online databases, they integrate, process, analyze, and interpret massive amounts of spatiotemporal data in a user-friendly and decision-making manner. Exploited for the ultrasensitive determination of toxins, including GLP, they will become practical tools in farmlands and point-of-care testing. Expectedly, smart sensors can be used for personalized diagnostics, real-time water, food, soil, and air quality monitoring, site-specific herbicide management, and crop control.

          Related collections

          Most cited references247

          • Record: found
          • Abstract: found
          • Article: not found

          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Global food demand and the sustainable intensification of agriculture.

            Global food demand is increasing rapidly, as are the environmental impacts of agricultural expansion. Here, we project global demand for crop production in 2050 and evaluate the environmental impacts of alternative ways that this demand might be met. We find that per capita demand for crops, when measured as caloric or protein content of all crops combined, has been a similarly increasing function of per capita real income since 1960. This relationship forecasts a 100-110% increase in global crop demand from 2005 to 2050. Quantitative assessments show that the environmental impacts of meeting this demand depend on how global agriculture expands. If current trends of greater agricultural intensification in richer nations and greater land clearing (extensification) in poorer nations were to continue, ~1 billion ha of land would be cleared globally by 2050, with CO(2)-C equivalent greenhouse gas emissions reaching ~3 Gt y(-1) and N use ~250 Mt y(-1) by then. In contrast, if 2050 crop demand was met by moderate intensification focused on existing croplands of underyielding nations, adaptation and transfer of high-yielding technologies to these croplands, and global technological improvements, our analyses forecast land clearing of only ~0.2 billion ha, greenhouse gas emissions of ~1 Gt y(-1), and global N use of ~225 Mt y(-1). Efficient management practices could substantially lower nitrogen use. Attainment of high yields on existing croplands of underyielding nations is of great importance if global crop demand is to be met with minimal environmental impacts.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Object based image analysis for remote sensing

                Bookmark

                Author and article information

                Journal
                Environ Sci Technol
                Environ Sci Technol
                es
                esthag
                Environmental Science & Technology
                American Chemical Society
                0013-936X
                1520-5851
                29 June 2023
                11 July 2023
                : 57
                : 27
                : 9898-9924
                Affiliations
                []Department of Electrode Processes, Institute of Physical Chemistry, Polish Academy of Sciences , 01-224 Warsaw, Poland
                []Bio & Soft Matter, Institute of Condensed Matter and Nanosciences, Université catholique de Louvain , 1 Place Louis Pasteur, 1348 Louvain-la-Neuve, Belgium
                [§ ]Functional Polymers Research Team, Institute of Physical Chemistry, Polish Academy of Sciences , 01-224 Warsaw, Poland
                []ENSEMBLE3 sp. z o. o. , 01-919 Warsaw, Poland
                []Faculty of Mathematics and Natural Sciences. School of Sciences, Cardinal Stefan Wyszynski University in Warsaw , 01-938 Warsaw, Poland
                [# ]Modified Electrodes for Potential Application in Sensors and Cells Research Team, Institute of Physical Chemistry, Polish Academy of Sciences , 01-224 Warsaw, Poland
                Author notes
                [* ]Phone: +32 10 47 8460. E-mail: jaroslaw.mazuryk@ 123456uclouvain.be .
                [* ]Phone: +48 22 343 31 88. E-mail: psharma@ 123456ichf.edu.pl .
                Author information
                https://orcid.org/0000-0003-3311-7136
                https://orcid.org/0000-0002-2003-0181
                https://orcid.org/0000-0003-3586-5170
                https://orcid.org/0000-0002-7729-8314
                Article
                10.1021/acs.est.3c01269
                10339735
                37384557
                0ef65093-6a79-4325-94ce-5ae8b6c904be
                © 2023 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 15 February 2023
                : 08 June 2023
                : 23 May 2023
                Funding
                Funded by: Narodowe Centrum Badan i Rozwoju, doi 10.13039/501100005632;
                Award ID: PhotonicSensing/1/2018
                Categories
                Critical Review
                Custom metadata
                es3c01269
                es3c01269

                General environmental science
                engineered nanomaterials,environmental pollution,glyphosate-based herbicides,lab-on-a-chip,precision agriculture,smart material-based sensors

                Comments

                Comment on this article