J. Semicond. > 2023, Volume 44 > Issue 2 > 023104
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REVIEWS

Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review

Jiayao He, Ke Chen, Xubin Pan, Junfeng Zhai and Xiangmei Lin

+ Author Affiliations

 Corresponding author: Junfeng Zhai, zjf1208@163.com; Xiangmei Lin, linxm@caiq.org.cn

DOI: 10.1088/1674-4926/44/2/023104

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Abstract: The threat posed to crop production by pests and diseases is one of the key factors that could reduce global food security. Early detection is of critical importance to make accurate predictions, optimize control strategies and prevent crop losses. Recent technological advancements highlight the opportunity to revolutionize monitoring of pests and diseases. Biosensing methodologies offer potential solutions for real-time and automated monitoring, which allow advancements in early and accurate detection and thus support sustainable crop protection. Herein, advanced biosensing technologies for pests and diseases monitoring, including image-based technologies, electronic noses, and wearable sensing methods are presented. Besides, challenges and future perspectives for widespread adoption of these technologies are discussed. Moreover, we believe it is necessary to integrate technologies through interdisciplinary cooperation for further exploration, which may provide unlimited possibilities for innovations and applications of agriculture monitoring.

Key words: precision agriculture, biosensors, crops, disease and pest management



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Fig. 1.  (Color online) The bimodal sensor to detect insect wingbeat[28].

DownLoad: Full-Size Img PowerPoint
Fig. 3.  (Color online) The principle of the e-nose detection for citrus fruits infested by B. dorsalis[39].

DownLoad: Full-Size Img PowerPoint
Fig. 2.  (Color online) (a) A schematic view of the electronic nose system. (b) Cylindrical sensor chamber with 6 metal–oxide–semiconductor sensors. (c) System procedure diagram with pump flow work that will control the sensor response in a specific cycle[31].

DownLoad: Full-Size Img PowerPoint
Fig. 4.  (Color online) (a) Optical images of the flexible wearable sensor mounted on a single plant leaf. (b) Exploded view illustration of the sap flow sensor[53]. (c) Schematic illustration of the fabrication process of the flexible humidity sensor[59]. (d) Schematic of the integrated device attached onto the lower epidermis of the leaf to monitor transpiration processes. (e) Photo of the front view of the multimodal flexible plant healthcare device integrated with a room humidity sensor, leaf-surface humidity sensor, optical sensor, and temperature sensor[62].

DownLoad: Full-Size Img PowerPoint
[1]
Unnevehr L J. Causes of and constraints to agricultural and economic development: Discussion. Am J Agric Econ, 2007, 89, 1168 doi: 10.1111/j.1467-8276.2007.01078.x
[2]
Savary S, Willocquet L, Pethybridge S J, et al. The global burden of pathogens and pests on major food crops. Nat Ecol Evol, 2019, 3, 430 doi: 10.1038/s41559-018-0793-y
[3]
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[4]
Preti M, Verheggen F, Angeli S. Insect pest monitoring with camera-equipped traps: Strengths and limitations. J Pest Sci, 2021, 94, 203 doi: 10.1007/s10340-020-01309-4
[5]
Weersink A, Fraser E, Pannell D, et al. Opportunities and challenges for big data in agricultural and environmental analysis. Annu Rev Resour Econ, 2018, 10, 19 doi: 10.1146/annurev-resource-100516-053654
[6]
Silva G, Tomlinson J, Onkokesung N, et al. Plant pest surveillance: From satellites to molecules. Emerg Top Life Sci, 2021, 5, 275 doi: 10.1042/ETLS20200300
[7]
Walter A, Finger R, Huber R, et al. Opinion: Smart farming is key to developing sustainable agriculture. Proc Natl Acad Sci USA, 2017, 114, 6148 doi: 10.1073/pnas.1707462114
[8]
Garnett T, Appleby M C, Balmford A, et al. Agriculture. Sustainable intensification in agriculture: Premises and policies. Science, 2013, 341, 33 doi: 10.1126/science.1234485
[9]
Stafford J V. Implementing precision agriculture in the 21st century. J Agric Eng Res, 2000, 76, 267 doi: 10.1006/jaer.2000.0577
[10]
Kashyap B, Kumar R. Sensing methodologies in agriculture for monitoring biotic stress in plants due to pathogens and pests. Inventions, 2021, 6, 29 doi: 10.3390/inventions6020029
[11]
Lee G, Wei Q S, Zhu Y. Emerging wearable sensors for plant health monitoring. Adv Funct Mater, 2021, 31, 2106475 doi: 10.1002/adfm.202106475
[12]
He D C, Zhan J S, Xie L H. Problems, challenges and future of plant disease management: From an ecological point of view. J Integr Agric, 2016, 15, 705 doi: 10.1016/S2095-3119(15)61300-4
[13]
Rossi V, Giosuè S, Caffi T. Modelling plant diseases for decision making in crop protection. Precision Crop Protection - the Challenge and Use of Heterogeneity. Dordrecht: Springer Netherlands, 2010, 241
[14]
Grünig M, Razavi E, Calanca P, et al. Applying deep neural networks to predict incidence and phenology of plant pests and diseases. Ecosphere, 2021, 12, e03791 doi: 10.1002/ecs2.3791
[15]
Cordier T, Forster D, Dufresne Y, et al. Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring. Mol Ecol Resour, 2018, 18, 1381 doi: 10.1111/1755-0998.12926
[16]
van Klink R, August T, Bas Y, et al. Emerging technologies revolutionise insect ecology and monitoring. Trends Ecol Evol, 2022, 37, 872 doi: 10.1016/j.tree.2022.06.001
[17]
Mahlein A K. Plant disease detection by imaging sensors - parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis, 2016, 100, 241 doi: 10.1094/PDIS-03-15-0340-FE
[18]
Zhang J C, Huang Y B, Pu R L, et al. Monitoring plant diseases and pests through remote sensing technology: A review. Comput Electron Agric, 2019, 165, 104943 doi: 10.1016/j.compag.2019.104943
[19]
Ennouri K, Kallel A. Remote sensing: An advanced technique for crop condition assessment. Math Probl Eng, 2019, 2019, 1 doi: 10.1155/2019/9404565
[20]
Mahlein A K, Kuska M T, Thomas S, et al. Plant disease detection by hyperspectral imaging: From the lab to the field. Adv Animal Biosci, 2017, 8, 238 doi: 10.1017/S2040470017001248
[21]
Carlos M O J, María H C L, Veronica B F. Detection of significant wavelengths for identifying and classifying Fusarium oxysporum during the incubation period and water stress in Solanum lycopersicum plants using reflectance spectroscopy. J Plant Prot Res, 2019, 59, 244 doi: 10.24425/jppr.2019.129290
[22]
do Prado Ribeiro L, Klock A L S, Filho J A W, et al. Hyperspectral imaging to characterize plant-plant communication in response to insect herbivory. Plant Methods, 2018, 14, 54 doi: 10.1186/s13007-018-0322-7
[23]
Eliopoulos P A, Potamitis I, Kontodimas D Ch, et al. Detection of adult beetles inside the stored wheat mass based on their acoustic emissions. J Econ Entomol, 2015, 108, 2808 doi: 10.1093/jee/tov231
[24]
Holguin G A, Lehman B L, Hull L A, et al. Electronic traps for automated monitoring of insect populations. IFAC Proc Vol, 2010, 43, 49 doi: 10.3182/20101206-3-JP-3009.00008
[25]
Shaked B, Amore A, Ioannou C, et al. Electronic traps for detection and population monitoring of adult fruit flies (Diptera: Tephritidae). J Appl Entomol, 2018, 142, 43 doi: 10.1111/jen.12422
[26]
Ding W G, Taylor G. Automatic moth detection from trap images for pest management. Comput Electron Agric, 2016, 123, 17 doi: 10.1016/j.compag.2016.02.003
[27]
Welsh T J, Bentall D, Kwon C, et al. Automated surveillance of lepidopteran pests with smart optoelectronic sensor traps. Sustainability, 2022, 14, 9577 doi: 10.3390/su14159577
[28]
Potamitis I, Rigakis I, Vidakis N, et al. Affordable bimodal optical sensors to spread the use of automated insect monitoring. J Sens, 2018, 2018, 1 doi: 10.1155/2018/3949415
[29]
Maffei M E. Sites of synthesis, biochemistry and functional role of plant volatiles. S Afr N J Bot, 2010, 76, 612 doi: 10.1016/j.sajb.2010.03.003
[30]
Xu S, Zhou Z Y, Li K L, et al. Recognition of the duration and prediction of insect prevalence of stored rough rice infested by the red flour beetle (tribolium castaneum herbst) using an electronic nose. Sensors, 2017, 17, 688 doi: 10.3390/s17040688
[31]
Nouri B, Fotouhi K, Mohtasebi S S, et al. Detection of different densities of Ephestia kuehniella pest on white flour at different larvae instar by an electronic nose system. J Stored Prod Res, 2019, 84, 101522 doi: 10.1016/j.jspr.2019.101522
[32]
Rutolo M F, Iliescu D, Clarkson J P, et al. Early identification of potato storage disease using an array of metal-oxide based gas sensors. Postharvest Biol Technol, 2016, 116, 50 doi: 10.1016/j.postharvbio.2015.12.028
[33]
Labanska M, Jenkins S, Van Amsterdam S, et al. Detection of the fungal infection in post-harvest Onions by an electronic nose. 2022 IEEE International Symposium on Olfaction and Electronic Nose, 2022, 1 doi: 10.1109/ISOEN54820.2022.9789676
[34]
Chang K P P, Zakaria A, Abdul Nasir A S, et al. Analysis and feasibility study of plant disease using e-nose. 2014 IEEE International Conference on Control System, Computing and Engineering, 2015, 58 doi: 10.1109/ICCSCE.2014.7072689
[35]
Hazarika S, Choudhury R, Montazer B, et al. Detection of citrus tristeza virus in mandarin orange using a custom-developed electronic nose system. IEEE Trans Instrum Meas, 2020, 69, 9010 doi: 10.1109/TIM.2020.2997064
[36]
Srivastava S, Mishra G, Mishra H N. Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain. Food Chem, 2019, 283, 604 doi: 10.1016/j.foodchem.2019.01.076
[37]
Cellini A, Blasioli S, Biondi E, et al. Potential applications and limitations of electronic nose devices for plant disease diagnosis. Sensors, 2017, 17, 2596 doi: 10.3390/s17112596
[38]
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    Received: 25 November 2022 Revised: 13 January 2023 Online: Uncorrected proof: 03 February 2023Accepted Manuscript: 03 February 2023Published: 10 February 2023

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      Article Navigation >  Journal of Semiconductors  > 2023  >  44(2): 023104
      Jiayao He, Ke Chen, Xubin Pan, Junfeng Zhai, Xiangmei Lin. Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review[J]. Journal of Semiconductors, 2023, 44(2): 023104. doi: 10.1088/1674-4926/44/2/023104 ****J Y He, K Chen, X B Pan, J F Zhai, X M Lin. Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review[J]. J. Semicond, 2023, 44(2): 023104. doi: 10.1088/1674-4926/44/2/023104
      Citation:
      Jiayao He, Ke Chen, Xubin Pan, Junfeng Zhai, Xiangmei Lin. Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review[J]. Journal of Semiconductors, 2023, 44(2): 023104. doi: 10.1088/1674-4926/44/2/023104 ****
      J Y He, K Chen, X B Pan, J F Zhai, X M Lin. Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review[J]. J. Semicond, 2023, 44(2): 023104. doi: 10.1088/1674-4926/44/2/023104
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      Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review

      DOI: 10.1088/1674-4926/44/2/023104

      • Chinese Academy of Inspection and Quarantine, Beijing 100176, China

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      • Jiayao He：focuses on plant protection, pest risk analysis, and monitoring of agriculture pests and diseases
      • Junfeng Zhai：got his Doctor’s degree of agricultural biotechnology at Jilin Agricultural University in 2013. His research fields include biotechnology development, biological safety evaluation, and monitoring of agriculture pests and diseases
      • Xiangmei Lin：got her Doctor’s degree of Veterinary Pathology at Nanjing Agricultural University in 1998. Her research interests include detection and monitoring technologies for animal diseases, zoonotic diseases, foreign animal diseases and monitoring technologies for the detection of genetic modification in animals
      • Corresponding author: zjf1208@163.com; linxm@caiq.org.cn
      • Received Date: 2022-11-25
      • Revised Date: 2023-01-13
        • Available Online: 2023-02-03

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