https://doi.org/10.4081/jae.2024.1647
Application of MOS gas sensors for detecting mechanical damage of tea plants
Published: 2 December 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Mechanical damage of tea plant is a serious problem in tea production. This work employed metal oxide semiconductor (MOS) gas sensors and gas chromatography-mass spectrometer (GC-MS), as an auxiliary technique, to detect tea plants with different types of mechanical damage in different severities. Various algorithms were applied. The results showed the uniformity of the results of gas sensors and GC-MS. While, it was hard for gas sensors to discriminate among tea plants with different types of mechanical damage. However, the feasibility of gas sensors for predicting the damage severity in different damaged types based on gas sensors was proven, which was more meaningful. Finally, multi-layer perceptron neural networks (MLPNN) was employed and the results showed that the correct discrimination accuracy rate for damage severity was 99.07% for the training set and 95.83% for the testing set, which indicated that MLPNN was an excellent algorithm for damage severity determination. This study provided a new technique for mechanical damage of tea plant detection and was very meaningful for tea plant protection.
Abiri, B., Amini, S., Hejazi, M., Hosseinpanah, F., Zarghi, A., Abbaspour, F., Valizadeh, M. 2023. Tea's anti-obesity properties, cardiometabolic health-promoting potentials, bioactive compounds, and adverse effects: A review focusing on white and green teas. Food Sci. Nutr. 11:5818-5836.
Aghoutane, Y., Brebu, M., Moufd, M., Ionescu, R., Bouchikhi, B., Bari, N.E. 2023. Detection of counterfeit perfumes by using GC-MS technique and electronic nose system combined with chemometric tools. Micromachines 14:524.
Andrews, S.J., Hackenberg, S C., Carpenter, L.J. 2015. Technical note: a fully automated purge and trap GC-MS system for quantification of volatile organic compound (VOC) fluxes between the ocean and atmosphere. Ocean Sci. 11:13-321.
Bezerra, R., Sousa-Souto, L., Santana, A., Ambrogi, B.G. 2021. Indirect plant defenses: volatile organic compounds and extrafloral nectar. Arthropod-Plant Inte. 15:467-489.
Chacón-Fuentes, M., Bardehle, L., Seguel, I., Espinoza, J., Lizama, M., Quiroz, A. 2023. Herbivory damage increased VOCs in wild relatives of Murtilla plants compared to their first offspring. Metabolites 13:616.
Ghooshkhaneh N. G., Mollazade K. 2023. Optical techniques for fungal disease detection in citrus fruit: a review. Food Bioprocess Tech. 16:1668-1689.
He, W., Yuan, Z., Yin, B., Wu, W., Min, Z. 2023. Robust locally linear embedding and its application in analogue circuit fault diagnosis. Meas. Sci. Technol. 34:105005.
Holopainen, J. K., Gershenzon, J. 2010. Multiple stress factors and the emission of plant VOCs. Trends Plant Sci. 15:176-184.
Jiang, S., Wang, J., Wang, Y., Cheng, S. 2017. A novel framework for analyzing MOS E-nose data based on voting theory: application to evaluate the internal quality of Chinese pecans. Sensors Actuat. B-Chem. 242:511-521.
Jiang, W., Su, B., Fan, S. 2023. Spatial disequilibrium and dynamic evolution of eco-efficiency in China’s tea industry. Sustainability (Basel) 15:9597.
Lautner, S., Grams, T.E.E., Matyssek, R., Fromm, J. 2005. Characteristics of electrical signals in poplar and responses in photosynthesis. Plant Physiol. 138:2200-2209.
Lee, J.P., Lee, S.W., Kim, C.S., Ji, H.S., Song, J.H., Lee, K.Y., et al. 2006. Evaluation of formulations of bacillus licheniformis for the biological control of tomato gray mold caused by Botrytis cinerea. Biol. Control 37:329-337.
Lin, W., Gao, Q., Du, M., Chen, W., Tong, T. 2021. Multiclass diagnosis of stages of Alzheimer's disease using linear discriminant analysis scoring for multimodal data. Comput. Biol. Med. 134:104478.
Lu, C., Feng, J., Chen, Y., Liu, W., Lin, Z., Ya,n S. 2020. Tensor robust principal component analysis with a new tensor nuclear norm. IEEE T. Pattern Anal. 42:925-938.
Ma, M., Yang, X., Ying, X., Shi, C., Jia, Z., Jia, B. 2023. Applications of gas sensing in food quality detection: a review. Foods (Basel) 12:3966.
Miller, A.R. 1992. Physiology, biochemistry and detection of bruising (mechanical stress) in fruits and vegetables. Postharvest News Inform. 3:53-58.
Nykänen, H., Koricheva, J. 2004. Damage-induced changes in woody plants and their effects on insect herbivore performance: a meta-analysis. Oikos 104:247-268.
Piłat-Rożek, M., Dziadosz, M., Majerek, D., Jaromin-Gleń, K., Szeląg, B., Guz, Ł., et al. 2023. Rapid method of wastewater classification by electronic nose for performance evaluation of bioreactors with activated sludge. Sensors (Basel) 23:8578.
Seo, M., Min, S. 2023. Graph neural networks and implicit neural representation for near-optimal topology prediction over irregular design domains. Eng. Appl. Artif. Intell. 123:106284.
Sheikhhosseini, Z., Mirzaei, N., Heidari, R., Monkaresi, H. 2021. Delineation of potential seismic sources using weighted k-means cluster analysis and particle swarm optimization (PSO). Acta Geophys. 69:2161-2172.
Sibi, S.P.L., Rajkumar, M., Manoharan, M., Mobika, J., Priya, V.N., Kumar, R.T.R. 2024. Humidity activated ultra-selective room temperature gas sensor based on W doped MoS2/RGO composites for trace level ammonia detection. Anal. Chim. Acta 1287:342075.
Tang, Z., Zhu, J., Song, Q., Daly, P., Kong, L., et al. 2024. Identification and pathogenicity of Fusarium spp. associated with tea wilt in Zhejiang Province, China. BMC Microbiol. 24:38.
Tonin, F., Tao, Q., Patrinos, P., Suykens, J.A.K. 2024. Deep Kernel principal component analysis for multi-level feature learning. Neural Networks 170:578-595.
Wang, J. 2012. Geometric structure of high-dimensional data and dimensionality reduction. Berlin, Springer.
Yang, R., Lin, W., Liu, J., Liu, H., Fu, X., Liu, H., et al. 2023. Formation mechanism and solution of Pu-erh tea cream based on non-targeted metabonomics. LTW 173:114331.
Zangerl, A. 2002. Impact of folivory on photosynthesis is greater than the sum of its holes. Proc. Natl. Acad. Sci. USA 99:1088-1091.
Zhu, C., Guo, J., Yuan, J., Jin, X., Li, C. 2021. Refining altimeter-derived gravity anomaly model from shipborne gravity by multi-layer perceptron neural network: a case in the South China Sea. Remote Sens. (Basel) 13:607.
Supporting Agencies
Zhejiang Provincial Natural Science Foundation of ChinaHow to Cite
Sun, Y. and Zheng, Y. (2024) “Application of MOS gas sensors for detecting mechanical damage of tea plants”, Journal of Agricultural Engineering, 55(4). doi: 10.4081/jae.2024.1647.
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