Electronic Detection of Pesticide Residue on Cherry Fruits
DOI:
https://doi.org/10.22399/ijcesen.401Keywords:
Cherry, Pesticide detection, Odour classification, Electronic nose, Extra Trees algorithm.Abstract
Agriculture is one of the most basic needs that is of vital importance for the human generation to be sustained. In agriculture, pesticides are an indispensable need both to increase productivity and to prevent crop losses. Some of these drugs are pesticides that protect the plant from harmful insects, fungi and rodents, while others are herbicides that protect it from weeds. However, if these chemicals penetrate into people as much as they ensure food safety, they can be harmful with various diseases in the long term. After these pesticides are applied to agricultural products, they decrease to safe levels after 3-10 days depending on the type and dosage of the drug. Long-term exposure to these chemicals can cause a number of health problems such as cancer, hormonal disorders, neurological diseases and immune system weakness. In this study, it was investigated whether the collected cherry and the pesticide-free cherry could be separated by smell after the pesticide was applied to it without passing the mentioned time. Cherries were collected from cherry trees that had never been sprayed, and then pesticide was sprayed on these trees, and cherries that were sprayed with pesticide were collected a day later. An electronic nose consisting of 11 very affordable gas sensors has been made for the study. One hundred pieces of odours were taken by the electronic nose, including different amounts of cherries with and without pesticides. Various attributes of these data have been extracted. Among the four different classification algorithms, the Extra Trees Classifier has given the most successful results with 94.30% classification accuracy, 93.00% sensitivity and 95.60% specificity classification success. The ability to detect the pesticides on the fruit with an electronic device is important for the monitoring of human health through food inspections.
References
M. A. Zeder, (2011). The Origins of Agriculture in the Near East, Curr. Anthropol., 52(S4);S221–S235, Oct. 2011, doi: 10.1086/659307.
E. Leon, (2020). Andes: Origins and Development of Agriculture, in Encyclopedia of Global Archaeology, C. Smith, Ed., Cham: Springer International Publishing, 352–357. doi: 10.1007/978-3-030-30018-0_1685.
A. Fekete and M. Sárospataki, (2023). Plant Use in the Late Renaissance Gardens of the 17–18th Century Transylvania,” Plants, 12(9) doi: 10.3390/plants12091798.
M. F. Hamdan, S. N. Mohd Noor, N. Abd-Aziz, T.-L. Pua, and B. C. Tan, Green Revolution to Gene Revolution: Technological Advances in Agriculture to Feed the World, Plants, 11(10)1297, doi: 10.3390/plants11101297.
S. K. Balasundram, R. R. Shamshiri, S. Sridhara, and N. Rizan, (2023). The Role of Digital Agriculture in Mitigating Climate Change and Ensuring Food Security: An Overview, Sustainability, 15(6)doi: 10.3390/su15065325.
D. C. Tsouros, S. Bibi, and P. G. Sarigiannidis, (2019). A Review on UAV-Based Applications for Precision Agriculture, Information, 10(11);349, doi: 10.3390/info10110349.
Z. Ye, Y. Liu, and Q. Li, (2021). Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods, Sensors, doi: 10.3390/s21227620.
O. B. Yurdakos and O. Cihanbegendi, (2024) System Design Based on Biological Olfaction for Meat Analysis Using E-Nose Sensors, ACS Omega, doi: 10.1021/acsomega.4c04791.
S. Güney and A. Atasoy, (2015). Study of fish species discrimination via electronic nose,” Comput. Electron. Agric., 119;83–91, doi: 10.1016/j.compag.2015.10.005.
K. Fujioka, (2021). Comparison of Cheese Aroma Intensity Measured Using an Electronic Nose (E-Nose) Non-Destructively with the Aroma Intensity Scores of a Sensory Evaluation: A Pilot Study, Sensors, 21(24) doi: 10.3390/s21248368.
N. Phukkaphan, T. Eamsa-ard, C. Chairanit, and T. Kerdcharoen, (2021). The Application of Gas Sensor Array based Electronic Nose for Milk Spoilage Detection,” in 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), pp. 273–276. doi: 10.1109/ICEAST52143.2021.9426263.
S. MacDougall, F. Bayansal, and A. Ahmadi (2022). Emerging Methods of Monitoring Volatile Organic Compounds for Detection of Plant Pests and Disease, Biosensors, 12(4)doi: 10.3390/bios12040239.
Z. Gan, Q. Zhou, C. Zheng, and J. Wang, “Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis,” Biosens. Bioelectron., vol. 237, p. 115540, Oct. 2023, doi: 10.1016/j.bios.2023.115540.
M. Mohd Ali, N. Hashim, S. Abd Aziz, and O. Lasekan, (2020). Principles and recent advances in electronic nose for quality inspection of agricultural and food products, Trends Food Sci. Technol., 99;1–10, doi: 10.1016/j.tifs.2020.02.028.
J. Wang, B.-C. Wei, X. Wang, Y. Zhang, and Y.-J. Gong, (2023). Aroma profiles of sweet cherry juice fermented by different lactic acid bacteria determined through integrated analysis of electronic nose and gas chromatography–ion mobility spectrometry, Front. Microbiol., 14, doi: 10.3389/fmicb.2023.1113594.
R. Cozzolino et al., (2021). Electronic-Nose as Non-destructive Tool to Discriminate ‘Ferrovia’ Sweet Cherries Cold Stored in Air or Packed in High CO2 Modified Atmospheres, Front. Nutr., 8 doi: 10.3389/fnut.2021.720092.
A. Spinardi, (2010). NON-DESTRUCTIVE EVALUATION OF SWEET CHERRY (PRUNUS AVIUM L.) RIPENESS USING AN ELECTRONIC NOSE, Ital. J. Food Sci., 22;299
N. A. Nategh, M. J. Dalvand, and A. Anvar, (2021). Detection of toxic and non-toxic sweet cherries at different degrees of maturity using an electronic nose, J. Food Meas. Charact., 15(2);1213–1224 doi: 10.1007/s11694-020-00724-6.
D. Pimentel, (2005). ‘Environmental and Economic Costs of the Application of Pesticides Primarily in the United States, Environ. Dev. Sustain., 7(2);229–252, doi: 10.1007/s10668-005-7314-2.
Md. W. Aktar, D. Sengupta, and A. Chowdhury, (2009). Impact of pesticides use in agriculture: their benefits and hazards, Interdiscip. Toxicol., 2(1);1–12, doi: 10.2478/v10102-009-0001-7.
Pesticides, environment, and food safety - Carvalho - 2017 - Food and Energy Security - Wiley Online Library.” Accessed: Jul. 26, 2024. [Online]. Available: https://onlinelibrary.wiley.com/doi/full/10.1002/fes3.108
M. C. R. Alavanja, M. K. Ross, and M. R. Bonner, (2013). Increased cancer burden among pesticide applicators and others due to pesticide exposure, CA. Cancer J. Clin., 63(2);120–142, doi: 10.3322/caac.21170.
Y. Hua and G. Liu, (2024). Food Pesticide Residues Monitoring and Health Risk Assessment, Foods, 13(3) doi: 10.3390/foods13030474.
CABI, “Rhagoletis cerasi (European cherry fruit fly),” CABI Compend., vol. CABI Compendium, p. 47050, Nov. 2021, doi: 10.1079/cabicompendium.47050.
A. Xie et al., (2021). Comprehensive benefit evaluation method of energy Internet platform based on cloud model,” in 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), pp. 736–742. doi: 10.1109/ICBASE53849.2021.00144.
Ö. F. Akmeşe, (2022). Diagnosing Diabetes with Machine Learning Techiques, Hittite J. Sci. Eng., 9(1), doi: 10.17350/HJSE19030000250.
E. Ozturk Kiyak, B. Ghasemkhani, and D. Birant,(2023). High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis, Electronics, 12(18), doi: 10.3390/electronics12183828.
O. Aydemir and T. Kayikcioglu, (2014). Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery, J. Neurosci. Methods, 229;68–75, doi: 10.1016/j.jneumeth.2014.04.007.
T. Kavzoglu and F. Bilucan, (2023). Effects of auxiliary and ancillary data on LULC classification in a heterogeneous environment using optimized random forest algorithm, Earth Sci. Inform., 16(1);415–435, doi: 10.1007/s12145-022-00874-9.
A. K. V, A. A, B. Jose, K. Anilkumar, and O. T. Lee, (2021). Phishing Detection using Extra Trees Classifier, in 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 1–6. doi: 10.1109/ISCON52037.2021.9702372.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
