Sensor MOS Hidung Elektronik untuk Membedakan Thrips dan Spodoptera pada Stroberi
Sari
Penelitian ini mengevaluasi efektivitas hidung elektronik berbasis sensor MOS, yaitu sensor TGS dan MQ, dalam mendeteksi dan membedakan hama thrips dan Spodoptera litura pada tanaman stroberi. Data volatil yang dihasilkan oleh tanaman diamati menggunakan sensor E-nose yang terhubung dengan model jaringan saraf tiruan Backpropagation (BPPN). Dengan penyetelan GridSearchCV, akurasi deteksi meningkat secara signifikan, terutama pada sensor TGS, yang menunjukkan kinerja lebih baik dibandingkan sensor MQ. Teknologi ini menawarkan pendekatan deteksi hama yang sensitif, tidak merusak, dan ramah lingkungan, dengan potensi untuk mendukung pengelolaan hama secara berkelanjutan dalam budidaya stroberi. Penelitian ini memberikan peluang baru untuk inovasi di bidang pertanian pintar dengan pengurangan penggunaan pestisida yang berlebihan dan optimalisasi strategi pengelolaan hama.
Kata kunci: hidung elektronik, deteksi, trips, spodoptera, stroberi
Abstract
This study evaluates the effectiveness of metal oxide semiconductor (MOS) electronic noses, specifically the TGS and MQ sensors, in detecting and distinguishing between thrips and Spodoptera litura pests on strawberry plants. Volatile compounds produced by the plants were analyzed using an E-nose connected to a Backpropagation Neural Network (BPNN) model. The GridSearchCV optimization significantly improved detection accuracy, particularly for the TGS sensor, which outperformed the MQ sensor. This technology offers a sensitive, non-invasive, and environmentally friendly approach to pest detection, supporting sustainable pest management in strawberry cultivation. The study opens new opportunities for smart agricultural innovations, reducing excessive pesticide use and optimizing pest control strategies.
Keywords: electronic nose, detection, thrips, spodoptera, strawberry
Teks Lengkap:
PDFReferensi
Abdelmaksoud, E. M., El-Refai, S. A., Mahmoud, K. W., & Ragab, M. E. (2020). Susceptibility of some new strawberry genotypes to infestation by western flower thrips, Frankliniella occidentalis (Pergande)(Thysanoptera: Thripidae) in the nursery. Annals of Agricultural Sciences, 65(2), 144–148.
Ali, M. Y., Naseem, T., Holopainen, J. K., Liu, T., Zhang, J., & Zhang, F. (2023). Tritrophic interactions among arthropod natural enemies, herbivores and plants considering volatile blends at different scale levels. Cells, 12(2), 251.
Amarathunga, D. C., Parry, H., Grundy, J., & Dorin, A. (2024). A predator–prey population dynamics simulation for biological control of Frankliniella occidentalis (Western Flower Thrips) by Orius laevigatus in strawberry plants. Biological Control, 188, 105409.
Conzemius, S. R., Reay-Jones, F. P. F., Greene, J. K., Campbell, B. T., Reisig, D. D., Wang, H., & Bridges, W. C. (2023). Field screening of wild cotton, Gossypium hirsutum, landraces for resistance to thrips (Thysanoptera: Thripidae). Crop Protection, 163, 106113.
Dong, C.-W., Yang, Y. E., Zhang, J.-Q., Zhu, H.-K., & Fei, L. I. U. (2014). Detection of thrips defect on green-peel citrus using hyperspectral imaging technology combining PCA and B-spline lighting correction method. Journal of Integrative Agriculture, 13(10), 2229–2235.
Feltes, G., Ballen, S. C., Soares, A. C., Soares, J. C., Paroul, N., Steffens, J., & Steffens, C. (2024). Discrimination of artificial strawberry aroma by electronic nose based on nanocomposites. Journal of Food Process Engineering, 47(1), e14501.
Fuentes, S., Tongson, E., Unnithan, R. R., & Gonzalez Viejo, C. (2021). Early detection of aphid infestation and insect-plant interaction assessment in wheat using a low-cost electronic nose (E-nose), near-infrared spectroscopy and machine learning modeling. Sensors, 21(17), 5948.
Kessek, L. I. M., Tulung, M., & Salaki, C. L. (2015). Jenis dan Populasi Hama Pada Tanaman Stroberi (Fragaria X Ananassa Duscesne). Eugenia, 21(1).
Kim, C.-Y., Ahmed, S., Stanley, D., & Kim, Y. (2023). HMG-like DSP1 is a damage signal to mediate the western flower thrips, Frankliniella occidentalis, immune responses to tomato spotted wilt virus infection. Developmental & Comparative Immunology, 144, 104706.
Lapcharoensuk, R., & Moul, C. (2024). Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 318, 124480.
MacDougall, S., Bayansal, F., & Ahmadi, A. (2022). Emerging methods of monitoring volatile organic compounds for detection of plant pests and disease. Biosensors, 12(4), 239.
Majid, A., Speed, L., Croijmans, I., & Arshamian, A. (2017). What makes a better smeller? Perception, 46(3–4), 406–430.
Mulyatni, A. S., Kresnawaty, I., Eris, D. D., Panji, T., Kimberly, W., Widiastuti, H., PRIYONO, P., CHOTIMAH, C., & TRIYANA, K. (2022). Potensi electronic nose 118 untuk mendeteksi penyakit busuk pangkal batang pada kelapa sawit. Menara Perkebunan, 90(1).
Olatinwo, R., & Hoogenboom, G. (2014). Weather-based pest forecasting for efficient crop protection. In Integrated pest management (pp. 59–78). Elsevier.
Rahman, K. S., Salehin, M. M., Roy, R., Swarna, J. B., Rakib, M. R. I., Saha, C. K., & Rahman, A. (2024). Prediction of mango quality during ripening stage using MQ-based electronic nose and multiple linear regression. Smart Agricultural Technology, 9, 100558.
Rao, J., Zhang, Y., Yang, Z., Li, S., Wu, D., Sun, C., & Chen, K. (2020). Application of electronic nose and GC–MS for detection of strawberries with vibrational damage. Food Quality and Safety, 4(4), 181–192.
Shih, H.-J., Kao, Y.-H., Chang, T.-W., Shen, W.-H., Lin, Y.-H., Lin, Y.-C., Liao, T.-S., Hsiao, W.-T., & Lin, H.-N. (2022). Smart Phone Operated Portable Nitrogen Dioxide Gas Sensor. 203, 10–20.
Tay, A., Lafont, F., Balmat, J.-F., Pessel, N., & Lhoste-Drouineau, A. (2021). Decision support system for Western Flower Thrips management in roses production. Agricultural Systems, 187, 103019.
DOI: https://doi.org/10.26760/mindjournal.v10i1.48-60
Refbacks
- Saat ini tidak ada refbacks.
____________________________________________________________
ISSN (Print): 2338-8323 | ISSN (Online): 2528-0902
Published by:
Department of Informatics, Institut Teknologi Nasional Bandung
Address:
Building 2, Jl. PHH Mustofa No. 23, Bandung 40124, Indonesia
Contact:
Phone: +62-22-7272215 (ext. 181) Fax: +62-22-7202892
Email: mind.journal@itenas.ac.id
______________________________
Statistik Pengunjung :
Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.