Empoasca Pest Attack Classification on Tea Plantations Using Multispectral Imaging and Deep Learning
Downloads
This study aims to enhance the management of Empoasca pests in tea cultivation, a critical sector for Indonesia’s economy, by developing an innovative detection method. The challenge of pest infestations may significantly reduce tea production yields, and the misuse of chemical pesticides further compromises tea quality. We propose a novel approach that integrates multispectral imaging with Convolutional Neural Networks (CNN), specifically employing ResNet-50 and AlexNet architectures to accurately detect Empoasca infestations. We begin with the data collection process, followed by the development of the preprocessing model and evaluation of its performance. We classify tea leaves affected by Empoasca pests using spectral data obtained from a multispectral camera operating across Green, NIR (Near Infrared), REG (Red Edge), and RED channels. We evaluated various spectral channels and identified the green spectrum as the most effective for revealing visual characteristics, such as curled leaves associated with Empoasca damage. Experimental results demonstrated that ResNet-50 outperformed AlexNet, achieving a remarkable accuracy of 99% on the green channel, while AlexNet showed notable accuracy declines on other channel combinations. These findings underscore the effectiveness of the green spectrum and the superiority of ResNet-50 in achieving precise pest detection, offering a reliable technological solution for modern tea plantation management.
Downloads
[1] Hajiboland, R. (2017). Environmental and nutritional requirements for tea cultivation. Folia Horticulturae, 29(2), 199–220. doi:10.1515/fhort-2017-0019.
[2] Adelianingsih, D., Hidayati, R., & Sugiarto, Y. (2019). Potential of Green Leafhopper Attack (Empoasca sp.) in Tea Plantation Based on Climate Change Scenarios. Agromet, 33(2), 84–95. doi:10.29244/j.agromet.33.2.84-95.
[3] Pramudita, A. A., Wahyu, Y., Rizal, S., Prasetio, M. D., Jati, A. N., Wulansari, R., & Ryanu, H. H. (2022). Soil Water Content Estimation with the Presence of Vegetation Using Ultra Wideband Radar-Drone. IEEE Access, 10, 85213–85227. doi:10.1109/ACCESS.2022.3197636.
[4] Narmilan, A., Gonzalez, F., Salgadoe, A. S. A., & Powell, K. (2022). Detection of White Leaf Disease in Sugarcane Using Machine Learning Techniques over UAV Multispectral Images. Drones, 6(9), 230. doi:10.3390/drones6090230.
[5] Hooshyar, M., Li, Y. S., Chun Tang, W., Chen, L. W., & Huang, Y. M. (2024). Economic Fruit Trees Recognition in Hillsides: A CNN-Based Approach Using Enhanced UAV Imagery. IEEE Access, 12, 61991–62005. doi:10.1109/ACCESS.2024.3391371.
[6] Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. doi:10.1023/B:VISI.0000029664.99615.94.
[7] Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110(3), 346–359. doi:10.1016/j.cviu.2007.09.014.
[8] Tsolakidis, D.G., Kosmopoulos, D.I., Papadourakis, G. (2014). Plant Leaf Recognition Using Zernike Moments and Histogram of Oriented Gradients. Artificial Intelligence: Methods and Applications. SETN 2014, Lecture Notes in Computer Science, 8445, Springer, Cham, Switzerland. doi:10.1007/978-3-319-07064-3_33.
[9] Hossain, S., Mou, R. M., Hasan, M. M., Chakraborty, S., & Razzak, M. A. (2018). Recognition and detection of tea leaf’s diseases using support vector machine. 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), 150–154. doi:10.1109/cspa.2018.8368703.
[10] Muralidharan, R., & Chandrasekar, C. (2011). Object recognition using SVM-KNN based on geometric moment invariant. International Journal of Computer Trends and Technology, 1(1), 215-220.
[11] Qadri, S. A. A., Huang, N. F., Wani, T. M., & Bhat, S. A. (2025). Advances and Challenges in Computer Vision for Image-Based Plant Disease Detection: A Comprehensive Survey of Machine and Deep Learning Approaches. IEEE Transactions on Automation Science and Engineering, 22, 2639–2670. doi:10.1109/TASE.2024.3382731.
[12] Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. doi:10.1186/s40537-021-00444-8.
[13] Hari, S. S., Sivakumar, M., Renuga, P., karthikeyan, S., & Suriya, S. (2019). Detection of Plant Disease by Leaf Image Using Convolutional Neural Network. 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 1–5. doi:10.1109/vitecon.2019.8899748.
[14] Zuhdi, F., Rambe, K. R., & Rahmadona, L. (2022). Analysis of Competitiveness and Forecasting of Indonesian Tea Exports to Main Destination Countries. Media Ekonomi Dan Manajemen, 37(2), 240. doi:10.24856/mem.v37i2.2888.
[15] Wang, F., Zhang, B., Wen, D., Liu, R., Yao, X., Chen, Z., Mu, R., Pei, H., Liu, M., Song, B., & Lu, L. (2022). Chromosome-scale genome assembly of Camellia sinensis combined with multi-omics provides insights into its responses to infestation with green leafhoppers. Frontiers in Plant Science, 13, 1004387. doi:10.3389/fpls.2022.1004387.
[16] Fornasiero, D., Pavan, F., Pozzebon, A., Picotti, P., & Duso, C. (2016). Relative infestation level and sensitivity of grapevine cultivars to the leafhopper empoasca vitis (Hemiptera: Cicadellidae). Journal of Economic Entomology, 109(1), 416–425. doi:10.1093/jee/tov313.
[17] Krichen, M. (2023). Convolutional Neural Networks: A Survey. Computers, 12(8), 151. doi:10.3390/computers12080151.
[18] Tran, D. T., Iosifidis, A., & Gabbouj, M. (2018). Improving efficiency in convolutional neural networks with multilinear filters. Neural Networks, 105, 328–339. doi:10.1016/j.neunet.2018.05.017.
[19] Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, 1–6. doi:10.1109/ICEngTechnol.2017.8308186.
[20] Ghiasi-Shirazi, K. (2019). Generalizing the Convolution Operator in Convolutional Neural Networks. Neural Processing Letters, 50(3), 2627–2646. doi:10.1007/s11063-019-10043-7.
[21] Xu, W., He, J., Shu, Y., & Zheng, H. (2020). Advances in Convolutional Neural Networks. Advances and Applications in Deep Learning, IntechOpen, London, United Kingdom. doi:10.5772/intechopen.93512.
[22] Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57(4), 99. doi:10.1007/s10462-024-10721-6.
[23] Yoo, J., & Kang, S. (2023). Class-Adaptive Data Augmentation for Image Classification. IEEE Access, 11, 26393–26402. doi:10.1109/ACCESS.2023.3258179.
[24] Haryono, K. B., Purnomo, H. C., Ferdinand, R., Lucky, H., & Suhartono, D. (2022). Investigating the Influence of Layers Towards Speed and Accuracy of Neural Networks. 2022 International Conference on Informatics Electrical and Electronics (ICIEE), 1–6. doi:10.1109/iciee55596.2022.10010322.
[25] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. doi:10.1145/3065386.
[26] Ramdan, A., Suryawati, E., Kusumo, R. B. S., Pardede, H. F., Mahendra, O., Dahlan, R., Fauziah, F., & Syahrian, H. (2019). Deep CNNBased Detection for Tea Clone Identification. Jurnal Elektronika Dan Telekomunikasi, 19(2), 45. doi:10.14203/jet.v19.45-50.
[27] Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. International conference on machine learning, 6-11 July, 2015, Lille, France.
[28] Mikolajczyk, A., & Grochowski, M. (2018). Data augmentation for improving deep learning in image classification problem. 2018 International Interdisciplinary PhD Workshop (IIPhDW), 117–122. doi:10.1109/iiphdw.2018.8388338.
[29] Reghunath, A., Nair, S. V., & Shah, J. (2019). Deep learning based Customized Model for Features Extraction. 2019 International Conference on Communication and Electronics Systems (ICCES), 1406–1411. doi:10.1109/icces45898.2019.9002299.
[30] Basha, S. H. S., Dubey, S. R., Pulabaigari, V., & Mukherjee, S. (2020). Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing, 378, 112–119. doi:10.1016/j.neucom.2019.10.008.
[31] Chen, H. (2023). Studies Advanced in Image Classification based on Deep Learning. Applied and Computational Engineering, 8(1), 623–628. doi:10.54254/2755-2721/8/20230287.
[32] Mukti, I. Z., & Biswas, D. (2019). Transfer Learning Based Plant Diseases Detection Using ResNet50. 2019 4th International Conference on Electrical Information and Communication Technology (EICT), 1–6. doi:10.1109/eict48899.2019.9068805.
[33] Hassan, S. M., & Maji, A. K. (2024). Pest Identification Based on Fusion of Self-Attention with ResNet. IEEE Access, 12, 6036–6050. doi:10.1109/ACCESS.2024.3351003.
[34] Sumit, S. S., Anavatti, S., Tahtali, M., Mirjalili, S., & Turhan, U. (2024). ResNet-Lite: On Improving Image Classification with a Lightweight Network. Procedia Computer Science, 246(C), 1488–1497. doi:10.1016/j.procs.2024.09.597.
[35] Andrew, J., Eunice, J., Popescu, D. E., Chowdary, M. K., & Hemanth, J. (2022). Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications. Agronomy, 12(10), 2395. doi:10.3390/agronomy12102395.
[36] Kanda, P. S., Xia, K., Kyslytysna, A., & Owoola, E. O. (2022). Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks. Plants, 11(21), 2935. doi:10.3390/plants11212935.
[37] Palma, D., Blanchini, F., & Montessoro, P. L. (2022). A system-theoretic approach for image-based infectious plant disease severity estimation. PLOS ONE, 17(7), e0272002. doi:10.1371/journal.pone.0272002.
[38] Fahmi, H., Zarlis, M., Mawengkang, H., Zendrato, N., & Sulindawaty. (2019). The Using of Thresholding and Region Merging Algorithm for Correcting the Multiple Choice Answer Sheets. Journal of Physics: Conference Series, 1255(1), 12047. doi:10.1088/1742-6596/1255/1/012047.
[39] Ahmad, N., Asif, H. M. S., Saleem, G., Younus, M. U., Anwar, S., & Anjum, M. R. (2021). Leaf Image-Based Plant Disease Identification Using Color and Texture Features. Wireless Personal Communications, 121(2), 1139–1168. doi:10.1007/s11277-021-09054-2.
[40] Nasra, P., & Gupta, S. (2024). ResNet50: Deep Learning Method for Automated Detection of Banana Leaf Spot Diseases. 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC), 1–6. doi:10.1109/icicec62498.2024.10808847.
[41] Jain, E., & Sharma, P. (2024). Advanced Detection of Bean Leaf Disease with ResNet50: A Cutting-Edge Method for Precise Classification. 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART), 79–84. doi:10.1109/smart63812.2024.10882591.
[42] Senthil Pandi, S., Suguna Devi, R., Bala Subramanian, C., & Kannaiah, S. K. (2024). Automatic Plant Leaf Disease Detection using AlexNet and Image Processing Techniques. 2024 International Conference on Cybernation and Computation, CYBERCOM 2024, 441–446. doi:10.1109/CYBERCOM63683.2024.10803225.
[43] Bharti, R., Srivastava, V., Bajpai, A., & Sahu, S. (2024). Comparative Analysis of Potato Leaf Disease Classification Using CNN and ResNet50. 2024 International Conference on Data Science and Its Applications (ICoDSA), 87–91. doi:10.1109/icodsa62899.2024.10651649.
[44] Arora, U., Mishra, U., Singh, S., & Singh, V. (2024). Comparative analysis of VGG16, Inception V4, AlexNet, and ResNet 50 for Plant Disease Identification. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–7. doi:10.1109/icccnt61001.2024.10725169.
- This work (including HTML and PDF Files) is licensed under a Creative Commons Attribution 4.0 International License.
