Detecting Pests and Diseases in Plants Using Efficient Network
Abstract
The agricultural sector in Indonesia is still faced with low agrarian production caused by pests and diseases. Therefore, agricultural land that is still vulnerable to pests but can detect the development of pest attacks must be designed. This study uses the PlantVillage dataset. The dataset will go through the preprocessing stage for dimension adjustment, and then the result will be used for building the network. The results are evaluated using a confusion matrix and showed that the convolutional neural network performs well in image processing and obtains architectural optimization in its field. The method we propose is an Efficient Network by selecting the correct input size. Implementing an Efficient Network in the convolutional neural network architecture increases its F1-score to 93%, indicating that Efficient Network has a higher F1-Score than the baseline convolution neural network. Implementing this network architecture can quickly increase the CNN baseline to a more varied target resource while maintaining the efficiency of the resulting model.
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[3] A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, and J. Garcia-Rodriguez, "A Review on Deep Learning Techniques Applied to Semantic Segmentation" pp. 1–23, 2017, [Online]. Available: http://arxiv.org/abs/1704.06857.
[4] S. Srinivas, R. K. Sarvadevabhatla, K. R. Mopuri, N. Prabhu, S. S. S. Kruthiventi, and R. V. Babu, "A taxonomy of deep convolutional neural nets for computer vision" Frontiers in Robotics and AI, vol. 2, no. JAN, pp. 1–13, 2016, doi: 10.3389/frobt.2015.00036.
[5] B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, "Object detectors emerge in deep scene CNNs" in 3rd International Conference Learning Representative ICLR 2015 [Online]. Available: file:///D:/COLLAGE/6 Semester/Skripsi/ref/Nov-w1/Object Detectors Emerge in Deep Scene CNNs.pdf.
[6] W. Li et al., "Classification of High-Spatial-Resolution Remote Sensing Scenes Method Using Transfer Learning and Deep Convolutional Neural Network" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, doi: 10.1109/JSTARS.2020.2988477.
[7] D. K. Das et al., "Computer aided tool for automatic detection and delineation of nucleus from oral histopathology images for OSCC screening" Applied Soft Computing, Vol.83, 2019, doi: 10.1016/j.asoc.2019.105642.
[8] C. Dong, C. C. Loy, and X. Tang, "Accelerating the Super - Resolution Convolutional Neural Network SR for Different Upscaling Factors" Eccv, 2016.
[9] M. Tan and Q. V. Le, "EfficientNet: Rethinking model scaling for convolutional neural networks" in 36th International Conference on Machine Learning, ICML 2019, 2019, vol. 2019-June, pp. 10691–10700.
[10] Y. Toda and F. Okura, "How convolutional neural networks diagnose plant disease" Plant Phenomics, vol. 2019, 2019, doi: 10.34133/2019/9237136.
[11] N. Sharma, V. Jain, and A. Mishra, "An Analysis of Convolutional Neural Networks for Image Classification" Procedia Computer Science, vol. 132, no. Iccids, pp. 377–384, 2018, doi: 10.1016/j.procs.2018.05.198.
[12] M. Tan and Q. V Le, "EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling" The latest from Google Research, 2019.
[13] G. Marques, D. Agarwal, and I. de la Torre Díez, "Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network" Applied Soft Computing, vol. 96, p. 106691, November 2020, doi: 10.1016/j.asoc.2020.106691.
[14] P. Tm, A. Pranathi, K. Saiashritha, N. B. Chittaragi, and S. G. Koolagudi, "Tomato Leaf Disease Detection Using Convolutional Neural Networks" in 2018 Eleventh International Conference on Contemporary Computing (IC3), pp. 2–4, 2018, doi: 10.1109/IC3.2018.8530532.
[15] M. Francis and C. Deisy, "Disease Detection and Classification in Agricultural Plants Using Convolutional Neural Networks - A Visual Understanding" in 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 1063–1068, 2019, doi: 10.1109/SPIN.2019.8711701.
[16] K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis" Computers and Electronics in Agriculture, vol. 145, no. September 2017, pp. 311–318, 2018, doi: 10.1016/j.compag.2018.01.009.
[17] Y. Huang et al., "GPipe: Efficient training of giant neural networks using pipeline parallelism" NIPS'19: Proceedings of the 33rd International Conference on Neural Information Processing Systems, [Online]. Available: https://dl.acm.org/doi/pdf/10.5555/3454287.3454297.
[18] S. Kornblith, J. Shlens, and Q. V Le, "Do Better ImageNet Models Transfer Better?" in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2661–2671, 2019, doi: 10.1109/CVPR.2019.00277.
[19] J. Wang, Q. Liu, H. Xie, Z. Yang, and H. Zhou, "Boosted efficientnet: Detection of lymph node metastases in breast cancer using convolutional neural networks" Cancers (Basel), vol. 13, no. 4, pp. 1–14, February 2021, doi: 10.3390/cancers13040661.
[20] Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, "Plant leaf disease classification using EfficientNet deep learning model" Ecological Informatics, vol. 61, p. 101182, March 2021, doi: 10.1016/j.ecoinf.2020.101182.
[21] B. Baheti, S. Innani, S. Gajre, and S. Talbar, "Eff-UNet: A novel architecture for semantic segmentation in unstructured environment" in IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), vol. 2020-June, pp. 1473–1481, 2020, doi: 10.1109/CVPRW50498.2020.00187.
[22] P. Chujai, K. Chomboon, P. Teerarassamee, N. Kerdprasop, and K. Kerdprasop, "Ensemble Learning For Imbalanced Data Classification Problem" in International Conference on Industrial Application Engineering, January 2015, pp. 449–456, 2015, doi: 10.12792/iciae2015.079.
[23] H. J. Xing and W. T. Liu, "Robust AdaBoost based ensemble of one-class support vector machines" Information Fusion, vol. 55, no. July 2019, pp. 45–58, 2020, doi: 10.1016/j.inffus.2019.08.002.
[24] B. Krawczyk, A. Cano, and M. Wozniak, "Selecting local ensembles for multi-class imbalanced data classification" in International Joint Conference on Neural Networks (IJCNN), vol. 2018-July, 2018, doi: 10.1109/IJCNN.2018.8489572.
[25] G. Douzas, F. Bacao, and F. Last, "Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE", Information Sciences, vol. 465, pp. 1–20, 2018, doi: 10.1016/j.ins.2018.06.056.
[26] G. Geetharamani and A. P. J., "Identification of plant leaf diseases using a nine-layer deep convolutional neural network" Computers & Electrical Engineering, vol. 76, pp. 323–338, 2019, doi: 10.1016/j.compeleceng.2019.04.011.
[27] G. Rostami, M. Hamid, and H. Jalaeikhoo, "Impact of the BCR-ABL1 fusion transcripts on different responses to Imatinib and disease recurrence in Iranian patients with Chronic Myeloid Leukemia" Gene, vol. 627, no. 1, pp. 202–206, 2017, doi: 10.1016/j.gene.2017.06.018.
[28] A. A. J. E. V. Priyangka and I. M. S. Kumara, "Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method" Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 2, p. 123, 2021, doi: 10.24843/lkjiti.2021.v12.i02.p06.
[29] I. M. Mika Parwita and D. Siahaan, "Classification of Mobile Application Reviews using Word Embedding and Convolutional Neural Network" Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, vol. 10, no. 1, p. 1, 2019, doi: 10.24843/lkjiti.2019.v10.i01.p01.
[30] T. Xiao, J. Zhang, K. Yang, Y. Peng, and Z. Zhang, "Error-driven incremental learning in deep convolutional neural network for large-scale image classification" MM '14: Proceedings of the 22nd ACM international conference on Multimedia, pp. 177–186, 3 November 2014, doi: 10.1145/2647868.2654926.
Published
2023-08-30
How to Cite
HAYATY, Mardhiya; MAHISSANULAR, Timur Haryo.
Detecting Pests and Diseases in Plants Using Efficient Network.
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 14, n. 2, p. 71-79, aug. 2023.
ISSN 2541-5832.
Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/83708>. Date accessed: 24 nov. 2024.
doi: https://doi.org/10.24843/LKJITI.2023.v14.i02.p01.
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