Deep Metric Learning with Augmented Latent Fusion and Response-Based Knowledge Distillation on Edge Device for Paddy Pests and Disease Identification

Hendri Darmawan - Politeknik Elektronika Negeri Surabaya, Indonesia
Mike Yuliana - Politeknik Elektronika Negeri Surabaya, Indonesia
Mochammad Zen Hadi - Politeknik Elektronika Negeri Surabaya, Indonesia
Arun Sangaiah - National Yunlin University of Science and Technology, Taiwan


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3-2.3104

Abstract


The health of paddy fields significantly impacts rice yields and the economic stability of farmers. Limited number of experts available to watch these issues poses a challenge. Consequently, a reliable diagnostic system is necessary to find pests and diseases in rice crops. In this study, we propose deep metric learning with augmented latent fusion (FADMAKA) combined with a response-based knowledge distillation (KD) approach. The student model, which processes single RGB input images, is trained using soft latent labels derived from four augmented input from the teacher model. Our method delivers a high validation accuracy of 0.973, keeps an accuracy of 0.782 on the unseen data, and with rapid inference time of 38.911 milliseconds. This approach’s accuracy outperforms SoftMax deep learning classification with fine-tuning, which only has a maximum accuracy of 0.739 on the unseen data with computation time of 36.224 ms, and the DML with augmented latent fusion with k-NN classifier on the same base model, which achieves an accuracy of 0.78 with computation time of 124.977 ms. Our proposed model has 0.12 giga floating point operations per second (GFLOPs) that is suitable for edge devices with low computational resources. Following the modeling phase, we deployed the highest-accuracy student model to a Raspberry Pi 4B device equipped with a camera. This system can provide biological agent-based recommendations for identified pest and disease threats in rice fields. Our approach not only improved accuracy but also proved efficiency, enabling farmers to identify pests and disease without relying on internet connectivity. 

Keywords


Knowledge distillation; Deep metric learning; Model compression; Paddy plant pests and disease

Full Text:

PDF

References


L.-D. Quach, Q. K. Nguyen, Q. A. Nguyen, and L. T. T. Lan, “Rice pest dataset supports the construction of smart farming systems,” Data in Brief, vol. 52. p. 110046, 2024.

L. Listihani, P. E. P. Ariati, ‪I G. A. D. Yuniti, and ‪Dewa G. W. Selangga, “The brown planthopper (Nilaparvata lugens) attack and its genetic diversity on rice in Bali, Indonesia,” Biodiversitas Journal of Biological Diversity. 2022.‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

S. Jesie and Dr. M. S. G. Premi, “A Review on Machine Learning to Detect and Classify Paddy Leaf Disease,” 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT). pp. 1822–1828, 2023.

M. Shoaib, B. Shah, S. EI-Sappagh, A. Ali, A. Ullah, F. Alenezi, T. Gechev, T. Hussain, and F. Ali, “An advanced deep learning models-based plant disease detection: A review of recent research,” Frontiers in Plant Science, vol. 14. Frontiers, p. 1158933, 2023.

S. Kurzadkar, A. Meshram, A. Barve, K. Dhargave, M. Alone, and V. Bhongale, “Plant Leaves Disease Detection System Using Machine Learning,” International Journal of Computer Science and Mobile Computing. 2022.

H. Darmawan, M. Yuliana, and Moch. Z. S. Hadi, “Cloud-based Paddy Plant Pest and Disease Identification using Enhanced Deep Metric Learning and k-NN Classification with Augmented Latent Fusion,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 6. pp. 158–170, 2023.

H. Ni, Z. Shi, S. Karungaru, S. Lv, X. Li, X. Wang, and J. Zhang, “Classification of Typical Pests and Diseases of Rice Based on the ECA Attention Mechanism,” Agriculture, vol. 13, no. 5. 2023.

C. R. Rahman, P. S. Arko, M. E. Ali, M. A. Iqbal Khan, S. H. Apon, F. Nowrin, and A. Wasif, “Identification and recognition of rice diseases and pests using convolutional neural networks,” Biosystems Engineering, vol. 194. pp. 112–120, 2020.

V. Malathi and M. P. Gopinath, “Classification of pest detection in paddy crop based on transfer learning approach,” Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, vol. 71, no. 7. Taylor & Francis, pp. 552–559, 2021.

P. A, M. D, and B. K. S, “PaddyNet: An Improved Deep Convolutional Neural Network for Automated Disease Identification on Visual Paddy Leaf Images,” International Journal of Advanced Computer Science and Applications. 2023.

S. Ramesh and D. Vydeki, “Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm,” Information Processing in Agriculture, vol. 7. pp. 249–260, 2020.

Y. Lu, Z. Li, X. Zhao, S. Lv, X. Wang, K. Wang, and H. Ni, “Recognition of Rice Sheath Blight Based on a Backpropagation Neural Network,” Electronics, vol. 10, no. 23. 2021.

B. Ciapas and P. Treigys, “Self-Checkout Product Class Verification using Center Loss approach,” Computer Science Research Notes. 2023.

H. Darmawan, M. Yuliana, and Moch. Z. Samsono Hadi, “Realtime Weather Prediction System Using GRU with Daily Surface Observation Data from IoT Sensors,” 2022 International Electronics Symposium (IES), pp. 221–226, 2022,

A. Alkhulaifi, F. Alsahli, and I. Ahmad, “Knowledge Distillation in Deep Learning and Its Applications,” PeerJ Comput Sci, vol. 7. p. e474, 2021.

Y. Ma, Q. Hua, Z. Wen, R. Zhang, Y. Zhang, and H. Li, “k Nearest Neighbor Similarity Join Algorithm on High-Dimensional Data Using Novel Partitioning Strategy,” Security and Communication Networks, vol. 2022, no. 1. p. 1249393, 2022.

F. Dang, D. Chen, Y. Lu, and Z. Li, “YOLOWeeds: A novel benchmark of YOLO object detectors for multiclass weed detection in cotton production systems,” Computers and Electronics in Agriculture, vol. 205. p. 107655, 2023.

L. Zhao and L. Wang, “A new lightweight network based on MobileNetV3,” KSII Trans. Internet Inf. Syst., vol. 16. pp. 1–15, 2022.

D. Shi, M. Orouskhani, and Y. Orouskhani, “A conditional Triplet loss for few-shot learning and its application to image cosegmentation,” Neural networks: the official journal of the International Neural Network Society, vol. 137. pp. 54–62, 2021.

H. Darmawan, M. Yuliana, and Moch. Z. S. Hadi, “GRU and XGBoost Performance with Hyperparameter Tuning Using GridSearchCV and Bayesian Optimization on an IoT-Based Weather Prediction System,” International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 3. INSIGHT - Indonesian Society for Knowledge and Human Development, pp. 851–862, 2023.

M. D. S. Antariksa, A. Y. Husodo, R. B. Huwae, and R. A. Nugraha, “Design and Development of Smart Farming System for Monitoring and Bird Pest Control Based on Raspberry Pi 4 with Implementation of YOLOv5 Algorithm,” 2023 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS). pp. 1–6, 2023.

N. James, L.-Y. Ong, and M. Leow, “Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster,” Future internet, vol. 14. p. 220, 2022.

E. Prasetyo, R. Purbaningtyas, and R. D. Adityo, “Cosine K-Nearest Neighbor in Milkfish Eye Classification,” International Journal of Intelligent Engineering and Systems, vol. 13, no. 3. Infonomics Society, Surabaya, Indonesia, p. 2020, 2020.

P. Ramachandran, T. Eswarlal, M. Lehman, and Z. Colbert, “Assessment of Optimizers and their Performance in Autosegmenting Lung Tumors,” Journal of Medical Physics, vol. 48, no. 2. pp. 129–135, Apr. 2023.

S. P. Singh, L. Wang, S. Gupta, H. Goli, P. Padmanabhan, and B. Gulyás, “3D Deep Learning on Medical Images: A Review,” Sensors, vol. 20, no. 18. MDPI, p. 5097, 2020.

E. Boateng, J. Otoo, and D. Abaye, “Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review,” Journal of Data Analysis and Information Processing, vol. 8, pp. 341–357, 2020.

N. Mahony, S. Campbell, A. Carvalho, L. Krpalkova, G. Velasco-Hernández, D. Riordan, and J. Walsh, “Understanding and Exploiting Dependent Variables with Deep Metric Learning,” in Proceedings of the International Conference on Intelligent Systems and Computing (ISC) 2020, pp. 97–113, 2020.

G. Guo and Z. Zhang, “Road damage detection algorithm for improved YOLOv5,” Scientific Reports, vol. 12, no. 1, p. 15523, 2022.

X. Ding, X. Zhang, J. Han, and G. Ding, “Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11953–11965, New Orleans, LA, USA, 2022.

L. Huang, Z. Xiang, J. Yun, Y. Sun, Y. Liu, D. Jiang, H. Ma, and H. Yu, “Target Detection Based on Two-Stream Convolution Neural Network with Self-Powered Sensors Information,” IEEE Sensors Journal, vol. 23, pp. 20681–20690, 2023.

W. Bismi, D. Riana, and A. S. Hewiz, “Disease Identification on Fig Leaf Images Using Deep Learning Method,” International Journal of Advanced Science Computing and Engineering, vol. 6, no. 2, pp. 57–63, 2024.