Deep Metric Learning with Augmented Latent Fusion and Response-Based Knowledge Distillation on Edge Device for Paddy Pests and Disease Identification - Darmawan, Hendri; Yuliana, Mike; Hadi, Mochammad Zen Samsono; Sangaiah, Arun Kumar (Society of Visual Informatics, 2024)
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.