Device-friendly Guava fruit and leaf disease detection using deep learning
This work addresses device-friendly disease detection for guava farmers, but it is incremental as it applies existing quantization techniques to standard CNNs.
The paper tackled plant disease detection for guava using deep learning, achieving 97% accuracy with a 0.143 MB quantized GoogleNet model and 99% accuracy with a 4.2 MB EfficientNet model.
This work presents a deep learning-based plant disease diagnostic system using images of fruits and leaves. Five state-of-the-art convolutional neural networks (CNN) have been employed for implementing the system. Hitherto model accuracy has been the focus for such applications and model optimization has not been accounted for the model to be applicable to end-user devices. Two model quantization techniques such as float16 and dynamic range quantization have been applied to the five state-of-the-art CNN architectures. The study shows that the quantized GoogleNet model achieved the size of 0.143 MB with an accuracy of 97%, which is the best candidate model considering the size criterion. The EfficientNet model achieved the size of 4.2MB with an accuracy of 99%, which is the best model considering the performance criterion. The source codes are available at https://github.com/CompostieAI/Guava-disease-detection.