Device-friendly Guava fruit and leaf disease detection using deep learning

TL;DR

This paper develops a deep learning-based system for detecting guava fruit and leaf diseases that is optimized for end-user devices through model quantization, achieving high accuracy with minimal model size.

Contribution

It introduces a novel approach combining CNNs with quantization techniques to create device-friendly disease detection models for guava plants.

Findings

Quantized GoogleNet achieved 97% accuracy at 0.143 MB size.

EfficientNet achieved 99% accuracy at 4.2 MB size.

Models are suitable for deployment on resource-constrained devices.

Abstract

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.