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

arXiv: https://arxiv.org/abs/2209.12557 · HTML: https://mlsanity.com/paper/2209.12557/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [Deep Learning on Mobile Devices - A Review](https://mlsanity.com/paper/1904.09274/) (2019)
- [Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network](https://mlsanity.com/paper/1903.10356/) (2019)

## Built on by (most cited first, 2 of 2)

- [Healthy Harvests: A Comparative Look at Guava Disease Classification Using InceptionV3](https://mlsanity.com/paper/2602.10967/) (2026)
- [A Novel CNN Gradient Boosting Ensemble for Guava Disease Detection](https://mlsanity.com/paper/2512.19989/) (2025)

## Abstract (arXiv)

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.