# Rice paddy disease classifications using CNNs

arXiv: https://arxiv.org/abs/2303.08415 · HTML: https://mlsanity.com/paper/2303.08415/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks](https://mlsanity.com/paper/1812.01043/) (2018)
- [A System for Automatic Rice Disease Detection from Rice Paddy Images Serviced via a Chatbot](https://mlsanity.com/paper/2011.10823/) (2020)

## Abstract (arXiv)

Rice is a staple food in the world's diet, and yet huge percentages of crop yields are lost each year to disease. To combat this problem, people have been searching for ways to automate disease diagnosis. Here, we extend on previous modelling work by analysing how disease-classification accuracy is sensitive to both model architecture and common computer vision techniques. In doing so, we maximise accuracy whilst working in the constraints of smaller model sizes, minimum GPUs and shorter training times. Whilst previous state-of-the-art models had 93% accuracy only predicting 5 diseases, we improve this to 98.7% using 10 disease classes.