# AOP: An Anti-overfitting Pretreatment for Practical Image-based Plant Diagnosis

arXiv: https://arxiv.org/abs/1911.10727 · HTML: https://mlsanity.com/paper/1911.10727/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy](https://mlsanity.com/paper/2309.01903/) (2023)
- [Hierarchical Object Detection and Recognition Framework for Practical Plant Disease Diagnosis](https://mlsanity.com/paper/2407.17906/) (2024)
- [Investigation to answer three key questions concerning plant pest identification and development of a practical identification framework](https://mlsanity.com/paper/2407.18000/) (2024)
- [Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data](https://mlsanity.com/paper/2507.04216/) (2025)
- [Robust Plant Disease Diagnosis with Few Target-Domain Samples](https://mlsanity.com/paper/2510.12909/) (2025)

## Abstract (arXiv)

In image-based plant diagnosis, clues related to diagnosis are often unclear, and the other factors such as image backgrounds often have a significant impact on the final decision. As a result, overfitting due to latent similarities in the dataset often occurs, and the diagnostic performance on real unseen data (e,g. images from other farms) is usually dropped significantly. However, this problem has not been sufficiently explored, since many systems have shown excellent diagnostic performance due to the bias caused by the similarities in the dataset. In this study, we investigate this problem with experiments using more than 50,000 images of cucumber leaves, and propose an anti-overfitting pretreatment (AOP) for realizing practical image-based plant diagnosis systems. The AOP detects the area of interest (leaf, fruit etc.) and performs brightness calibration as a preprocessing step. The experimental results demonstrate that our AOP can improve the accuracy of diagnosis for unknown test images from different farms by 12.2% in a practical setting.