# Image Quality Assessment for Foliar Disease Identification (AgroPath)

arXiv: https://arxiv.org/abs/2209.12443 · HTML: https://mlsanity.com/paper/2209.12443/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 50 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [An open access repository of images on plant health to enable the development of mobile disease diagnostics](https://mlsanity.com/paper/1511.08060/) (2015)
- [DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning](https://mlsanity.com/paper/1810.05726/) (2018)
- [Using Transfer Learning for Image-Based Cassava Disease Detection](https://mlsanity.com/paper/1707.03717/) (2017)
- [Leaf Image-based Plant Disease Identification using Color and Texture Features](https://mlsanity.com/paper/2102.04515/) (2021)

## Built on by (most cited first, 4 of 4)

- [MetaQAP - A Meta-Learning Approach for Quality-Aware Pretraining in Image Quality Assessment](https://mlsanity.com/paper/2506.16601/) (2025)
- [Enhancing Wireless Device Identification through RF Fingerprinting: Leveraging Transient Energy Spectrum Analysis](https://mlsanity.com/paper/2506.17439/) (2025)
- [Attention-Based Ensemble Learning for Crop Classification Using Landsat 8-9 Fusion](https://mlsanity.com/paper/2506.18321/) (2025)
- [A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement](https://mlsanity.com/paper/2506.18323/) (2025)

## Abstract (arXiv)

Crop diseases are a major threat to food security and their rapid identification is important to prevent yield loss. Swift identification of these diseases are difficult due to the lack of necessary infrastructure. Recent advances in computer vision and increasing penetration of smartphones have paved the way for smartphone-assisted disease identification. Most of the plant diseases leave particular artifacts on the foliar structure of the plant. This study was conducted in 2020 at Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan to check leaf-based plant disease identification. This study provided a deep neural network-based solution to foliar disease identification and incorporated image quality assessment to select the image of the required quality to perform identification and named it Agricultural Pathologist (Agro Path). The captured image by a novice photographer may contain noise, lack of structure, and blur which result in a failed or inaccurate diagnosis. Moreover, AgroPath model had 99.42% accuracy for foliar disease identification. The proposed addition can be especially useful for application of foliar disease identification in the field of agriculture.