# Leaf Image-based Plant Disease Identification using Color and Texture Features

arXiv: https://arxiv.org/abs/2102.04515 · HTML: https://mlsanity.com/paper/2102.04515/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 56 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)
- [An open access repository of images on plant health to enable the development of mobile disease diagnostics](https://mlsanity.com/paper/1511.08060/) (2015)

## Built on by (most cited first, 6 of 6)

- [Image Quality Assessment for Foliar Disease Identification (AgroPath)](https://mlsanity.com/paper/2209.12443/) (2022)
- [Multi-objective hybrid knowledge distillation for efficient deep learning in smart agriculture](https://mlsanity.com/paper/2512.22239/) (2025)
- [Development of Crop Yield Estimation Model using Soil and Environmental Parameters](https://mlsanity.com/paper/2102.05755/) (2021)
- [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)
- [Deep Learning-Based Age Estimation and Gender Deep Learning-Based Age Estimation and Gender Classification for Targeted Advertisement](https://mlsanity.com/paper/2507.18565/) (2025)

## Abstract (arXiv)

Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices. Our proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features based on the Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification. In this study, six color features and twenty-two texture features have been calculated. Support vector machines is used to perform one-vs-one classification of plant disease. The proposed model of disease identification provides an accuracy of 98.79% with a standard deviation of 0.57 on 10-fold cross-validation. The accuracy on a self-collected dataset is 82.47% for disease identification and 91.40% for healthy and diseased classification. The reported performance measures are better or comparable to the existing approaches and highest among the feature-based methods, presenting it as the most suitable method to automated leaf-based plant disease identification. This prototype system can be extended by adding more disease categories or targeting specific crop or disease categories.