# An Ensemble Deep Learning Approach for Reliable and Scalable Lemon Leaf Disease Classification

arXiv: https://arxiv.org/abs/2606.14871 · HTML: https://mlsanity.com/paper/2606.14871/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 12 Jun 2026.

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

- [Enhancing Tea Leaf Disease Recognition with Attention Mechanisms and Grad-CAM Visualization](https://mlsanity.com/paper/2512.17987/) (2025)
- [Healthy Harvests: A Comparative Look at Guava Disease Classification Using InceptionV3](https://mlsanity.com/paper/2602.10967/) (2026)
- [Lemon and Orange Disease Classification using CNN-Extracted Features and Machine Learning Classifier](https://mlsanity.com/paper/2408.14206/) (2024)
- [Toward Reliable and Explainable Nail Disease Classification: Leveraging Adversarial Training and Grad-CAM Visualization](https://mlsanity.com/paper/2602.04820/) (2026)

## Abstract (arXiv)

Early detection of plant diseases is crucial to plants and for the farmers. Plant diseases reduce fruit yield and quality, and plants are more susceptible to other stresses when they are infected. The lemon leaf disease dataset contains 1354 images. The dataset has 9 classes. Among the 9 classes only one class is for healthy leaf, and the other 8 classes are leaf diseases. The dataset was split into training (70%), testing (15%) and validation (15%) sets after comprehensive preprocessing. Two pretrained models (InceptionV3 and MobileNetV2) were applied and then combined these models using an ensemble technique to boost robustness. Ensemble models showed a promising performance of 99.27% accuracy. Adversarial Training is applied to improve models' ability and ensure reliable predictions under noisy data. Grad-CAM visualization highlights the important regions of leaf images that validate the model prediction with confidence level.