Fine-Tuned CNN-Based Approach for Multi-Class Mango Leaf Disease Detection

• URL: http://arxiv.org/abs/2510.05326v1
• Date: Mon, 06 Oct 2025 19:47:26 GMT
• Title: Fine-Tuned CNN-Based Approach for Multi-Class Mango Leaf Disease Detection
• Authors: Jalal Ahmmed, Faruk Ahmed, Rashedul Hasan Shohan, Md. Mahabub Rana, Mahdi Hasan,
• Abstract summary: This research examines the performance of five pre-trained convolutional neural networks, DenseNet201, InceptionV3, ResNet152V2, SeResNet152, and Xception, for multi-class identification of mango leaf diseases.<n>DenseNet201 delivered the best results, achieving 99.33% accuracy with consistently strong metrics for individual classes.
• Score: 1.057098647974782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mango is an important fruit crop in South Asia, but its cultivation is frequently hampered by leaf diseases that greatly impact yield and quality. This research examines the performance of five pre-trained convolutional neural networks, DenseNet201, InceptionV3, ResNet152V2, SeResNet152, and Xception, for multi-class identification of mango leaf diseases across eight classes using a transfer learning strategy with fine-tuning. The models were assessed through standard evaluation metrics, such as accuracy, precision, recall, F1-score, and confusion matrices. Among the architectures tested, DenseNet201 delivered the best results, achieving 99.33% accuracy with consistently strong metrics for individual classes, particularly excelling in identifying Cutting Weevil and Bacterial Canker. Moreover, ResNet152V2 and SeResNet152 provided strong outcomes, whereas InceptionV3 and Xception exhibited lower performance in visually similar categories like Sooty Mould and Powdery Mildew. The training and validation plots demonstrated stable convergence for the highest-performing models. The capability of fine-tuned transfer learning models, for precise and dependable multi-class mango leaf disease detection in intelligent agricultural applications.

Related papers

• Toward Reliable Tea Leaf Disease Diagnosis Using Deep Learning Model: Enhancing Robustness With Explainable AI and Adversarial Training [0.0]
  This study develops an automated deep learning model for tea leaf disease classification based on the teaLeafBD dataset.<n>The proposed pipeline contains data preprocessing, data splitting, adversarial training, augmentation, model training, evaluation, and comprehension.<n>Our experimental outcomes revealed that EfficientNetB3 achieved the highest classification accuracy of 93%, while DenseNet201 reached 91%.
  arXiv  Detail & Related papers  (2026-02-11T17:21:36Z)
• CBAM Integrated Attention Driven Model For Betel Leaf Diseases Classification With Explainable AI [0.48342038441006796]
  This paper presents a lightweight CBAM-CNN model with just 2.13 million parameters (8.13 MB)<n>The model's capacity to discern minute variations among leaf disease classes is improved by the integrated attention mechanism.<n>The proposed model achieved a precision of 97%, recall of 94%, and F1 score of 95%, and 95.58% accuracy on the test set.
  arXiv  Detail & Related papers  (2025-09-30T16:30:09Z)
• Transfer Learning-Based CNN Models for Plant Species Identification Using Leaf Venation Patterns [0.0]
  This study evaluates the efficacy of three deep learning architectures: ResNet50, MobileNetV2, and EfficientNetB0 for automated plant species classification based on leaf venation patterns.
  arXiv  Detail & Related papers  (2025-09-03T21:23:09Z)
• Detection and Classification of Diseases in Multi-Crop Leaves using LSTM and CNN Models [0.0]
  Plant diseases pose a serious challenge to agriculture by reducing crop yield and affecting food quality.<n>This study applies Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to classify plant leaf diseases.
  arXiv  Detail & Related papers  (2025-04-30T02:36:51Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [56.99710477905796]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas.<n>This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• SugarcaneNet: An Optimized Ensemble of LASSO-Regularized Pre-trained Models for Accurate Disease Classification [0.46180371154032906]
  sugarcaneNet2024 is a unique model that outperforms previous methods for automatically and quickly detecting sugarcane disease. Our proposed model consolidates an optimized weighted average ensemble of seven customized and LASSO-regularized pre-trained models. This optimized sugarcaneNet2024 model performed the best for detecting sugarcane diseases, having achieved accuracy, precision, recall, and F1 score of 99.67%, 100%, 100%, and 100%.
  arXiv  Detail & Related papers  (2024-03-26T11:23:08Z)
• Facilitated machine learning for image-based fruit quality assessment in developing countries [68.8204255655161]
  Automated image classification is a common task for supervised machine learning in food science. We propose an alternative method based on pre-trained vision transformers (ViTs) It can be easily implemented with limited resources on a standard device.
  arXiv  Detail & Related papers  (2022-07-10T19:52:20Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs [43.43732218093039]
  We compare a popular thoracic disease classification model, CheXNet, and a thoracic lesion detection model, CheXDet. We found incorporating external training data even led to performance degradation for CheXNet. By visualizing the models' decision-making regions, we revealed that CheXNet learned patterns other than the target lesions.
  arXiv  Detail & Related papers  (2021-04-21T14:21:37Z)
• One-Shot Learning with Triplet Loss for Vegetation Classification Tasks [45.82374977939355]
  Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks. Starting from 2015, many projects use Siamese networks and this kind of loss for face recognition and object classification.
  arXiv  Detail & Related papers  (2020-12-14T10:44:22Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.