International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Survey on Mango Leaf Disease Detection Using Convolutional Neural Networks |
|---|---|
| ABSTRACT | This study investigates the application of Convolutional Neural Networks (CNNs) for identifying mango leaf diseases such as Anthracnose, Powdery Mildew, Bacterial Canker, Die Back, and Leaf Spot, all of which severely impact mango yield and fruit quality. It reviews existing literature and approaches to understand how CNN-based models automatically learn and capture discriminative features from leaf images, including color variations, texture details, leaf morphology, and lesion patterns caused by diseases. Different CNN architectures, including AlexNet, VGG, ResNet, and EfficientNetV2, are analyzed and compared based on their classification accuracy, training efficiency, and scalability for large-scale agricultural applications. Reported results show that AlexNet achieves approximately 85–90% accuracy, VGG networks reach 88–93%, ResNet models perform between 92–96%, while EfficientNetV2 delivers the highest performance with about 95–98% accuracy. The study emphasizes the benefits of CNN-based approaches for mango leaf disease detection, such as higher accuracy compared to traditional manual inspection, early-stage disease detection capability, and suitability for deployment in large orchards. By reviewing recent advancements, this study provides insights into how deep learning techniques can enhance disease monitoring, guide future research, and support the development of automated systems for sustainable mango cultivation. |
| AUTHOR | NEHA P. VAIDYA, PROF. DR. B. D. PHULPAGAR M. Tech Student, Department of Computer Engineering, P.E.S. Modern College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India Department of Computer Engineering, P.E.S. Modern College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405097 |
| pdf/97_Survey on Mango Leaf Disease Detection Using Convolutional Neural Networks.pdf | |
| KEYWORDS | |
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