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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
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| Paper Title: | DeepLeaf-X: Cross-Dataset Plant Disease Detection Using EfficientNet |
| Authors Name: | S Mahammed Shahid , S Mahamad Shakeer , K Maheswari , M Murali Priya |
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IJRTI_211302
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| Published Paper Id: | IJRTI2604256 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Automated detection of diseases in plants from leaf images is a growing need in the context of modern precision agriculture, where disease detection at early stages can help prevent significant loss in crops. However, existing deep learning- based solutions are mostly designed to detect a particular type of crop or a set of diseases, which makes them less applicable in real-world scenarios where multiple crops are involved.” In this paper, the authors propose a framework called EfficientCropNet, which uses a two-stage fine-tuned transfer learning model based on EfficientNetB3 for the simultaneous detection of 13 different disease and health states in three important crops: Apple, Corn, and Tomato. In the proposed framework, the authors use a robust multi-source consolidation of PlantVillage and FieldPlant datasets, along with class-balanced sampling with a maximum of 2,000 images for a single class, and multiple augmentations for a training set comprising 27,239 images.In the training process, the network utilizes a two-stage training strategy, where in Stage 1, the network focuses on training the custom classification head with the backbone network frozen, and in Stage 2, the last two groups of EfficientNetB3 convolutional blocks are unfrozen for domain-specific fine-tuning. The performance of the proposed EfficientCropNet has been evaluated on a validation set of 4,535 images, where the network has achieved an accuracy of 99.78%, a macro F1-score of 0.99, and weighted-average precision and recall of 1.00, indicating the performance of the proposed network is comparable to the state-of-the-art single- crop ensemble approaches, with the advantage of the simpler single backbone deployment architecture of the proposed network for precision agriculture |
| Keywords: | Plant disease detection, EfficientNetB3, transfer learning, fine-tuning, multi-crop classification, deep learning, precision agriculture, convolutional neural networks |
| Cite Article: | "DeepLeaf-X: Cross-Dataset Plant Disease Detection Using EfficientNet", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b872-b878, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604256.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604256
Registration ID:211302
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b872-b878 Country: Nandyal, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604256 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604256 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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