Multiple crop disease detection using EfficientNetB0 – a CNN model
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Ritesh Kumar Kushwaha
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Meenakshi Munjal
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Sania Chauhan
Abstract
Agriculture, the backbone of livelihood as well as the global food security system. Nowadays, it is heavily affected by several diseases. Manually detecting and recognizing diseases in crops in an extensive range is a complicated task, as it requires a great deal of labor and vast experience. To prevent damage to large-scale productions, this project aims to propose the use of Deep Learning approach. A powerful CNN model used to extract features from the dataset provided, and based on that model trained and evaluated. This image segmenting technique used in the model evaluation on the parameters-accuracy, recall, precision, & F1-score and differentiating the disease plants and the healthy ones. The trained model provided the tremendous 99.9 % accuracy in detecting wheat diseases. This approach will help in further advancements as well as help the farmers in safeguarding the crops.
Acknowledgments
The authors would like to thank the Chandigrah University and colleagues who contributed insights and feedback throughout the course of this research.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Ritesh Kumar Kushwaha: Prepared the manuscript draft and incorporated technical feedback from coauthors. Meenakshi Munjal: Conducted joint evaluation and validation of results in collaboration with the research team. Sania Chauhan: Developed and implemented the computational code for experiments. Devanshu Saini: Performed the literature review and drafted the introduction section.
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Use of Large Language Models, AI and Machine Learning Tools: Generative AI tools, including ChatGPT, were used solely to assist with language improvement and figure formatting. All scientific content, analysis, and conclusions were developed independently by the author.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: 1. High Accuracy in Disease Detection: A deep learning-based CNN model achieved 99.9 % accuracy in detecting wheat diseases from images, significantly outperforming manual methods. 2. Effective Feature Extraction: The CNN model successfully extracted discriminative features from crop images, enabling precise differentiation between diseased and healthy plants. 3. Robust Performance Metrics: The model was evaluated using precision, recall, and F1-score, demonstrating high reliability in disease classification. 4. Automation Advantage: The proposed approach eliminates dependency on labor-intensive manual inspection, offering a scalable solution for large-scale agricultural monitoring. 5. Practical Impact: This system aids farmers in early disease detection, mitigating crop losses and enhancing global food security.
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Data availability: Not applicable.
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