A Multi-District Field-Acquired Sugarcane Disease Image Dataset From Assam For AI-Based Crop Disease Detection
DOI:
https://doi.org/10.29284/bc4sqh63Keywords:
Sugarcane disease; Image dataset; Deep learning; Precision agriculture; Assam.Abstract
Sugarcane is a major agricultural crop in Assam, India, where disease outbreaks significantly affect yield and farmer income. The development of automated disease detection systems requires region-specific, high-quality datasets. This study presents a curated dataset comprising 5,769 field-acquired sugarcane leaf images collected from multiple districts of Assam under natural environmental conditions. The dataset contains 5,769 field-acquired images categorized into nine classes representing eight disease types and healthy plants.
Images were captured following standardized acquisition protocols and annotated through a structured labeling process. Quality control measures were applied to ensure clarity and class consistency. To evaluate dataset robustness, benchmark experiments were conducted using established convolutional neural network architectures. Performance metrics including accuracy, precision, recall, and F1-score are reported.
The dataset is made publicly available to support reproducible research and AI-driven crop disease monitoring systems tailored to humid agro-climatic regions.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
