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AMCD: A multi-domain agricultural crop and flower image dataset for deep learning-based classification.
Md Ahsan Karim1, Md Tanjum An Tashrif1, Shahariar Hossain Mahir1
1Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Nayarhat, Savar, Dhaka 1340, Bangladesh.
Data in Brief
|June 30, 2026
Summary
The Agricultural Multidisciplinary Collection Dataset (AMCD) offers 5405 diverse images of agricultural specimens. This dataset supports advancements in agricultural image classification and plant analysis technologies.
Area of Science:
- Agricultural Science
- Computer Vision
- Data Science
Background:
- The agricultural sector increasingly relies on digital tools for monitoring and analysis.
- High-quality, diverse image datasets are crucial for developing robust AI models in agriculture.
Purpose of the Study:
- To introduce the Agricultural Multidisciplinary Collection Dataset (AMCD), a novel collection of agricultural images.
- To provide a valuable resource for research in agricultural image classification, transfer learning, and model benchmarking.
Main Methods:
- Collected 5405 JPG images of fruits, vegetables, flowers, and crops/grains in Bangladesh using smartphone cameras.
- Images were captured in natural settings with variable conditions and processed (resized, color balanced, enhanced).
- Applied conservative, non-synthetic data augmentation to address underrepresented subclasses.
Main Results:
- The dataset comprises 77 subclasses of agricultural and floricultural specimens.
- Images feature natural lighting, varied backgrounds, and multiple viewpoints.
- The processed dataset is suitable for various agricultural AI applications.
Conclusions:
- The AMCD is a comprehensive resource for developing and validating AI models in agriculture.
- This dataset facilitates research in plant image analysis, enabling applications like disease identification and yield estimation.
- The availability of AMCD supports the advancement of smart farming technologies.
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