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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.

|June 30, 2026

Summary

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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

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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.

Keywords:

Agricultural AI Agricultural image dataset Bangladesh agriculture Computer vision Crop classification Field-condition imagery Multi-class classification Transfer learning

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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.