RGB image dataset for okra maturity classification to enhance agricultural quality and market readiness.

Abstract

Okra is a highly nutritious farming product that combats malnutrition issues while supporting sustainable agricultural methods. In order to keep its quality and versatility in preparation applications, it is important to classify its maturity stages into under-mature, mature, and over-mature categories. Classification is vital to identify the best time to harvest, satisfy market demands, minimize post-harvest losses, and optimize cooking uses. This data in brief uses a non-destructive approach to okra maturity classification based on a dataset of images taken under controlled illumination using a standard RGB camera. The dataset contains okra samples that were sourced from various farms and vegetable markets, ensuring that it encapsulates the natural variability found in real-world farm and market environments. The availability of such a large dataset enables the creation of precise classification models that can assist farmers in optimizing the time of harvest, fulfilling consumers' requirements, and improving market results. The research has great relevance to promoting agricultural quality evaluation and boosting market readiness using non-invasive techniques.

Plant phenotyping relevance

RGB画像データセットによるオクラ果実の成熟段階分類が研究の中心であり、植物器官の状態を画像から推定する再利用可能なフェノタイピング資源に該当する。

Code and data availability

The paper is a Data in Brief article whose core contribution is a public RGB okra image dataset (364 images across three maturity classes) deposited on Mendeley Data, directly serving as the paper's phenotyping image asset. No separate analysis code repository is described.