Image Dataset of Local Cocoa, Maize and Soybean Seed Varieties from Ghana

Published: 29 September 2026| Version 2 | DOI: 10.17632/t94nzy4ptz.2
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Description

This dataset comprises 31,069 confirmed, expert-verified single-seed images spanning 12 locally recognised varieties across three economically important Ghanaian crops: cocoa (4 varieties—Agric, Gardener, Hybrid, and Tetteh Quarshie; 3,005 images), maize (4 varieties—Bhihilifa, Obatanpa, Sanzal Sima, Wang Basik; 13,438 images), and soyabean (4 varieties—Afayak, FarmerSaved, Favor, and Jenguma; 14,626 images). Maize and soyabean seeds were pre-sorted by variety at Heritage Seeds Ghana; cocoa seeds were extracted from pods pre-sorted by variety by farm experts in Berekum, Ghana, and sun-dried for seven days before imaging. All seeds were photographed in 352 groups against a plain blue background with a Google Pixel 9a smartphone under natural daylight, then automatically cropped into individual seed images (native 640 × 640 px) using a colour-based segmentation pipeline, with variety labels independently re-confirmed by the respective experts — Heritage Seeds Ghana for maize and soyabean, and the Quality Control unit of the Ghana Cocoa Board (COCOBOD), Sunyani, alongside Berekum farm experts, for cocoa—before every cropped image underwent a further manual visual audit. Images are released at their native 640 × 640 px resolution and organized into folders by crop and variety. The dataset supports the development of low-cost, mobile deep-learning models for seed variety identification and certification, addressing the current lack of openly available, cultivar-labeled image data for African seed crops. Class distributions are close to balanced, though a few varieties—Agric cocoa (17.7%) and Bhihilifa maize (20.2%) among them—are comparatively under-represented, while Wang Basik maize (34.3%) is comparatively over-represented.

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Steps to reproduce

Maize and soybean seeds were sourced and already sorted by variety from Heritage Seeds Ghana, Tamale. Cocoa pods were sourced pre-sorted by variety from farm experts in Berekum, Ghana; seeds were removed from the pods, labelled by variety, and sun-dried for 7 days under the guidance of the Berekum farm experts and Heritage Seeds Ghana. For each variety, the seeds/beans were arranged in groups on a plain background and photographed during daylight hours using a Google Pixel 9a smartphone (48 MP wide, ƒ/1.7 rear camera). Each grouped photograph was tagged with its known crop and variety, then individual seed images were cropped out via an automated color-based segmentation pipeline, carrying the label through. Variety labels were verified throughout—by Heritage Seeds Ghana specialists (maize, soybean) and by the Quality Control unit of the Ghana Cocoa Board (COCOBOD), Sunyani, alongside the Berekum farm experts (cocoa)—from initial sorting to the final cropped images, with every cropped image additionally passing a manual visual audit before release. Confirmed images were organized into class folders by crop and variety and released at their native 640 × 640 px crop resolution.

Categories

Computer Vision, Machine Learning, Image Classification, Precision Agriculture, Mobile Deep Learning

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