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In contemporary farming, visual perception systems grounded in deep learning are emerging to equip autonomous vehicles with the capability to perform crop-field interventions. Yet securing strong performance from these techniques hinges on access to large volumes of data, a resource that is typically scarce in agricultural contexts. The core reason is that constructing an agricultural dataset that captures a given problem in exhaustive breadth is difficult, since the visual presentation of symptoms is subject to change. There is pronounced sensitivity to ambient variables such as heat, dampness, and lighting regimes. An efficient pipeline is therefore imperative to methodically shepherd the full sequence of building an agricultural dataset, from initial image capture through to its web-facing release. This manuscript describes the Eden Library, a platform conceived to help close the recognized shortfall in freely available crop and plant repositories that span both close-range and overhead imagery. The end-to-end workflow underpinning the platform’s conception and launch is also set forth and explored. This workflow merits attention because the datasets on offer are designed not as static, one-off research outputs confined to a narrow slice of species, growth stages, and environmental circumstances, but rather as evolving resources to be stewarded and expanded across time. Annotations delineating plants and their associated symptoms are supplied, thereby relieving users of the manual labeling workload. At the time of writing, the Eden Library spans 15 distinct crop species, 9 weed varieties, and 30 forms of disorder (encompassing pests, diseases, and nutritional shortfalls). The Eden Library endeavors to narrow this divide by delivering a voluminous, richly varied image trove of plants, curated coherently, to accelerate the development of future vision-driven, AI-augmented field tools.