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Developing image datasets is difficult, especially for agricultural applications. Collecting and annotating images pose major challenges.

Labeling is Time Consuming

Time consuming

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Image Collection

Data hungry deep learning models require hundreds of thousands, sometimes millions, of images to train

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other have noted that manually labeling images can take minutes to hours (Table 1)

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and become invariant to changes in location, lighting, backgrounds, or other factors. Devising high-throughput systems is necessary to capture hundreds of thousands of images of weeds at various growth stages while affected by field-like conditions. A network of agronomic, hardware, and software engineers is needed to automate large scale image collection.

Labeling is Time Consuming

Pixel-wise labels must be accurate. The process of labeling by hand can take minutes to hours even when using third-part annotation tools. Many have reported the high time requirement needed for labeling images of weeds.

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