Agricultural AI

AgriTech annotation

Label the data needed for your agricultural AI with a Human in the Loop!

Labeling for AgriTech is not an easy task, because it requires annotators to develop a very deep expertise in the types of plants they are dealing with. Our teams have a proven track record of working with companies and research institutes and producing high-quality labeling on both RGB and multispectral imagery.

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Client success story

Read how French research institute InRAE has partnered with Humans in the Loop in order to use annotation to optimize the plant phenotyping process

Crop detection

Fields in different geographies might vary greatly and a lot of training data is needed in order to detect and classify crops correctly. In addition to bounding box and polygon labeling, instance segmentation can be applied in order to count exactly the number of leaves or fruits of each plant.

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Weed detection

Detecting weeds in a precise way is necessary in order to optimize the use of chemicals such as herbicides and to avoid affecting crops. Weeds can be detected by using bounding boxes or, for even better precision, polygons or semantic/instance segmentation.

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Fruit counting

Using keypoints or bounding boxes, computer vision models can be trained on datasets of produce where each instance/fruit is labelled. By using this data, fruit counting and classification can be done automatically at scale.

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Health monitoring

By using labeled data, machine learning models can be trained to detect and even diagnose a diseased plant at scale. This can be done in various ways such as labeling the diseased plants with bounding boxes or polygons or segmenting the diseased parts and the healthy parts separately.

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Growth analysis

Precision agriculture needs a lot of data about plant growth stage and yield in order to apply water and fertilizer in a targeted way. This requires the labeling of each crop with information about its growth stage and status (e.g. water stress, nitrogen stress, etc).

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Soil analysis

With labeled data, often aerial and sometimes even satellite images, computer vision models can be trained to classify the soil in an area. This can be useful for example in searching for fertile land, or tracking the effects of deforestation and erosion.

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Aarhus Plant Segmentation

Download our free plant segmentation dataset!

Humans in the Loop has published an open access segmentation masks for a dataset provided by the Computer Vision and Biosystems Signal Processing Group at the Department of Electrical and Computer Engineering at Aarhus University

How would you like your data annotated?

Types of annotation

Bounding Box

Interested in having a Human in the Loop label your Agriculture dataset?
Get in touch with our team at Humans in the Loop and a project manager will help you find the best solution to your computer vision needs!