Agriculture

New AI technologies make the farming process controlled, predictable, and reliable
We have experience in object detection and visual recognition for agricultural and farming AI projects. For example, plant diseases on open farms, personal and industrial greenhouses.

You can get a better result if you use private training data, i.e. shooting videos and photos of the same objects which are used for training your neural networks.

We can help you recognize the following things in datasets from your farm:
Tagias Manager helps you create a good technical task for the data or markup that needs to be performed. So that you get a training dataset that gives real results and a low error rate.
FRUIT
visually noticeable leaf disease;
the degree of fruit maturation;
the presence of caterpillars, beetles, and other parasites.


CASES

Use cases

Image annotation

Disease
detection
Crop
management
Seasonal forecasting
Infected plants datasets are collected and annotated to help AI to detect diseases
in farming

Monitoring the condition of fruits for successful harvest management
using machine
learning algorithms

Using collected datasets in order to predict the planting stages according to seasons, weather conditions, etc.

Disease detection
Crop management
Seasonal forecasting
Bounding boxes
Classification
Keypoints
Polygons
Lines
Semantic segmentation
Bounding box annotation
Bounding boxes
The most common type of data annotation. Bounding boxes are mainly intended to detect objects, delineating their borders with transparent rectangles. Bounding boxes are applied in almost all industries of the data labelling market.
Images classification
Classification
This method is used to determine the presence of an object in the image. A specific label is assigned to each image. Image tagging helps train AI to recognize objects' classes.
Keypoints annotation
Keypoints
This type of annotation uses dots across an image to identify an object or its details/parts. Keypoints are applied mostly with facial recognition, body parts, and postures.
Polygons annotation
Polygons
Polygons are used to determine an object's shape and location with maximum precision while avoiding additional noise. Polygon annotation is applied in almost all industries.
Lines annotation
Lines
Lines annotation is used for detecting and recognizing lanes. This type of annotation is applied mostly in the automotive industry to make self-driving cars safe.
Semantic segmentation
Semantic segmentation
A data labelling method where objects are delineated in the image at the pixel level, depending on class. It's called semantic segmentation because each pixel has semantic meaning. Segmentation is usually applied in the automotive industry.