From identifying diseases in plants at scale, to finely targeting fertilizer and pesticide dispersal - AI is playing an increasingly important role in Agriculture 2.0.

International organizations such as the UN and World Bank are increasing investment in Agriculture 2.0, which combines technologies such as IoT, AI, and vertical, cellular, and precision farming. 

Satellites, drones, and UAVs monitor crops from above, feeding data into ML models for analysis. Meanwhile, ML models deployed on the ground are equipping farmers and agriculturalists with the tools they need to make swift decisions in treating their crops for disease. To find out more about how machine learning is used in crop disease detection, view this case study.

Crop Maintenance in Agriculture 2.0

AI is enhancing crop monitoring and maintenance at scale. Satellite and UAV imagery can be used to calculate Normalized Difference Vegetation Index (NDVI), which provides a gauge of vegetation coverage and health. 

Healthy vegetation absorbs more visible light and reflects more near-infrared light compared to unhealthy vegetation. NDVI has been applied to crop maintenance, where image data is fed into computer vision models at scale to study and analyze crop health. 

Moreover, computer vision enhances disease monitoring on the ground. For example, aya Data collaborated with agronomic experts in Ghana to label 5000 images of diseased and non-diseased maize plants. The subsequent model could determine the specific maize disease with 95% accuracy, suggesting treatment options. 

Novel AI and ML models are already used to automatically sow seeds after forest fires, fertilize plants based on remote monitoring, and for numerous other creative applications. However, there is still great untapped potential -  Agriculture 2.0 demands high-quality data labeled with the assistance of domain experts. Aya Data has a proven track record in creating robust datasets for the agriculture and agritech industries. 

Data Labeling for Crop Maintenance and Monitoring

Aya Data assists companies in increasing their crop yields, reducing pests, fighting disease, and driving efficiencies in fertilizer soil treatment. Our high-quality labeled agricultural image and video datasets have already been employed in novel spaces within Agriculture 2.0. 

Services: Polygon, Bounding Box, Semantic Segmentation, Key Points

Domain Expertise: Agronomy