AI + Imagery in Agriculture: The Perfect Pairing

May 1, 2019

The adoption of services that combine artificial intelligence with imagery in agriculture is changing agriculture as we know it. This is especially in the case of cognitive computing in particular, as highlighted in research by Mindtree.

Cognitive computing is set to become the most disruptive technology in agriculture services as it can understand, learn, and respond to different situations (based on learning) to increase efficiency.

Computer vision technology, IOT and drone data can be combined to ensure rapid actions by farmers. Feeds from drone image data can generate alerts in real time to accelerate precision farming.

Photo by Quang Nguyen Vinh from Pexels

Here are the top three ways in which AI, with cognitive computing, is benefitting agricultural technology.

  • Detection of Diseases
    An example of this application is capturing images of leaves, then preprocessing the images to ensure they are segmented into areas such as background, non-diseased part and diseased part. The diseased portions are then cropped and sent to remote labs for further diagnosis. This is of great help towards pest identification and the recognition of nutrient deficiency.
  • Field Management
    By using high-definition images from airborne systems, real-time estimates can be made during cultivation periods by creating a field map and identifying areas where crops require water, fertilizer or pesticides. This is a great help in resource optimization. High-resolution cameras in drones collect precision field images which can be used to identify areas that require further analysis. In terms of infected plants, by scanning crops in both RGB and near-infra red light, it is possible to generate multispectral images using drone devices. It is then possible to specify which plants have been infected including their location in a vast field to apply remedies, instantly.
  • Identification of Crop Readiness
    Images of different crops under white/UV-A light are captured to determine how ripe the green fruits are. Farmers can create different levels of readiness based on the crop/fruit category and add them into separate stacks before sending them to the market.

Explore More