Agriculture
❯
Crop health Analysis
❯Precision Farming as a Service
Crop Diagnosis & Product Recommendations Through AI
For:
Farmers, Biochemical companies, Crop disease treatment manufacturers
Goal:
Improved Product Development / R&D, Improve Operation Efficiency
Real-time segmentation and prediction of plant growth dynamics using low- power embedded systems equipped with AI
For:
Agriculture, ecology management, sanitary services
Scope:
The project is devoted to the development of a low-power embedded system and AI algorithm for real-time plant segmentation and prediction of its growth. The proposed distributed system is aimed for use in greenhouses and remote areas, where edge-computing autonomous systems are in demand. A branch of this project also aims to develop the payload for drones for the segmentation of harmful plants in real-time.
Goal:
Improve Operation Efficiency
Ecosystems management from causal relation inference from observational data
For:
Environment, ecosystem
Scope:
Infer important latent variables to control a whole ecosystem using a database including human observation and sensor data.
Goal:
Improved Product Development / R&D, Improve Operation Efficiency
Precision Farming as a Service
For:
Farmers
Scope:
Use visual recognition to identify and help fight parasites attacking organic farms.
Goal:
Anticipate Risks, Improve Operation Efficiency
Precision Farming as a Service
For:
FarmersGoal:
Anticipate Risks, Improve Operation EfficiencyProblem addressed
The use case shows how AI is contributing to modernization of the agriculture
industry.
Scope of use case
Use visual recognition to identify and help fight parasites attacking organic farms.
Description
BioBotGuard main goals are to cut the use of phytosanitary
treatments to contain the environmental health risk by
estimating the probability of incubation and development of
plant diseases or harmful insect attacks and anticipate
treatments. BioBotGuard monitors microclimatic conditions
with high accuracy measurement and prediction models to
optimize irrigations.
From the technology point of view, it employs: AgroDrones
to patrol and map the culture field. They are equipped with
20Mx high-resolutions cameras to capture in real-time
images. On the back end, the drone sends data to a computer
vision API for image classifications and pattern detections.
Among other things, the system is able to detect harmful
insects and build a georeferenced risk map of the crop.
As a result, bioBotGuard can help AgriFood producers to
change the cost structure of the industry by requiring less
water and less treatment, as well as a significant reduction in
labour costs.
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AI: Perceive
IOT
Computer Vision
AI: Understand
Automate Process
AI: Act