In July 2018, the Karnataka Government launched a Centre of Excellence in Data Sciences and Artificial Intelligence in collaboration with NASSCOM. The CoE initiative is a nationwide program on innovative solutions in smart manufacturing, automotive, healthcare, agriculture, energy, IoT, retail, telecom, banking, and financial services. 

In October 2017, the Government of Karnataka signed a MoU with Microsoft India to increase farmers’ income using cloud-based technologies, machine learning, and advanced analytics. The MoU will help Karnataka Agricultural Price Commission (KAPC) and Department of Agriculture to improve price forecasting model based on various datasets such as historical sowing area, production, yield, and weather. The MoU aims to bring digital farming using Microsoft Cortana Intelligence Suite including technologies such as machine learning and business intelligence.

The primary source of revenue for Karnataka Forest Department is sale of timber. Currently there is no predictability on the expected baselines and revenues. Also, there are no Level-1 insights generated pre/post the sale process. This leads to lost opportunities in maximizing revenues leading to a better process of KFD in meeting their objective of a “Better Forest”.

KFD and Nasscom CoE ran workshops, competition and hackathons with 60+ shortlisted B-Tech Start-ups and AI innovators from across Karnataka to identify and finalize 1 AI machine learning analytics vendors with custom solution with 5 years historic data on 1,400 registered bulk purchasers, which include sawmills and companies. Nine lakh tons of lots of timber sales data from 2013 to December 1, 2018 was analysed, churned and deep learning algorithm deployed to identify the following

• Forecast Quantity of Wood Required
• Type of wood by Seasons
• Customer or Buyer behaviour - By Type of Transactions/ By Type of Wood/ By GEO/Warehouse or Bidding location
• Behaviour of the Price
• Behaviour of the Species
• Class of timber that fetches the best rate
• Best time to dispose of timber
• Best disposal methods

Timeline: 6 to 9 months

KSPCB is responsible to monitor, prevent, control & abatement of pollution across State. Monitoring done by multiple vendors using different systems. Each vendor gives data in different formats and visualizations and inconsistent reporting schedules.

Their main objectives are:

• KSPCB aims to provide ‘One View’ for air monitoring by area / location across different monitoring stations & elements.
• KSPCB and Nasscom CoE ran workshops, competition and hackathons to identify AI + IoT solutions relevant to solve the pollution monitoring challenge.
• KSPCB leveraged 3 years historic data on air quality and levels of pollution by different areas/ regions and weather conditions plus number of commercial establishments around selected regions
• Deep learning and machine learning algorithms were deployed to analyse and identify the following

i. Pin Code or Area Wise Level of Air Pollution

ii. Pin Code or Area Wise Weather Conditions

iii.Type of reporting visualizations

iv. Type of reporting schedules

v. Correlation of Pollution, Weather Conditions, Moisture, # of Cars, # of Factories and commercial establishments to understand air pollution levels.

1. By Type of Transactions

2. By Type of Wood

3. By GEO/Warehouse or Bidding location

vi. Behaviour of non-pollutants

vii. Behaviour of pollutants

viii.  Best Air Quality time by Area/Pin Code

 ix. Worst Air Quality time by Area/Pin Code

“INTELLIGENT VISUALIZATION” for Ambient Air Monitoring was designed and delivered by KSPCB making it first of its kind by a state government.

Timeline: 8 to 10 months 

The Bengaluru traffic police have introduced an Intelligent Traffic Management System (ITMS), where AI-enabled cameras will detect traffic violations and issue challans through SMSs to mobile phones. 

The government of Karnataka collaborated with CRUBN, an IIT Kanpur-based startup to develop a state-of-the-art blockchain network for all e-Government Procurement (e-GP) systems across the country. This network will be used for validating the claims of suppliers along with their identities. The proposed system will connect all the e-GP systems through a network, which will help retrieve and validate relevant supplier data between various e-GP Systems that are in use within India. The proposed system will also digitize Bank guarantees by powering them using the trust-based automation of Blockchain.