Page 16 - Preliminary Analysis Towards a Standardized Readiness Framework - Interim Report

Preliminary Analysis Towards a Standardized Readiness Framework

updated insights to users. Cloud APIs for extraction and exposition of data are important
interoperability consideration in such use cases.

"AI-Based Chat Box for Farmers" [49] aims to optimize the agriculture sector by unifying multiple
government agencies' data using Artificial intelligence models.

The productivity of agriculture and its allied sectors is influenced by numerous factors, including
climate conditions; soil fertility; crop breeding; water management; seed quality, pesticides,
fertilisers, and machinery; environmental sustainability; farmer's training and education; market
access, and government policies. Many of these factors are managed using traditional methods
and practices till now, which often limit farmer’s livelihoods and grain productivity.

Combining AI technologies with government policies enables various stakeholders to make
informed decisions to achieve sustainable development goals. With this goal in mind, the
use case uses data from such as the Indian Agricultural Statistics Research Institute (IASRI),
Indian Institute of Soil Science (IISS), National Bureau of Soil Survey and Land Use Planning
(NBSS&LUP), Indian Meteorological Department (IMD) to collect information regarding the
Indian agriculture, land use, soil information, climate data and so on.

Unified data from various agencies and machine learning models can be used to predict the
best plans, policies, and strategies for stakeholders to make informed decisions and implement
effective interventions for sustainable agriculture and development. A pilot study of agriculture-
related AI technology on 7000 farmers in the Khammam district of Telangana (India) showed
promising results, where the net income of the farmers using the AI technology had doubled
($800 per acre) from the average income in 6 months [37].

Fig 3 - Instances of Readiness Factors in Case Study-2

3.3 Case Study-3: Collaborative Multi-agent Systems

This case study includes use cases that use multi-agent systems hosted on end-user devices
such as drones, collaborating on specific missions such as disaster response. The devices may
be equipped with multiple data inputs such as visual cameras and networking capabilities

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