Page 28 - AI Ready – Analysis Towards a Standardized Readiness Framework

AI Ready – Analysis Towards a Standardized Readiness Framework

This model is deployed in affordable weather stations located directly on local farmlands. These
stations would enable precise and localized forecasting of environmental conditions essential
for optimizing agricultural practices and minimizing risk. These weather stations operate without
mechanical parts, capturing real-time data on rainfall intensity, wind direction and intensity,
temperature, humidity, pressure, and air quality. Simultaneously, they conduct real-time soil
analyses to measure temperature, humidity, pH levels, NPK content, and conductivity. The
collected data is then visualized through intuitive dashboards accessible via smartphone apps
and web browsers.

The inference from the model could be used to maximize the precision of the irrigation and
reduce energy consumption by controlling actuators such as sprinklers or other types of
dispensers. The process is visualized via mobile and web applications; thus, experts and farmers
can monitor the model in real-time.

4�3�11 Water Conservation using AI-enabled Smart Irrigation Systems in
Agriculture

This use case [71] aims to utilize AI-driven smart irrigation systems to optimize water usage.
The traditional irrigation methods in Tanzania face the problem of efficiency and water wastage
[76]. By leveraging the power of real-time monitoring and adjustment of irrigation schedules
realized by integrating AI, sustainable agricultural practices [72] could be achieved. The use case
utilizes public data including weather data, soil data, and environmental data from the Ministry
of Agriculture in Tanzania, and incorporates private data from local regions [73].

The use case applies the Long Short Term Model (LSTM) model that is trained based on
local data and can be fine-tuned for a good performance designed to conserve water during
irrigation. This model is also capable of providing predictive analysis of crop status, soil moisture
level, and future water needs using historical data and real-time sensor inputs [75]. The model
also allows feedback collected from experts, can be integrated into agricultural practices via
communication networks.

This use cases utilizes opensource toolsets such as TensorFlow, NumPy, Keras, Pandas and scikit-
Learn and the jupyter notebooks and PyCham as the software tools for developing solutions.
Simulation environments such as Matlab/Simulink and SimPy are used to experiment and
simulate various conditions.

The use case has been deployed in testbeds in the Dodoma region due to its representative
soil type, crop varieties, and climate conditions prevalent in Tanzania [74].

4.4 Health Care

Healthcare use cases studied in this section focus on the combination of portable application
of AI technology and localization, with an aim to enhance affordable access to basic healthcare
services. These use cases utilize generalizable models and data and retrain the model with the
local context. Specific requirements on personal data protection in the healthcare domain make
it essential to establish international standards.

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