Page 843 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

2�2 Benefits of the use case

Improve the livelihoods of local farmers, who often live in poverty and rely solely on crop sales
for income. By providing AI-enabled predictive soil and weather insights, farmers can better
plan their planting and cultivation cycles, leading to increased yields and improved economic Agriculture 4.11: Smart
outcomes.

Support climate action through the collection and analysis of localized environmental data from
farms across the country. This data helps visualize the impact of climate change on agriculture
and informs targeted interventions to mitigate its effects.

2�3 Future Work

Several enhancements and developments are planned to advance the project beyond its current
experimental phase. The immediate next steps include the integration of GPS functionality
to enable precise location tagging of collected data, improving the spatial accuracy of soil
and weather monitoring. Additional field deployments across multiple farms and diverse
geographical regions are planned to expand the data set and enhance model generalization.

Further work will focus on upgrading sensor hardware for better durability and precision,
particularly for long-term outdoor deployments. The machine learning models will undergo
continuous improvement through expanded data collection, feature engineering, and the
exploration of advanced algorithms such as ensemble learning and deep neural networks
optimized for TinyML deployment.

Additional resources needed include advanced IoT modules, low-power edge AI chips,
extended AWS cloud services for scalable data management, and increased access to diverse
soil samples from across different agro-ecological zones. Collaboration with local agricultural
extension services, universities, and international research institutions is envisioned to support
model validation, farmer engagement, and technology transfer.

Future expansions will also explore integrating pest and disease monitoring into the station’s
capabilities, enabling even broader support for smart farming practices. By fostering open
collaborations and sharing findings, the project aims to create a scalable model for data-driven
agriculture that can be replicated in other regions facing similar challenges.

3 Use Case Requirements
• REQ - 01: It is required to collect real-time soil metrics (temperature, humidity, pH, NPK,
and conductivity) and weather metrics (rainfall, temperature, humidity, and pressure)
using fixed sensors without moving parts.
• REQ - 02: It is required to securely and reliably transmit the collected data to the cloud
for storage and analysis.
• REQ - 03: It is required to integrate with the Grafina platform to enable real-time data
visualization and dashboard generation.
• REQ - 04: It is required to implement server-side timestamping for accurate and traceable
data logging.
• REQ - 05: It is required to develop supervised machine learning models trained on soil
and weather data to enable localized predictions.
• REQ - 06: It is required to optimize and deploy lightweight TinyML models for low-power,
on-device crop recommendation delivery.

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