Crop Recommendation and Agricultural Query Answering System Using Spatio-Temporal Graph Neural Networks and Hybrid Retrieval Augmentation
arXiv:2606.09160v1 Announce Type: new Abstract: This paper presents a unified system designed to support precision agriculture by integrating advanced weather prediction, crop recommendation, and a question-answering tool for farmers. We propose two deep learning models -- a Transformer-based Graph Neural Network and a Spatio-Temporal Graph Convolutional Network (STGCN) -- to forecast weather conditions for the next 30 days using data from 1,359 locations in Nepal. The STGCN outperforms the Transformer-based model in accuracy (MSE ~0.011 vs. 0.013), effectively modeling both spatial and tempor
The continuous advancements in AI and graph neural networks, combined with the increasing urgency of climate change and food security, are accelerating the development of precision agriculture solutions.
This development indicates a tangible application of advanced AI for critical sectors like agriculture, addressing global food security and climate resilience through highly localized predictions and resource management.
The ability to generate precise, localized 30-day weather forecasts and integrate them with crop recommendations and query answering tools changes how farmers can plan and optimize agricultural practices, moving from general advice to data-driven operational decisions.
- · Farmers in developing regions
- · Precision agriculture technology providers
- · AI model developers
- · Governments focused on food security
- · Traditional agricultural consultants
- · Farmers reliant on archaic methods
- · Regions without access to such technology
Improved crop yields and reduced agricultural waste due to better forecasting and recommendations.
Increased food security and economic stability in regions adopting these advanced agricultural AI systems.
Potential for AI-driven platforms to become central aggregators of agricultural data, influencing commodity markets and land use at scale.
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Read at arXiv cs.LG