FarmD: A Web Interface for Visualization of Predicted Weather Parameters Using 1D Transformer Hybrid Models

Precisely predicting weather parameters is crucial for precision horticulture, especially in horticultural lands where timely environmental insights significantly impact crop yield and quality. This study presents a novel hybrid modeling approach employing 1D Transformer networks integrated with traditional machine learning techniques to predict hourly temperature variations. Utilizing the ERA5 reanalysis dataset spanning from 1940 to December 2024, the hybrid model efficiently captures location-specific spatiotemporal dependencies and nonlinear trends in historical weather data.

The predicted weather data generated by the hybrid model is used in FarmD, a web-based user interface developed for farmer-centric applications. FarmD provides real-time visualization of critical weather parameters, including temperature, relative humidity, wind patterns, rainfall, and soil temperature, specifically tailored to horticultural regions. Through its intuitive interface, users can query predicted and historical data by selecting attributes, dates, and times, with an option for location-specific searches to support targeted agricultural decision-making.

This integration of predicted data with an accessible web platform highlights significant advancements in delivering actionable insights to end users. By combining advanced computational methods with user-focused design, FarmD enables horticulturists to adopt data-driven practices, contributing to sustainable and efficient agricultural management.