Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 3
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Forecasting on Temperature and Dew Point based on Agricultural Field data using Transformer Based Model

Author(s) Subhash Kumar, Dr. SP Singh, Dr. Dheerendra Pratap Singh
Country India
Abstract A IoT based project in a village of Maharashtra Narayangaon was as a pilot study. In this village the farmers of this village mainly do the farming of grapes and sugarcane along with seasonal vegetables like cabbage, tomato etc. Through remote sensing the data is collected on wind speed, relative humidity, dew point etc.
This project is divided in various modules like data collection module, statistical model for timeseries forecasting, deep learning model in forecasting, agricultural field coverage design module Out of various modules this paper concentrates on the new time series model using Transformer. Advantages of Transformer are Parallel Processing where All words processed together and it works faster on GPUs due to parallelism. Another advantage is Long Dependency Learning through which It understands the distant relationships.
The current paper discusses the use of Transformer architecture for forecasting in Time Series on air temperature, wind speed, humidity based on the historical information collected from the sensor installed in the agricultural field. Transformer introduces the concept of self-attention and Transformer uses parallel computation of sequential ordered data such as in time series for faster processing. Proposed field of study is related to providing solution for the farmers in terms of minimizing loss due to heavy rains, winds and other weather conditions. Especially when the crop is ready for harvesting and unexpected rains come which leads to financial loss. A better prediction on atmospheric condition will help the farmers to minimize crop loss.
Keywords Relative Humidity, Dew Point, Remote Sensing, Transformer, Self Attention, Long Dependency, Parallel Computation.
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-06-03
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E-ISSN 3048-7641
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