sustainable and precision agriculture biotechnological model using deep learning algorithm

Objectivewith the help of weather data from the agricultural internet of things (iot) method’s, it is possible to plan for changes in the weather. this is an excellent way to prepare and keep track of the production of green agriculture. thus, the aim of this study was to make weather information forecasting more accurate in the precision agriculture (pa) system.materials and methodsit is difficult to accurately predict future trends as the data is complicated and requires simple linear links. the evolution of communication technology and the increasing number of interconnected things have had a profound impact on the agricultural sector. advances in ai, and deep learning in particular, have facilitated faster and more accurate data processing in this modern digital era. a new data analytics technology called deep learning has the potential to make farming more efficient, eco-friendly, and predictable. in this study deep learning (dl) predictions with a two-level decomposition structure and biotechnology (bt) were used to make the prediction of weather information in the precision agriculture (pa) system more accurate. first, the weather data was decomposed into four parts. then, the gated recurrent unit (gru) systems were created as sub-predictors for each part. resultsfirst, the weather data was decomposed into four parts. then, the gated recurrent unit (gru) systems were created as sub-predictors for each part. the predictions for the medium and long-term future were made by combining the results from the grus. using weather data from the bt-based iot systems, it was confirmed that the tests work with the suggested structure. conclusionsthe proposed prediction method can predict the temperature and humidity correctly and meets the pa standards. it can assist farmers in the management of their agricultural operations. it is possible to provide an initial prediction and assessment of extreme weather conditions in agriculture to minimize risks and maximize profits.

sustainable and precision agriculture biotechnological model using deep learning algorithm

objectivewith the help of weather data from the agricultural internet of things (iot) method’s, it is possible to plan for changes in the weather. this is an excellent way to prepare and keep track of the production of green agriculture. thus, the aim of this study was to make weather information forecasting more accurate in the precision agriculture (pa) system.materials and methodsit is difficult to accurately predict future trends as the data is complicated and requires simple linear links. the evolution of communication technology and the increasing number of interconnected things have had a profound impact on the agricultural sector. advances in ai, and deep learning in particular, have facilitated faster and more accurate data processing in this modern digital era. a new data analytics technology called deep learning has the potential to make farming more efficient, eco-friendly, and predictable. in this study deep learning (dl) predictions with a two-level decomposition structure and biotechnology (bt) were used to make the prediction of weather information in the precision agriculture (pa) system more accurate. first, the weather data was decomposed into four parts. then, the gated recurrent unit (gru) systems were created as sub-predictors for each part. resultsfirst, the weather data was decomposed into four parts. then, the gated recurrent unit (gru) systems were created as sub-predictors for each part. the predictions for the medium and long-term future were made by combining the results from the grus. using weather data from the bt-based iot systems, it was confirmed that the tests work with the suggested structure. conclusionsthe proposed prediction method can predict the temperature and humidity correctly and meets the pa standards. it can assist farmers in the management of their agricultural operations. it is possible to provide an initial prediction and assessment of extreme weather conditions in agriculture to minimize risks and maximize profits.