Soft Computing-Based Climate Change Monitoring System Using Multi- Sensor Dataset

Abstract

Conventional agricultural systems are unable to meet both global food safety issues and the demands of the human population. These difficulties are a result of farmers' poor crop planning. To handle air, dirt, water, etc., a digital twist is required. New farming strategies need to be developed to address the consequences on the air, land, water, etc. To mitigate the potential consequences of climate change, "climate-smart agriculture" (CSA) is recognized as a viable and sustainable agricultural system. The objective of this study is to forecast crop production. Farmers would select the crop that will produce greater amounts based on the local climate. Making critical decisions will also be easier for farmers if they have advanced knowledge of the crop output. The results of this research will help farmers forecast crop yields based on soil and climate factors. With the use of the proper soft computing approaches, the model discovers the association between variables such as crop production and characteristics like rainfall and soil type. The best crops can be selected by farmers with its assistance, and the agricultural businesses will benefit from these forecasts as well.