Introduction
Geographic Information Systems (GIS) aid in the visualisation, analysis, and interpretation of spatial data by fusing data analytic and mapping technologies. This technology gives farmers a thorough grasp of their land and its features, which empowers them to makedecisions. Aerial surveys, soil data, and satellite images are used by GIS to improve overall farm management, optimise resource utilisation and enable precision farming.
GIS is transforming the agricultural sector by improving resource management and precision farming. Advancements in technology such as the introduction of Unmanned Aerial Vehicles(UAVs)helps farmers administer inputs like water, fertiliser, and pesticides precisely where needed. Sensors present in these UAVs capture images which are processed and analysed to derive meaningful insights. These insights are used to monitor the health of crops, soil moisture content and detect diseases in crops.
GIS also plays a crucial role in differentiating crops and vegetation using Normalized Difference Vegetation Index (NDVI),soil analysis by mapping soil types and nutrient levels, guiding crop selection and tailored fertilization techniques. By examining topography, soil moisture, and meteorological data, GIS optimises water utilisation in irrigation, resulting in more effective systems and proactive scheduling.Accurate yield forecast, early pest detection, and real-time crop health monitoring are made possible by the combination of GIS and remote sensing technology. GIS enables farmers to integrate and analyse weather data, allowing them to make better decisions based on climatic circumstances. Farmers, for example, can use GIS to estimate how weather patterns would affect crop growth and arrange their planting and harvesting schedules appropriately.There have been developments of various agricultural models which input various parameters to guide farmers.
Conclusion
GIS will continue to improve agricultural operations as technology develops, leading to increased productivity, sustainability and efficiency.As technology advances, GIS in agriculture capabilities also advance and therefore the integration of machine learning and data analytics will provide real-time data on agricultural products and techniques which will enable predictions and provide valuable insights.