Abstract:
In the domain of agriculture, effective nutrient management practices serve as a fundamental pillar for achieving optimal crop yields while minimizing the adverse environmental impact. General fertilizer recommendations often lead to over or under fertilization and traditional soil testing methods fail to capture the spatial variability of nutrients in the field. This research aims to enhance maize farming practices through the integration of Internet of Things (IoT) and geospatial techniques for nutrient distribution mapping. It involves the design and implementation of an IoT-enabled handheld device for spatially referenced macronutrient measurements in maize fields and employing an interpolation technique, Inverse Distance Weighting (IDW) for data analysis and nutrient distribution map generation. These maps empower farmers with the invaluable insights to make informed decisions regarding the application of fertilizers allowing for the optimization of maize crop growth while minimizing resource wastage. The efficacy of this system is validated through field tests conducted on a maize farm. These nutrient distribution map has an accuracy percentage of 94.92, 92.99, 94.04 for nitrogen, phosphorus and potassium prediction for un sampled locations respectively. This study establishes the foundation for implementing more sustainable, efficient, and environmentally conscious maize farming practices by harnessing the power of IoT and geospatial integration, thereby contributing to global food security and responsible resource management.
