Precision Farming with AI and Drones

Abstract

Field level, data driven decision making is increasingly improving the performance of agriculture using precision farming technologies. Implementation of remote sensing technologies, using artificial intelligence, for the optimization of crop management is developed in this study. We employ autonomous drones, which are integrated with machine learning for real time crop surveillance, to obtain high resolution aerial images of the farm. The system provides real time detection of plants under stress, nutrient, or pest deficient conditions with 92% precision for tailored responsive action. Major components include a computer vision system for the detection of diagnostic indicators of crop health, a prediction model for yield, and a decision support system that creates prescription maps for variable rate (VRA) application. Implementation on 800 hectares of land recorded a 28% saving of water due to irrigation optimization, a 22% savings of fertilization rate due to nutrient management, and a 35% reduction in pesticide usage due to pest control. The system's edge computing design makes it operable in remote areas, catering to their needs. This technology can meet the dual challenge of improving productivity and sustainability by illustrating AI's role in precision farming. It demonstrated a viable system for the sustainable intensification of agriculture with an 18% increase in yield and a reduction of the environmental footprint. Access to cutting edge agricultural technologies at an affordable price is especially beneficial to small and medium sized farms due to the cost effective and versatile nature of the solution.