In this paper, an attempt has been made to predict the rate of penetration (ROP) of rocks by incorporating thrust, revolutions per minute (rpm), flushing media and compressive strength of rocks using artificial neural network (ANN) technique. A three-layer feedforward back-propagation neural network with 4-7-1 architecture was trained using 472 experimental data sets of sandstone, limestone, rock phosphate, dolomite, marble and quartz-chlorite-schist rocks. A total of 146 new data sets were used for the testing and comparison of the ROP by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of ROP. The coefficient of determination by ANN was 0.985, while coefficient of determination was 0.629 for rate of penetration. The mean absolute error (MAE) for rate of penetration by ANN was 0.3547, whereas MAE by MVRA was 1.7499.

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The utilization of artificial intelligence (AI) has facilitated the automation of drone control, which includes the management of navigation and movement. This application can be accomplished through several methods, including GPS tracking, computer vision, and machine learning algorithms. Drones exhibit a distinctive combination of spatial coverage and resolution, rendering them indispensable for land survey and mapping. The incorporation of multiple ground-control points has the potential to yield high precision georeferencing for the Orthomosaic product. In conjunction with field observations, drones provide a prompt and precise means of recording land data and its use. A drone survey and mapping operation was conducted within a mining lease situated near the village of Kanthariya, in the Tehsil and District of Chittorgarh, covering an area of 64.75 hectares, for the analysis of agricultural land use in the mining lease.