Abstract

Smart agriculture uses technology to enhance farming processes, with Unmanned Aerial Vehicles (UAVs) as a crucial tool for aerial imaging, crop monitoring and precision spraying. The inability of existing research to precisely identify spray regions impedes the effective control of pests and diseases in agricultural settings. In this study, we present the development of the Clonal Flower Pollination integrated Adaptive radial deep neural network (CFPO-ARDNN), which uses data from UAV s to identify spray zones. The classifier was evaluated in agricultural settings using an RGB camera, considering five distinct croplands. To assess and test the created system, high-resolution UAV photos were employed in five distinct target fields: pea, apricot, strawberry, peach and coriander. The Joint Photographic Experts Group (JPEG) converter, RGB to hue saturation intensity (HSI), Wavelet Transform and Local Binary Pattern (LBP) for feature extraction were all used in the preparation of the video. In comparison, the CFPO-ARDNN model outperformed the others in predicting the following: strawberry (91.85%), Apricot (96.56%), Peach (95.67%), Coriander (93.47%) and pea (94.45%). The experiment’s findings demonstrate how well the suggested CFPO-ARDNN model works in comparison to the current approaches.