The advancement in computer science technology has led to some serious concerns about the piracy and copyright of digital content. Digital watermarking technique is widely used for copyright protection and other similar applications. In this paper, a technique for digital watermarking based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Grasshopper Optimization Algorithm (GOA) is proposed. The method computes the DWT of the cover image to obtain the sub-components of the image. The subcomponent is converted to frequency domain using DCT. The challenge is to find the optimal scaling factor to be used for watermarking. The authors have designed a GOA based technique that finds the optimized scaling factor and the coefficient for embedding the watermark. GOA makes the watermark undetectable and is invisible in the cover image. The watermark image is embedded in the cover image using these coefficients. The extraction of watermark from the cover image is done by using inverse DCT and DWT. The proposed method is compared with the other state of the art methods. The effectiveness of the proposed method is computed using Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC) and Image Fidelity (IF). The proposed method outperforms the other methods and can be effectively used for practical digital watermarking.
The population is quickly growing, as is the need for food. The farmers' traditional tactics are insufficient to achieve these goals. As a result, new automated methods (such as drone technology) emerged. These innovative techniques met food demands while also offering employment opportunities for billions of people. Drone technology saves water, pesticides, and herbicides, protects soil fertility, and allows for more efficient use of labour to boost productivity and quality. This article will look at the usage of drones in agricultural applications. According to the literature, drones may be utilised for a range of agricultural reasons. The authors took the approach of doing a thorough review of past studies in this topic. This study describes the current state of agricultural drone technology, including crop health monitoring and farm activities such as weed control, evapotranspiration estimation, and spraying. The study concludes by recommending more farmers to invest in drone technology to boost agricultural output.
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