ABSTRACT
In the current situation the use of electronic devices like mobile, laptop, computer, ear phone etc has been increased in the daily routine activities specially in COVID-19. The education sector and IT industries are almost fully working in online mode in the today environment by sharing of their content in the form of digital data or multimedia data. The digital data is increasing in large scale every day and due to this increasing large amount of data, new research areas has been come introduced like bigdata, data analytic, data science etc to manage the digital data in the better or proper way. But with this the other property of digital data like its security, copyright protection, Copy control, Content authentication, Integrity verification etc. are also major concerns. The digital data or multimedia data basically includes text, images, audio, video, software etc. of individuals / organizations. Each persons or organizations are sharing his/her digital data like videos, images, messages, etc. through social media (i.e., Say namaste, Telegram, Snapchat, Instagram, WhatsApp, Facebook, etc.) to other persons or organization without any authentications. This general activity of persons has been increased in the todays life and some persons are observing them and doing the fraud with them or misusing their digital data without his awareness. Sometimes it becomes major problems in term of legal issues. To ensure the authentication of digital data (photos), this paper proposed secure watermarking technique for color images using Aadhar number, DWT and SVD methodologies. The proposed methodology is best to protect from fraud or misuse from all type of color images shared in the public domain or globe. The experimental results are shown in different form which shows this technique is more secure and very useful for society when they are sharing their family photos in the globe.
ABSTRACT
The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R2 and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively;those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework.