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The need for sustainable agricultural techniques is greater than ever because environmental issues are becoming more pressing worldwide. Growing crops in greenhouses, which offer controlled environments for enhancing plant productivity in the advancement of technology, is one possible solution. In order to overcome these obstacles and optimize agricultural productivity, food security, crop nutritional quality, and environmental sustainability, smart greenhouse integration of the Internet of Things (IoT) is emerging as a critical lever. Furthermore, data security remains a major challenge, even if agricultural IoT devices play a crucial role in data presentation and linkage through wired and wireless synchronized-based communication as the aim is to gather different aggregated greenhouses to reach the fog and cloud computers. Optimizing the PAYLOAD charge and utilizing modern technologies like visual-based watermarking and real-time data synchronization are crucial. Particularly with the aid of computational intelligence, we can approximate lost and inaccurate sensor results and generate the required forecasts using data analysis tools, ensuring that farmers make informed decisions and perform better. The methods that support objective and subjective PAYLOAD optimization and have a noticeable impact on data analysis and prediction to well understand those kinds of data, energy efficiency, and saving in terms of deploying synchronized handshaking between either I2C Master and its relative I2C Slaves and also between the Cloud and IoT Nodes and also by idling all unworking nodes, and system performance in terms of PAYLOAD time transportation duration, are described in this chapter.

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Green, Intelligent, Optimized, and Visual Watermarking Solutions Applied to Precision Farming to Optimize Crop’s PAYLOAD and Enhance Its Objective and Subjective Data

  • 会议论文

  • 发表日期：

    2025年

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  • Hicham Essamri,
  • Abderrahim Bajit,
  • Youness Zahid,
  • Driss Zejli,
  • Rachid El Bouayadi

摘要

The need for sustainable agricultural techniques is greater than ever because environmental issues are becoming more pressing worldwide. Growing crops in greenhouses, which offer controlled environments for enhancing plant productivity in the advancement of technology, is one possible solution. In order to overcome these obstacles and optimize agricultural productivity, food security, crop nutritional quality, and environmental sustainability, smart greenhouse integration of the Internet of Things (IoT) is emerging as a critical lever. Furthermore, data security remains a major challenge, even if agricultural IoT devices play a crucial role in data presentation and linkage through wired and wireless synchronized-based communication as the aim is to gather different aggregated greenhouses to reach the fog and cloud computers. Optimizing the PAYLOAD charge and utilizing modern technologies like visual-based watermarking and real-time data synchronization are crucial. Particularly with the aid of computational intelligence, we can approximate lost and inaccurate sensor results and generate the required forecasts using data analysis tools, ensuring that farmers make informed decisions and perform better. The methods that support objective and subjective PAYLOAD optimization and have a noticeable impact on data analysis and prediction to well understand those kinds of data, energy efficiency, and saving in terms of deploying synchronized handshaking between either I2C Master and its relative I2C Slaves and also between the Cloud and IoT Nodes and also by idling all unworking nodes, and system performance in terms of PAYLOAD time transportation duration, are described in this chapter.