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<p>The novel idea of “smart farming” employs cutting-edge information technology to boost agricultural productivity. Modern developments in networking, robotics, and AI allow farmers to keep a closer eye on every process and use treatments calculated by machines with an accuracy level comparable to that of a person. Engineers, data scientists, and farmers are all persistent in their pursuit of methods that will enable them to maximize the efficiency of human labor in farming. Smart farming becomes an ever-improving learning system as vital information resources continue to improve. Artificial intelligence (AI) techniques from deep learning (DL), including CNN, RNN, and GAN, have been extensively researched and used in several industries recently, including agriculture. Software frameworks are often used by agricultural researchers without a thorough examination of a technique’s concepts and operations. Failure of crops owing to low rainfall, soil infertility, and other similar issues have been plaguing farmers recently. The planned effort aids in determining how to intelligently manage harvests and crops in light of the environmental changes occurring. A person may use it as a guide for smart farming. The main objective of this project is to provide a means for a person to effectively grow crops, allowing them to achieve great output while keeping costs down. The entire cost of cultivation may also be better estimated with its aid. A comprehensive approach in farming would be achieved if this helped a person to pre-plan the actions before growing.</p>

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Climatic Condition–Based Comparative Study of Deep Learning Models for Yield Forecasting in Smart Agriculture

  • 期刊论文

  • 发表日期：

    2025年1月14日

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  • Jyoti A. Dhanke,
  • Sachin Dadu Khandekar,
  • Roop Raj,
  • Madhava Rao Chunduru,
  • Aniket K. Shahade,
  • P. Mani,
  • Allam Balaram

摘要

The novel idea of “smart farming” employs cutting-edge information technology to boost agricultural productivity. Modern developments in networking, robotics, and AI allow farmers to keep a closer eye on every process and use treatments calculated by machines with an accuracy level comparable to that of a person. Engineers, data scientists, and farmers are all persistent in their pursuit of methods that will enable them to maximize the efficiency of human labor in farming. Smart farming becomes an ever-improving learning system as vital information resources continue to improve. Artificial intelligence (AI) techniques from deep learning (DL), including CNN, RNN, and GAN, have been extensively researched and used in several industries recently, including agriculture. Software frameworks are often used by agricultural researchers without a thorough examination of a technique’s concepts and operations. Failure of crops owing to low rainfall, soil infertility, and other similar issues have been plaguing farmers recently. The planned effort aids in determining how to intelligently manage harvests and crops in light of the environmental changes occurring. A person may use it as a guide for smart farming. The main objective of this project is to provide a means for a person to effectively grow crops, allowing them to achieve great output while keeping costs down. The entire cost of cultivation may also be better estimated with its aid. A comprehensive approach in farming would be achieved if this helped a person to pre-plan the actions before growing.