Enhancing the Agricultural Yield Using Artificial Intelligence Techniques

In this work, an advanced agricultural platform leveraging machine learning to address critical challenges in modern farming has been suggested. By integrating data-driven analysis with intelligent algorithms, it empowers farmers with actionable insights to enhance productivity and sustainability. Four modules namely crop recommendation, fertilizer optimization, plant disease prediction, and crop damage reason analysis has been designed, each designed to optimize agricultural practices and mitigate risks. The crop recommendation (CR) module suggests ideal crops and rotations based on soil fertility, climate conditions, and historical data. The fertilizer recommendation (FR) module provides precise nutrient application strategies by analyzing soil samples and monitoring crop health in real time. The plant disease prediction (PDP) module employs deep learning and image recognition to detect and diagnose diseases early. Additionally, the crop damage reason prediction (CDRP) module identifies whether crop damage is caused by excessive pesticide use or other factors. The accuracy of CR module is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 6 \%}$</tex>, FR module is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 0 \%}$</tex>, PDP module is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 9. 8 8 \%}$</tex> and CDRP module is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 2 \%}$</tex>. Together, these modules form a comprehensive smart farming solution that modernizes agricultural practices, reduces waste, and promotes sustainable food production. By empowering farmers with cutting-edge technology, it supports a data-driven transformation of the agricultural industry.