Agri- wiz An AI Driven Platform for Crop Recommendation and Yield Maximization

Agri- wiz An AI Driven Platform for Crop Recommendation and Yield Maximization

Jathar Raj Girish, Sapkal Pavan Baban, Bhojani Alimehdi Kausarali, Mohite Sourav Chitrasen.

HOD: Mr.A.N. Patil (Dept: CSE)

JAYWANT COLLEGE OF ENGINEERING & POLYTECHNIC, KILLE MACHINDRA GAD, SANGLI. AFFILIATED TO DR. BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY, LONERE 2024

- 2025

Abstract - This presentation delves into the evolution and real-world use of a crop recommendation system powered by machine learning. By harnessing historical agricultural data and key environmental variables, the system offers farmers personalized crop choices that promote higher productivity. Drawing on diverse data sources and cutting-edge algorithms, these AI-based solutions have shown impressive gains in performance—boosting efficiency, enhancing precision, and supporting more sustainable practices.

Among the notable advancements are a 15% increase in the accuracy of yield predictions, a reduction in water usage by up to 30%, and a 20% decrease in fertilizer application, all without sacrificing output. While the journey toward implementation isn’t without its hurdles—such as concerns around data privacy and the need for upfront investments—the long-term advantages of adopting AI in agriculture are compelling. These include better profitability for farmers, improved ecological balance, and stronger food security.

Ultimately, this in-depth analysis sheds light on the transformative power of AI-driven precision agriculture. It reveals how these technologies are redefining traditional farming methods and paving the way to solving global food production challenges, all while lessening the environmental impact of agriculture.

Key Words: Various sources include historical crop data, weather records, soil analysis reports, and satellite imagery.