AI-Powered Crop Recommendation for Smart Farming, Current Barriers, and Future Perspectives - Shilpi Shukla,; Sweta Singh,; Dr. Yusuf Perwej,; Farheen Siddiqui,; Dr. Nikhat Akhtar, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
Crop analysis and prediction is a quickly growing field that is very important for improving farming methods. Crop recommendation is important in farming because it helps farmers choose the best crops for their land and climate. In the past, this process depended a lot on expert knowledge, which took a lot of time and work. Also, with the world population expected to reach 9.7 billion by 2050, it is even more important to find ways to grow more food in a way that is good for the environment. Machine learning techniques can be very helpful in automating crop recommendations and finding pests and diseases. This will help farmers get the most out of their land while also keeping the soil healthy and adding nutrients that are needed. This paper examines the efficacy of crop recommendation utilizing seven distinct machine-learning algorithms. The proposed system uses a variety of factors, such as soil composition and climate data, to accurately predict which crops will grow best in certain areas. This system could change the way crops are recommended, which would help farmers of all sizes by increasing crop yields, sustainability, and overall profits. We have reached almost perfect accuracy by training and testing the machine learning algorithms with a wide range of configurations on a large set of historical data. We consistently achieve accuracy exceeding 95% across all models, with the peak accuracy reaching 99.5%. This research's results could help farmers, agronomists, and policymakers make better use of their resources and increase agricultural productivity by giving them data-driven insights.
Keywords: Agriculture Data; Machine Learning; Data Analysis; Recommendation System; Crop Selection; Pattern Recognition; Crop Yield Prediction; Consolidated Learning