• P. Nokesh
• D. Prasanna
• V. Srikanth
• Dr. Syed Jahangir Badashah
• Dr. Vikram Palodiya

The problem of choosing the proper crops to put in the fields in order to maximize agricultural productivity across many regions of India is a complex one because weather patterns are erratic and soil quality is uneven. Modern technological tools that could support farmers in selecting the most appropriate crops for their land are often often not available to them or not available in a consistent form. And other knowledge related to agricultural science being limited, unaware many do not adopt innovations in the agricultural science, and they have to rely on traditional practices which are outdated. This reduces crop yields and, in some cases, whole harvests fail. The causes for these problems are typically due to improper fertilizer use, or unexpected rainfall. Therefore, the most optimal solution would be to suggest crops complementary with prevailing soil conditions and rainfall expected during the sowing period. This challenge is confronted with the design of a software recommendation system called the Soil-Based Crop Profiling Framework. This tool takes into account soil characteristics, like nitrogen, phosphorus, potassium levels and pH as well as rainfall predictions, to determine what crop and fertilizer is most suitable to grow. The implementation of the framework is using a graphical desktop application that helps to provide a practical support to minimize crop failure and decision making while farming.

Deep learning, crop prediction models, plant disease detection

"AgroSmart: Enhancing Agricultural Productivity through Data-Driven Crop Recommendation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.i515-i521, May-2025, Available :http://www.jetir.org/papers/JETIR2505958.pdf

"AgroSmart: Enhancing Agricultural Productivity through Data-Driven Crop Recommendation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppi515-i521, May-2025, Available at : http://www.jetir.org/papers/JETIR2505958.pdf