Year: 2025
Type: article
Abstract: —This study proposes a machine learning-based framework for crop prediction using real-time agricultural data. The model incorporates essential environmental parameters, including temperature, rainfall, humidity, and soil ph. To understand how crop conditions change over time, we use specialized AI tools called Recurrent Neural Networks (RNNs) - with a focus on the particularly effective Long Short-Term Memory (LSTM) models. These models help in effectively forecasting suitable crops. The system works like a well-organized farming season - first we collect all the necessary field information, then we clean and prepare the data like preparing soil for planting. Next, we train our smart algorithms just like training farm workers, and finally we present the findings through clear, colorful charts that anyone can understand at a glance. all aimed at supporting farmers in making informed, data-driven agricultural decisions.
DOI: 10.55041/ijsrem48770
Author: Prof.Nirmala Ganiger
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4410739547