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Crop Cultivation Prediction Using Real-Time Dataset
Crop Cultivation Prediction Using Real-Time Dataset
Khushi A Babar Computer science And Engineering
KLS VDIT, Haliyal
Prof.Nirmala Ganiger Computer science and engineering
KLS VDIT Haliyal
Rohitha N Bedare lComputer Science And Engineering
KLS VDIT, Haliyal
line 5: email address or ORCID
Rutuja B Tonape Computer science And Engineering
KLS VDIT, Haliyal
line 5: email address or ORCID
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.
