Enhancing Crop Cultivation through Weather Forecasting and Data Analytics

Linking meteorological and climatic data with agricultural decision-making poses significant challenges.Farmers currently have access to a vast array of weather and climate information, yet much of this data is either operational or still in development.Specifically, the forecasts derived from climatic data may not adequately meet the needs of farmers during their decision-making processes.To effectively leverage natural factors, it is essential for farmers to incorporate and comprehend these elements in their decision-making.Meteorological data related to agriculture, especially climatic information, plays a crucial role in planning agricultural production.Therefore, understanding climatic conditions is vital for informed decision-making.Key factors can be determined through the systematic recording of hourly, daily, and weekly data on temperature, rainfall, solar radiation, wind speed, evaporation, relative humidity, and evapotranspiration.The use of Artificial Neural Networks (ANNs) not only facilitates data analysis but also enables the model to learn from the input data.This paper introduces a predictive analysis aimed at identifying the most suitable crops for specific weather conditions.Additionally, it proposes a hybrid recommender system that integrates Case-Based Reasoning (CBR) to enhance the system's effectiveness.The novelty of this approach lies in its district-wise analysis of agricultural data to predict future climatic conditions and recommend crops accordingly, taking into account the local agricultural patterns through a hybrid recommender system.