Smart Agriculture: Iot-Based Yield Prediction Through Real-Time Soil Analysis and Machine Learning Models
DOI:
https://doi.org/10.63682/jns.v14i4S.6892Keywords:
Smart agriculture, Internet of Things (IoT), machine learning, crop yield prediction, real-time soil analysis, precision farming, soil nutrients, climate variables, data analytics in agriculture, sustainable agricultureAbstract
The inputs of the Internet of Things (IoT) and machine learning (ML) are greatly changing the agricultural sector, forming the core of smart farming. The research examines how sensors connected through the Internet of Things and data predicting methods can improve crop yields in various parts of Maharashtra. A dataset comprising 500 data points, reporting soil factors (pH, EC, OC, N, P, K), weather variables (temperature, humidity, rainfall) and production data was studied using Python for statistics and graphical displays. Key patterns and interactions between variables were found using descriptive statistics, some analysis tools and various types of charts. Researchers found that level of nitrogen in the soil, organic carbon and temperature were all heavily linked to how much the plants yielded. In addition, distributions of yield varied a lot for different crop types and locations, suggesting that differences in climate and soil influence how crops are farmed. As a result, it is clear that using data from IoT in conjunction with analytics can encourage efficient farming that is effective and uses resources appropriately. This study shows that with predictive ML models, smart agriculture can improve the ability of resource-constrained regions to be more sustainable, stable and provide sufficient food.
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