Soil Health Assessment and Crop Recommendation Using Deep Neural Networks: A Data-Driven Approach for Sustainable Agriculture
Keywords:
Agricultural informatics, Crop recommendation, Data-driven agriculture, Deep Neural Networks (DNN), Machine learning, Precision agriculture, Soil health, Sustainable farmingAbstract
Soil health determines agricultural productivity and sustainable farming practices. Traditional soil evaluation methods are usually time-consuming, labor-oriented, and unscalable. In this research, we present a data-driven framework using Deep Neural Networks (DNN) to evaluate soil health and recommend crops. Applying structured soil parameters like nitrogen, phosphorus, potassium, pH, temperature, humidity, and rainfall, we train a DNN model to recommend the best-suited crop according to given soil parameters. The model shows superior prediction accuracy compared to standard machine learning models. Our technique not only automates soil health evaluation but also guides farmers and agricultural planners in making informed choices, hence promoting precision agriculture. The findings confirm the capability of DNN-based approaches to boost agricultural productivity by leveraging intelligent analysis of data.
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