AI-Driven Models for Assessing Soil Nutrient Dynamics in Organic and Conventional Farming Systems
DOI:
https://doi.org/10.64137/XXXXXXXX/IJAES-V1I1P102Keywords:
Artificial intelligence, Soil nutrient dynamics, Organic farming, Conventional farming, Precision agriculture, Machine learning, Deep learning, Soil healthAbstract
The need for advanced technologies to monitor and then optimize soil nutrient dynamics is growing, as the world demands an increased use of sustainable agriculture. In this paper, we undertake a full investigation of the application of models of soil nutrient dynamics informed by Artificial Intelligence (AI) in organic and conventional farming systems. Conventional approaches to soil assessment are labor and time-intensive, often inconsistent, whereas AI models provide a scalable, cheaper and accurate answer. For the nutrient prediction, we explore machine learning (ML) algorithms, the deep learning (DL) frameworks and the hybrid models which include satellite imagery, weather data and in situ sensor readings. The relative differences in nutrient cycling patterns, microbial interactions and external input dependencies among organic and conventional systems are the focus of our study. We also examine how the effects of AI models can be used to optimize fertilizer application, decrease environmental impact and increase crop yield. Performance of the model is validated across different geographies and soil types through the use of case studies and comparative analyses. The results show that AI-driven models could greatly improve the accuracy of nutrient management tactics for organic and conventional farmers. Our findings provide insights into agricultural policy development, the application of precision agriculture practices and the role of AI in the future of agrotechnology
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