article · BMC Bioinformatics
A deep learning system known as the Agro Deep Learning Framework has been developed to enhance precision agriculture. By analysing large volumes of environmental data, including soil moisture, temperature, and humidity, the framework models and predicts crop behaviour to support farm-level decision-making. In evaluations, the model attained an overall accuracy of 85.41 percent, an F1-score of 88.91 percent, a precision of 84.87 percent, and a recall of 84.24 percent. These predictive capabilities allow the framework to detect emerging crop cultivation problems early, helping farmers manage resources more effectively and mitigate agricultural losses. The findings demonstrate the viability of applying advanced artificial intelligence to optimise crop management and boost farming yields, with future development needed to test performance across varied crop types and diverse agricultural settings.
Precision agriculture relies on timely insights to prevent crop failure and maximise harvest yields. By applying deep learning to routine environmental data like humidity and soil moisture, farm managers can identify crop stress before visible damage occurs. This approach supports sustainable food production by helping agricultural operations reduce waste, cut crop losses, and make data-informed operational decisions.
The framework is intended for decision-support tools used by farmers and farm managers to guide crop management. It represents early-stage, algorithm-level research tested on environmental data, currently sitting at a distance from commercial deployment. Translating the framework into a market-ready tool will require software integration with sensor systems and further validation across diverse crops and real-world farm environments.
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BACKGROUND: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity. RESULTS: The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses. CONCLUSIONS: The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments.
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DOI: 10.1186/s12859-024-05970-9
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