Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Improving crop production using an agro-deep learning framework in precision agriculture Subject The topic of the resource Agricultural technology; Agro deep learning framework; Artificial intelligence in agriculture; Crop monitoring; Crop yield prediction; Data analysis; Deep learning; Neural networks; Precision agriculture; Smart farming Description An account of the resource 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. The Author(s) 2024. Creator An entity primarily responsible for making the resource Logeshwaran J.; Srivastava D.; Kumar K.S.; Rex M.J.; Al-Rasheed A.; Getahun M.; Soufiene B.O. Source A related resource from which the described resource is derived BMC Bioinformatics, Vol-25, No. 1 Publisher An entity responsible for making the resource available BioMed Central Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1186/s12859-024-05970-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1186/s12859-024-05970-9</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208290747&amp;doi=10.1186%2Fs12859-024-05970-9&amp;partnerID=40&amp;md5=5837d9936542670bbdfb60cfd5b797c8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208290747&amp;doi=10.1186%2fs12859-024-05970-9&amp;partnerID=40&amp;md5=5837d9936542670bbdfb60cfd5b797c8</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 14712105; PubMed ID: 39487390; CODEN: BBMIC Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Logeshwaran J., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Srivastava D., Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India; Kumar K.S., Department of Computing Technologies, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Rex M.J., Department of Computing Technologies, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Al-Rasheed A., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Getahun M., Department of Computer Science and Information Technology, College of Engineering and Technology, Kebri Dehar University, Kebri Dehar, Ethiopia; Soufiene B.O., PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia