ItemArticle

Improving crop production using an agro-deep learning framework in precision agriculture

Title
Improving crop production using an agro-deep learning framework in precision agriculture
Creator
Logeshwaran J.; Srivastava D.; Kumar K.S.; Rex M.J.; Al-Rasheed A.; Getahun M.; Soufiene B.O.
Description
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.
Source
BMC Bioinformatics, Vol-25, No. 1
Date
2024-01-01
Publisher
BioMed Central Ltd
Subject
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
Coverage
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
Rights
All Open Access; Gold Open Access
Relation
ISSN: 14712105; PubMed ID: 39487390; CODEN: BBMIC
Format
Online
Language
English
Type
Article

Collection

Articles

Citation

Logeshwaran J.; Srivastava D.; Kumar K.S.; Rex M.J.; Al-Rasheed A.; Getahun M.; Soufiene B.O., “Improving crop production using an agro-deep learning framework in precision agriculture,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/12685.

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