Journal Article

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

Getahun, Masresha, Kumar, K. Sree, Al-Rasheed, Amal, Srivastava, Durgesh, Logeshwaran, J., Rex, M. Jenolin, Soufiene, Ben Othman

2024

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Agricultural practices / Agricultural production / Agriculture / Agriculture - methods / Agro deep learning framework / Algorithms / Artificial intelligence / Bioinformatics / Bioinformatics in agriculture / Biomedical and Life Sciences / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Crop diseases / Crop growth / Crop management / Crop production / Crop Production - methods / Crop yield / Crop yield prediction / Crops / Crops, Agricultural - growth & development / Data analysis / Data mining / Datasets / Decision making / Deep Learning / Efficiency / Farmers / Farming / Internet of Things / Life Sciences / Machine learning / Methods / Microarrays / Neural networks / Pest control / Plant diseases / Precision agriculture / Precision farming / Production processes / Productivity / Sensors / Soil moisture / Soil temperature / Technology application / Wheat