Multimodal Remote Sensing and Machine Learning for Precision Agriculture: A Review

O. B. Falana *

Department of Biosystems Engineering, Auburn University, Alabama, United States.

O. I. Durodola

Department of Biosystems Engineering, Auburn University, Alabama, United States.

*Author to whom correspondence should be addressed.


This study proposed various machine learning and deep learning techniques to integrate and analyze varieties of data in precision agriculture systems. Agricultural systems have undergone a digital transformation, which has resulted in the evolution of many management components into artificially intelligent systems to better value the ever-increasing amounts of data. In the process of putting in place farming systems that are based on knowledge, several obstacles can be overcome using machine learning. The data obtained are transmitted to on-site storage, where extraction, loading, and transformation are performed. The data is preprocessed and transferred to the AWS (Amazon Web Services) cloud (Amazon S3 Bucket). The best model is deployed such that new data can be fit into the model to make adequate prediction or classification. Such a solution can be adapted by building an algorithm to simulate the AWS machine learning technique. A small-scale pilot project can be executed, and the output of the prediction or classification model can be displayed using a web-based software or mobile app.

Keywords: Data fusion, deep learning, intelligent system, machine learning, remote sensing

How to Cite

Falana, O. B., & Durodola, O. I. (2022). Multimodal Remote Sensing and Machine Learning for Precision Agriculture: A Review. Journal of Engineering Research and Reports, 23(8), 30–34.


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