A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications

• Published in: Ecological informatics 2022-07, Vol.69, p.101678, Article 101678
• Main Authors: Khan, Atiya, Vibhute, Amol D., Mali, Shankar, Patil, C.H.
• Format: Article
• Language: English
• Subjects: Crop classification; Deep learning; Hyperspectral imaging; Machine learning; Precision agriculture; Remote sensing
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2022.101678

The globe's population is increasing day by day, which causes the severe problem of organic food for everyone. Farmers are becoming progressively conscious of the need to control numerous essential factors such as crop health, water or fertilizer use, and harmful diseases in the field. However, it is challenging to monitor agricultural activities. Therefore, precision agriculture is an important decision support system for food production and decision-making. Several methods and approaches have been used to support precision agricultural practices. The present study performs a systematic literature review on hyperspectral imaging technology and the most advanced deep learning and machine learning algorithm used in agriculture applications to extract and synthesize the significant datasets and algorithms. We reviewed legal studies carefully, highlighted hyperspectral datasets, focused on the most methods used for hyperspectral applications in agricultural sectors, and gained insight into the critical problems and challenges in the hyperspectral data processing. According to our study, it has been found that the Hyperion hyperspectral, Landsat-8, and Sentinel 2 multispectral datasets were mainly used for agricultural applications. The most applied machine learning method was support vector machine and random forest. In addition, the deep learning-based Convolutional Neural Networks (CNN) model is mainly used for crop classification due to its high performance with hyperspectral datasets. The present review will be helpful to the new researchers working in the field of hyperspectral remote sensing for agricultural applications with a machine and deep learning methods. •Hyperspectral image data and deep/machine learning methods have been synthesized.•The most used hyper and multispectral data is EO-1 Hyperion, Landsat and Sentinel.•The most used machine learning methods are SVM, random forest, and neural networks.•The most widely used deep learning methods are convolutional neural networks.•Python is the most preferred language for developing deep learning models.