Special Issue "Hyperspectral Imaging Technique in Agriculture"

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editor

Dr. Zongmei Gao
E-Mail Website
Guest Editor
Department of Neurology, Columbia University, New York, NY 10033, USA
Interests: data mining; machine learning; image processing; deep learning

Special Issue Information

Dear Colleagues,

In recent years, precision agriculture (PA) has been urgently needed for whole-farm management. Optical sensing, which is among the most useful PA tools, has been widely applied in detecting crop and food responses. Among these techniques, hyperspectral imaging has been adopted as a powerful method to achieve such targets. HSI acquires a three-dimensional dataset called a hypercube, with two spatial dimensions and one spectral dimension, from visible to near-infrared or even short infrared wavelengths. Spatially resolved spectral imaging obtained by HSI provides diagnostic information on tissue physiology, morphology, and composition. This Special Issue will focus on hyperspectral imaging techniques in agriculture—from the fundamentals of sensing systems to novel applications for agricultural purposes.

The large-scale farming of agricultural crops requires on-time detection management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, while the application of such a technique is affected by the precision and accuracy under field conditions. In addition to sensors, reducing the high dimensionality of hypercubes is also becoming important for agriculture. Hyperspectral imaging applied in labs or fields could contribute to improvements in data collection, data mining, and result validation.

This Special Issue aims to promote the development of hyperspectral imaging techniques in agriculture. We would like to invite the scientific community to submit their research related to hyperspectral imaging in agriculture. Contributions could include, but are not limited to, the following: precision agriculture, hyperspectral imaging, smart farming, remote sensing, platforms and sensors, machine vision, robotics in agriculture, the Internet of Things, machine learning, and deep learning.

Dr. Zongmei Gao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • hyperspectral imaging
  • precision agriculture
  • remote sensing
  • platforms and sensors
  • machine vision
  • Internet of Things
  • machine learning
  • deep learning
  • artificial intelligence

Published Papers (1 paper)

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The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat
AgriEngineering 2021, 3(4), 924-941; https://doi.org/10.3390/agriengineering3040058 (registering DOI) - 25 Nov 2021
Viewed by 200
Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the [...] Read more.
Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the key tools to manage crown rot, because it is necessary to understand disease infection conditions, identify the severity of infection, and discover potential resistant varieties. However, screening crown rot is challenging as there are no clear visible symptoms on leaves at early growth stages. Hyperspectral imaging (HSI) technologies have been successfully used to better understand plant health and disease incidence, including light absorption rate, water and nutrient distribution, and disease classification. This suggests HSI imaging technologies may be used to detect crown rot at early growing stages, however, related studies are limited. This paper briefly describes the symptoms of crown rot disease and traditional screening methods with their limitations. It, then, reviews state-of-art imaging technologies for disease detection, from color imaging to hyperspectral imaging. In particular, this paper highlights the suitability of hyperspectral-based screening methods for crown rot disease. A hypothesis is presented that HSI can detect crown-rot-infected plants before clearly visible symptoms on leaves by sensing the changes of photosynthesis, water, and nutrients contents of plants. In addition, it describes our initial experiment to support the hypothesis and further research directions are described. Full article
(This article belongs to the Special Issue Hyperspectral Imaging Technique in Agriculture)
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