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Hyperspectral Imaging for Precision Farming

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 5533

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Special Issue Editors

Dr. Stephanie Delalieux

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Guest Editor

Remote Sensing Department, Flemish Institute for Technological Research (VITO-TAP), 2400 Mol, Belgium
Interests: remote sensing; spectral imaging; image processing; precision agriculture; horticulture; disease detection
Special Issues, Collections and Topics in MDPI journals

Dr. Stefan Livens

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Guest Editor

Flemish Institute for Technological Research, Center for Remote Sensing and Earth Observation Processes (VITO-TAP), 2400 Mol, Belgium
Interests: pattern recognition; image processing; computer vision; image analysis; feature selection; wavelet; calibration; classification; hyperspectral image analysis; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision farming aims to optimize agricultural production by measuring variability in crops, both at field and intra-field level. Continuous monitoring and proper understanding of plant physiological processes in relation to the environment is essential to determine suitable actions. Based on the observations, time- and site-specific treatment can be carried out to use the land more effectively and efficiently. Yield, in terms of crop size and quality, can be increased through optimal use of inputs, e.g., water, fertilizers, and pesticides. This optimizes returns while preserving resources.

Precision farming is a key technology to respond to the challenges in modern agriculture: how to increase food production to feed the world’s growing population when the land suitable for agriculture is limited and pressure on the environment needs to be kept under control. Precision farming based on remote sensing observations, implemented using automated systems, can offer good solutions to address this. Therefore, it is rapidly emerging as an enabling technology, and many achievements based on regular RGB and multispectral imaging have been demonstrated in the last few years.

To increase benefits, more detailed information on growth processes, drought stress, and diseases is highly wanted. Hyperspectral imaging can provide such additional information as subtle changes in crops are often revealed in through spectral content, giving early indications of important processes.

Imaging spectroscopy technology has evolved fast, with ever smaller and lighter spectral cameras becoming available. This has allowed hyperspectral imaging to be applied outside traditional lab use and beyond specialized high-end aerial imaging. Today, miniature imagers carried by drones allow for regularly monitoring fields and capturing imagery with information at the desired high spatial and spectral detail. Temporal flexibility allows bridging of the gap between information needs and data availability for precision farming. Combining existing sensors and technologies with crop growth models enables us to issue yield forecasts at a range of spatial scales.

In this framework, this Special Issue aims to publish a collection of investigations into and developments of novel hyperspectral sensors and methods to analyze the images thereof for precision farming applications, including on time series analysis, multiresolution spatiotemporal data fusion, unmixing, inverse modeling, and data assimilation.

Dr. Stephanie Delalieux
Dr. Stefan Livens
Guest Editors

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 submissions that pass pre-check are 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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.

Keywords

hyperspectral
precision agriculture
sensors
vegetation dynamics
yield
productivity
classification
fusion

Published Papers (3 papers)

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21 pages, 88499 KiB

Open AccessArticle

Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Based Hyperspectral Imagery

by Shuang Tian, Qikai Lu and Lifei Wei

Remote Sens. 2022, 14(14), 3292; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143292 - 08 Jul 2022

Cited by 8 | Viewed by 1888 | Correction

Abstract

As an effective approach to obtaining agricultural information, the remote sensing technique has been applied in the classification of crop types. The unmanned aerial vehicle (UAV)-manned hyperspectral sensors provide imagery with high spatial and high spectral resolutions. Moreover, the detailed spatial information, as well as abundant spectral properties of UAV-manned hyperspectral imagery, opens a new avenue to the fine classification of crops. In this manuscript, multiscale superpixel-based approaches are proposed for the fine identification of crops in the UAV-manned hyperspectral imagery. Specifically, the multiscale superpixel segmentation is performed and a series of superpixel maps can be obtained. Then, the multiscale information is integrated into image classification by two strategies, namely pre-processing and post-processing. For the pre-processing strategy, the superpixel is regarded as the minimum unit for image classification, whose feature is obtained by using the average of spectral values of pixels within it. At each scale, the classification is performed on the basis of the superpixel. Then, the multiscale classification results are combined to generate the final map. For the post-processing strategy, the pixel-wise classification is implemented to obtain the label and posterior probabilities of each pixel. Subsequently, the superpixel-based voting is conducted at each scale, and these obtained voting results are fused to generate the multiscale voting result. To evaluate the effectiveness of the proposed approaches, three open-sourced hyperspectral UAV-manned datasets are employed in the experiments. Meanwhile, seven training sets with different numbers of labeled samples and two classifiers are taken into account for further analysis. The results demonstrate that the multiscale superpixel-based approaches outperform the single-scale approaches. Meanwhile, the post-processing strategy is superior to the pre-processing strategy in terms of higher classification accuracies in all the datasets. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Precision Farming)

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23 pages, 4957 KiB

Open AccessArticle

Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

by Hsiang-En Wei, Miles Grafton, Michael Bretherton, Matthew Irwin and Eduardo Sandoval

Remote Sens. 2021, 13(16), 3198; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163198 - 12 Aug 2021

Cited by 8 | Viewed by 2090

Abstract

Monitoring and management of plant water status over the critical period between flowering and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R² = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Precision Farming)

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