Hyperspectral Imaging for Precision Farming
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 6494
Special Issue Editors
Interests: remote sensing; spectral imaging; image processing; precision agriculture; horticulture; disease detection
Special Issues, Collections and Topics in MDPI journals
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
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Keywords
- hyperspectral
- precision agriculture
- sensors
- vegetation dynamics
- yield
- productivity
- classification
- fusion
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