Infrared and visible image fusion methods and applications: A survey

Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

Remote sensing for agricultural applications: A meta-review

Remotely Piloted Aircraft (RPA) is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles (UAVs) or more extensively Unmanned Aerial Systems (UAS) are platforms considered under the RPAs paradigm. Simultaneously, the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus, the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First, some examples of typical platforms used in remote sensing are provided. Second, a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third, multi-UAVs in collaboration, coordination, and cooperation in remote sensing are considered. Finally, a collection of applications in several areas are proposed, where the combination of unmanned platforms and sensors, together with methods, algorithms, and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas, each independent from the others, so that the reader does not need to read the full paper when a specific application is of interest

Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)

Nitrogen (N) plays a key role in the plant life cycle. It is the main plant mineral nutrient needed for chlorophyll production and other plant cell components (proteins, nucleic acids, amino acids). Crop yield is affected by plant N status. Thus, the optimization of nitrogen fertilization has become the object of intense research due to its environmental and economic impact. This article focuses on reviewing current methods and techniques used to determine plant N status. Kjeldahl digestion and Dumas combustion have been used as reference methods for N determination in plants, but they are destructive and time consuming. By using spectroradiometers, reflectometers, imagery from satellite sensors and digital cameras, optical properties have been measured to estimate N in plants, such as crop canopy reflectance, leaf transmittance, chlorophyll and polyphenol fluorescence. High correlation has been found between optical parameters and plant N status, and those techniques are not destructive. However, some drawbacks include chlorophyll saturation, atmospheric and soil interference, and the high cost of instruments. Electrical properties of plant tissue have been used to estimate quality in fruits, and water content in plants, as well as nutrient deficiency, which suggests that they have potential for use in plant N determination.

https://www.mdpi.com/1424-8220/13/8/10823/pdf

A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances

https://hal.archives-ouvertes.fr/hal-00613201/document

Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat

https://hal.archives-ouvertes.fr/hal-01118700/document

In-field crop row phenotyping from 3D modeling performed using Structure from Motion

https://hal.archives-ouvertes.fr/hal-01603342/document

Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery

https://hal.archives-ouvertes.fr/hal-01196894/document

From pixel to vine parcel: A complete methodology for vineyard delineation and characterization using remote-sensing data

Spectrometric and analytical techniques in general collect multivariate signals from chemical or biological materials by means of a specific measurement instrumentation, usually in order to characterize or classify them through the estimation of one of several compounds of interest. However, measurement conditions might induce various additive (baseline) or multiplicative effects on the collected signals, which may jeopardize the accuracy and generalizability of estimation models. A common way of dealing with such issues is signal normalization and in particular, when the baseline is constant, the standard normal variate (SNV) transform. Despite its efficiency, SNV has important drawbacks, in terms of physical interpretation and robustness of estimation models, because all the variables are equally considered, independently on what their actual relationship with the response(s) of interest is. In the present study, a novel algorithm is proposed, named variable sorting for normalization (VSN). This algorithm automatically produces, for a given set of multivariate signals, a weighting function favoring signal variables that are only impacted by additive and multiplicative effects, and not by the response(s) of interest. When introduced in SNV preprocessing, this weighting function significantly improves signal shape and model interpretation. Moreover, VSN can be successfully used not only for constant but also with more complex baselines, such as polynomial ones. Together with the description of the theory behind VSN, its application on various synthetic multivariate data, as well as on real SWIR spectral data, is presented and discussed.

Gilles Rabatel

Papers

Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat

In-field crop row phenotyping from 3D modeling performed using Structure from Motion

Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery

From pixel to vine parcel: A complete methodology for vineyard delineation and characterization using remote-sensing data

VSN: Variable sorting for normalization