Systematic Mapping Study on Remote Sensing in Agriculture

Abstract

The area of remote sensing techniques in agriculture has reached a significant degree of development and maturity, with numerous journals, conferences, and organizations specialized in it. Moreover, many review papers are available in the literature. The present work describes a literature review that adopts the form of a systematic mapping study, following a formal methodology. Eight mapping questions were defined, analyzing the main types of research, techniques, platforms, topics, and spectral information. A predefined search string was applied in the Scopus database, obtaining 1590 candidate papers. Afterwards, the most relevant 106 papers were selected, considering those with more than six citations per year. These are analyzed in more detail, answering the mapping questions for each paper. In this way, the current trends and new opportunities are discovered. As a result, increasing interest in the area has been observed since 2000; the most frequently addressed problems are those related to parameter estimation, growth vigor, and water usage, using classification techniques, that are mostly applied on RGB and hyperspectral images, captured from drones and satellites. A general recommendation that emerges from this study is to build on existing resources, such as agricultural image datasets, public satellite imagery, and deep learning toolkits.

Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

“…Unmanned aerial vehicles (UAVs) are widely applied for monitoring and mapping purposes all around the world [1][2][3][4]. The use of relatively low-cost commercial UAV platforms can produce high-resolution visible orthophotos, thus providing an increased resolution product in comparison to the traditional aerial or satellite observations.…”

Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status

“…One theory for this change in position is that stronger vigour is related to higher chlorophyll content, which increases absorption around the red region, thus widening the absorption well and shifting the curve to absorb more energy in longer wavelengths [17]. This is an area of substantial long-term research, however, there are a number of studies showing that the red edge position is impacted by multiple sources, such as water content, salinity and leaf solar geometry, with some divergent results encountered across different studies [14,[17][18][19]. The geometry of the crop surface in the region of interest in relation to both the sun and viewing sensor zenith (vertical) and azimuth (horizontal) angles also has nonlinear impacts on the reflectance received at the sensor, referred to as the bi-directional reflectance distribution function (BRDF) [20].…”

“…With the rise in computer processor speed and capacity as well as available observation data streams, supervised and unsupervised learning methods, collectively referred to as machine learning, have seen increasing use in agricultural remote sensing applications since 2010 [18]. There are a large number of techniques in use, with many of these reviewed by Verrelst et al (2015).…”

Identification of Internal Defects in Potato Using Spectroscopy and Computational Intelligence Based on Majority Voting Techniques

“…Among the non-destructive methods for quality control of products, machine vision and spectroscopy techniques have a promising prospect in agricultural science. These technologies are used in various fields such as analysis of ground and aerial mapping of natural resources, crop monitoring, precision agriculture, robotics, automated guidance, non-destructive inspection of products, quality control, predicting the chemical properties of products, and so on [ 11 ]. Therefore, they can be used to quickly determine the quality of agricultural products both on a laboratory scale and in online processing [ 12 , 13 , 14 ].…”