scholarly journals Applications of Remote Sensing in Precision Agriculture: A Review

2020 ◽  
Vol 12 (19) ◽  
pp. 3136
Author(s):  
Rajendra P. Sishodia ◽  
Ram L. Ray ◽  
Sudhir K. Singh
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Data Storage ◽  
Precision Agriculture ◽  
Crop Production ◽  
Irrigation Management ◽  
Green Revolution ◽  
Vegetation Indices ◽  
Past Century ◽  
The Past

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

scholarly journals Contribution of the Black Sea observing system to ECOOP

2011 ◽  
Vol 8 (4) ◽  
pp. 1695-1722 ◽  
Author(s):  
G. K. Korotaev ◽  
V. L. Dorofeev ◽  
S. V. Motyzhev ◽  
V. N. Belokopytov ◽  
A. Palazov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Black Sea ◽  
The Black Sea ◽  
Past Century ◽  
Network Integration ◽  
The Past ◽  
Time Operation ◽  
Real Time Operation ◽  
Set Up

Abstract. Regular observations in the Black Sea basin started in the past century, and quite good multidisciplinary observing system operated in the 70–80ies based on the ship observations. Modern oceanographic observing system in the basin is built according to the GOOS principles. It includes space remote sensing observations, data of free floating buoys and costal observational network. Integration of the observing system and its real-time operation were started within the framework of the FP5 ARENA project and later were improved during the FP6 ASCABOS project. The coastal observing system which includes time series from the coastal platforms and multidisciplinary surveys of the coastal areas fulfilled by the research vessels was set up during the ECOOP. Paper describes all components of the Black Sea observing system operated during the ECOOP project and its applications in the framework of the project.

scholarly journals PRECISION VITICULTURE FROM MULTITEMPORAL, MULTISPECTRAL VERY HIGH RESOLUTION SATELLITE DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 919-925
Author(s):  
Z. Kandylakis ◽  
K. Karantzalos
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Quality Parameters ◽  
Total Phenolic ◽  
Canopy Reflectance ◽  
Northern Greece ◽  
In Situ Data ◽  
Very High

In order to exploit efficiently very high resolution satellite multispectral data for precision agriculture applications, validated methodologies should be established which link the observed reflectance spectra with certain crop/plant/fruit biophysical and biochemical quality parameters. To this end, based on concurrent satellite and field campaigns during the veraison period, satellite and in-situ data were collected, along with several grape samples, at specific locations during the harvesting period. These data were collected for a period of three years in two viticultural areas in Northern Greece. After the required data pre-processing, canopy reflectance observations, through the combination of several vegetation indices were correlated with the quantitative results from the grape/must analysis of grape sampling. Results appear quite promising, indicating that certain key quality parameters (like brix levels, total phenolic content, brix to total acidity, anthocyanin levels) which describe the oenological potential, phenolic composition and chromatic characteristics can be efficiently estimated from the satellite data.

THE APPLICATION OF REMOTE SENSING IN PRECISION AGRICULTURE

1970 ◽  
pp. 10-16
Author(s):  
S. Yu. Blokhina
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Crop Yields ◽  
Temporal Frequency ◽  
Organic Matter Content ◽  
Sensing Applications ◽  
Remote Sensing Applications

The paper provides an overview of foreign literature on the remote sensing applications in precision agriculture. Remote sensing applications in precision agriculture began with sensors for soil organic matter content, and have quickly advanced to include hand held sensors to tractor or aerial or satellite mounted sensors. Wavelengths of electromagnetic radiation initially focused on a few key visible or near infrared bands, and nowadays electromagnetic wavelengths in use range from the ultraviolet to microwave portions of the spectrum. Spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of crop stress, crop biophysical or biochemical characteristics and specific compounds. A variety of spectral indices have been widely implemented within various precision agriculture applications, rather than a focus on only normalized difference vegetation indices. Spatial resolution and temporal frequency of remote sensing imagery has increased significantly, allowing evaluation of soil and crop properties at fine spatial resolution at the expense of increased data storage and processing requirements. At present there is considerable interest in collecting remote sensing for operational management of soil and crop yields, as well as control over the spread of pests and weeds practically in real time.

Data Acquisition Considering of Fixed-Wing UAVs in Mountainous Areas

2014 ◽  
Vol 543-547 ◽  
pp. 2151-2154
Author(s):  
Ling Li Zhao ◽  
Shuai Liu ◽  
Li Ma
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Data Acquisition ◽  
Remote Sensing Data ◽  
Landing Site ◽  
Unmanned Aircraft ◽  
Military Operations ◽  
The Past ◽  
Sensing System ◽  
Sensing Technology

Over the past decade, there has been a great demand of Unmanned Aerial Vehicles (UAVs) in numerous industrial and military operations around the world. This paper is focused on low fixed-wing UAV remote sensing system, put remote sensing technology and UAV technology closely to fixed-wing unmanned aircraft as a platform, which is equipped with high-resolution digital remote sensing sensors, it has easy transition since the airport does not depend on landing site, it is a new low-speed high-resolution remote sensing data acquisition system. It has capability of a survey of real-time quick monitoring, and has been an effective complement to conventional means for satellite remote sensing and aerial photography.

scholarly journals Development of multivariate integrated drought monitoring index (MIDMI) for Warangal region of Telangana, India

2021 ◽  
Author(s):  
Mahesh R. Tapas ◽  
Uday Kumar ◽  
Sudhakar Mogili ◽  
K. V. Jayakumar
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Crop Production ◽  
Continuous Monitoring ◽  
Irrigation Management ◽  
Weighted Average ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Landsat 8 ◽  
Percentage Area

Abstract Agricultural drought is one of the most frequent natural disasters in India's southern part. Remote sensing-based drought indices give advantages in terms of continuous monitoring of land surface. The crop production in the Warangal region in India's southern part is adversely affected due to insufficient rainfall and poor irrigation management. This study aims to develop a multivariate remote sensing-based composite drought index (CDI) to monitor the agricultural drought. Landsat-8 satellite data for all the 11 subregions of Warangal urban and 15 subregions of the rural district of Telangana from 2013 to 2020 for the month of May is used to obtain drought indices. The drought indices are used in this study to develop MIDMI and are compared according to the percentage area of the Warangal region under five different drought categories. In this study, the MIDMI is computed by a weighted average of five vegetation drought indices for the Warangal region as per the method developed by Iyengar and Sudarshan for the multivariate data. MIDMI for all the 26 subregions of the Warangal rural and Warangal Urban Districts is between 0.4 and 0.6, which makes the Warangal region moderately vulnerable to agricultural drought.

scholarly journals DenseResUNet: An Architecture to Assess Water-Stressed Sugarcane Crops from Sentinel-2 Satellite Imagery

2021 ◽  
Vol 38 (4) ◽  
pp. 1131-1139
Author(s):  
Shyamal S. Virnodkar ◽  
Vinod K. Pachghare ◽  
Virupakshagouda C. Patil ◽  
Sunil Kumar Jha
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Crop Production ◽  
Vegetation Indices ◽  
Detection Threshold ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Visual Signal ◽  
Agricultural Crop ◽  
Remotely Sensed Data

A single most immense abiotic stress globally affecting the productivity of all the crops is water stress. Hence, timely and accurate detection of the water-stressed crops is a necessary task for high productivity. Agricultural crop production can be managed and enhanced by spatial and temporal evaluation of water-stressed crops through remotely sensed data. However, detecting water-stressed crops from remote sensing images is a challenging task as various factors impacting spectral bands, vegetation indices (VIs) at the canopy and landscape scales, as well as the fact that the water stress detection threshold is crop-specific, there has yet to be substantial agreement on their usage as a pre-visual signal of water stress. This research takes the benefits of freely available remote sensing data and convolutional neural networks to perform semantic segmentation of water-stressed sugarcane crops. Here an architecture ‘DenseResUNet’ is proposed for water-stressed sugarcane crops using segmentation based on encoder-decoder approach. The novelty of the proposed approach lies in the replacement of classical convolution operation in the UNet with the dense block. The layers of a dense block are residual modules with a dense connection. The proposed model achieved 61.91% mIoU, and 80.53% accuracy on segmenting the water-stressed sugarcane fields. This study compares the proposed architecture with the UNet, ResUNet, and DenseUNet models achieving mIoU of 32.20%, 58.34%, and 53.15%, respectively. The results of this study reveal that the model has the potential to identify water-stressed crops from remotely sensed data through deep learning techniques.

scholarly journals APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 989-992 ◽  
Author(s):  
C. Yao ◽  
Y. Zhang ◽  
Y. Zhang ◽  
H. Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Precision Agriculture ◽  
Rapid Development ◽  
Reference Value ◽  
Remote Sensing Images ◽  
Training Samples ◽  
Traditional Classification ◽  
Crop Classification

With the rapid development of Precision Agriculture (PA) promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN). For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

scholarly journals Practical Applications of Remote Sensing Technology—An Industry Perspective

2000 ◽  
Vol 10 (3) ◽  
pp. 475-480 ◽  
Author(s):  
John LeBoeuf
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Management Practices ◽  
Plant Stress ◽  
Economic Benefits ◽  
Learning By Doing ◽  
Accurate Information ◽  
Fresh Market ◽  
Practical Applications

The initial surge of interest in precision agriculture technologies exhibited by innovators and early adopters involved in crop production appears to have crossed over an important threshold. As valuable field experience increases and learning by doing advances, successful applications of management practices are being identified even though few are adequately documented with economic benefits. Access to accurate information pertaining to applications of site-specific management would be expected to motivate more producers to incorporate technology uses with crop production. This next group of producers has been watching technology developments as they preferred to avoid risk and wait for identifiable benefits. Waiting for detailed case studies involving high value fruits and vegetables may be the wrong approach to take. Fierce competition and strict confidentiality are expected in the fresh market industry. Thus, personal experience with technology becomes more relevant to innovative producers than published literature. This is especially true in California where 350 different crops are produced. High resolution imagery from digital aerial and satellite sensors has been used in crop production in California to identify plant stress, direct plant tissue and soil sampling efforts, and provide information for analysis and interpretation of crop growth. Examples of remote sensing imagery that have provided valuable in-season progress reports will be identified. The focus will be on practice, not theory, as seen from an industry perspective.

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