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.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

Remote sensing and its use in detection and monitoring plant diseases: A review

2018 ◽  
Author(s):  
N.K. Gogoi ◽  
B. Deka ◽  
L.C. Bora
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Imaging Techniques ◽  
Crop Protection ◽  
Remote Sensing Data ◽  
Modern Technology ◽  
Plant Diseases ◽  
Resonance Spectroscopy ◽  
Agricultural Crop ◽  
Non Invasive

Remote sensing is a rapid, non-invasive and efficient technique which can acquire and analyze spectral properties of earth surfaces from various distances, ranging from satellites to ground-based platforms. This modern technology holds promise in agricultural crop production including crop protection. Variability in the reflectance spectra of plants resulting from occurrence of disease and pests, allows their identification using remote sensing data. Various spectroscopic and imaging techniques like visible, infrared, multiband and fluorescence spectroscopy, fluorescence imaging, multispectral and hyperspectral imaging, thermography, nuclear magnetic resonance spectroscopy etc. have been studied for the detection of plant diseases. Several of these techniques have great potential in phytopathometry. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results and thereby rendering agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection.

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.

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 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.

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.

scholarly journals UAS-Remote Sensing Methods for Mapping, Monitoring and Modeling Crops

2020 ◽  
Vol 12 (23) ◽  
pp. 3873
Author(s):  
Francisco Javier Mesas-Carrascosa
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Management ◽  
Small Sample ◽  
Special Issue ◽  
Sensor Applications ◽  
Aerial Vehicle ◽  
Continue Research ◽  
Remote Sensing Techniques ◽  
Remote Sensing Methods

The advances in Unmanned Aerial Vehicle (UAV) platforms and on-board sensors in the past few years have greatly increased our ability to monitor and map crops. The ability to register images at ultra-high spatial resolution at any moment has made remote sensing techniques increasingly useful in crop management. These technologies have revolutionized the way in which remote sensing is applied in precision agriculture, allowing for decision-making in a matter of days instead of weeks. However, it is still necessary to continue research to improve and maximize the potential of UAV remote sensing in agriculture. This Special Issue of Remote Sensing includes different applications of UAV remote sensing for crop management, covering RGB, multispectral, hyperspectral and LIght Detection and Ranging (LiDAR) sensor applications on-board (UAVs). The papers reveal innovative techniques involving image analysis and cloud points. It should, however, be emphasized that this Special Issue is a small sample of UAV applications in agriculture and that there is much more to investigate.

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