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.

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.

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 Research of rice crops in Krasnodar region by remote sensing data

2020 ◽  
Vol 175 ◽  
pp. 01004
Author(s):  
Sergey Garkusha ◽  
Mikhail Skazhennik ◽  
Evgeny Kiselev ◽  
Vitaliy Chizhikov ◽  
Alexey Petrushin
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Remote Sensing Data ◽  
Test Site ◽  
Experimental Methodology ◽  
Federal State ◽  
Automated Mapping ◽  
Yield Information ◽  
Geoinformation Modeling ◽  
Sentinel 2A

The concept of digitalization of agricultural production in the Russian Federation provides for the implementation of measures to develop and create a system of geographic information monitoring and decision support in crop production. The aim of the research was to conduct geoinformation monitoring of rice crops to develop methods for automated mapping of their condition and yield forecasting. The studies were carried out on a test site of the Federal State Budgetary Scientific Institution “Federal Scientific Rice Centre” with an area of 274 hectares. The survey was performed by a quadcopter with a MicaSense RedEdge-M multispectral camera mounted on a fixed suspension. The shooting period using an unmanned aerial vehicle (UAV) was limited to early June and additionally used the Sentinel-2A satellite. To assess the state of rice crops, the normalized relative vegetative index NDVI was used. Based on the NDVI distribution and yield information from the combine TUCANO 580 (CLAAS), a statistical analysis was carried out in fields 7 and 9. Testing of the experimental methodology for monitoring crops in 2019 on the basis of remote sensing of test plots and geoinformation modeling and the statistical apparatus should be considered satisfactory.

scholarly journals METHODS OF DETECTION OF DISEASES ON WHEAT CROPS ACCORDING TO REMOTE SENSING (overview)

2019 ◽  
Vol 48 (6) ◽  
pp. 76-89
Author(s):  
O. A. Dubrovskaya ◽  
T. A. Gurova ◽  
I. A. Pestunov ◽  
K. Yu. Kotov
Keyword(s):  
Remote Sensing ◽  
Early Detection ◽  
Crop Production ◽  
Crop Protection ◽  
Hyperspectral Data ◽  
Fungal Diseases ◽  
Crop Damage ◽  
Optical Methods ◽  
Agricultural Crops ◽  
Wheat Diseases

Nowadays multi- and hyperspectral data of remote sensing is widely used in many countries worldwide for agricultural lands monitoring. The issue of their application for detection and assessment of infestation of agricultural crops, damage from diseases and weeds is understudied both in Russia and abroad. Early detection and accurate diagnosis of various wheat diseases are key factors in crop production, contributing to the reduction of qualitative and quantitative crop losses, as well as improving the effectiveness of protective measures. The paper presents a review of up-to-date methods for detecting diseases and assessing the extent of crop damage by remote sensing of wheat using optical imaging systems, the most promising of which is hyperspectral imaging equipment. The identification spectra of healthy plants and the ones with signs of damage from the main fungal diseases as well as the correlation of spectra with the degree of damage are shown. To be able to effectively use the results of diagnostics and detection of diseases, the informational value of the spectral indices of vegetation in the detection of diseases is presented. A table of vegetation indices is given, calculated from the values of reflection coefficients in wide and narrow spectral ranges when determining wheat diseases. The use of optical methods in the monitoring of the main fungal diseases of wheat will accurately identify lesions of crops, reliably diagnose diseases and the extent of plant damage from diseases, and thereby provide support to agricultural producers in decision-making on timely and effective crop protection measures. The results of the review will be used to develop digital technology of early detection and lesion focalization of spring wheat and other agricultural crops.

scholarly journals Integrated Archaeological Research: Archival Resources, Surveys, Geophysical Prospection and Excavation Approach at an Execution and Burial Site: The German Nazi Labour Camp in Treblinka

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 336
Author(s):  
Sebastian Różycki ◽  
Rafał Zapłata ◽  
Jerzy Karczewski ◽  
Andrzej Ossowski ◽  
Jacek Tomczyk
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Archaeological Research ◽  
Burial Site ◽  
Sensing Data ◽  
Non Invasive ◽  
Geophysical Surveys ◽  
Forced Labour ◽  
Labour Camp

This article presents the results of multidisciplinary research undertaken in 2016–2019 at the German Nazi Treblinka I Forced Labour Camp. Housing 20,000 prisoners, Treblinka I was established in 1941 as a part of a network of objects such as forced labour camps, resettlement camps and prison camps that were established in the territory of occupied Poland from September 1939. This paper describes archaeological research conducted in particular on the execution site and burial site—the area where the “death pits” have been found—in the so-called Las Maliszewski (Maliszewa Forest). In this area (poorly documented) exhumation work was conducted only until 1947, so the location of these graves is only approximately known. The research was resumed at the beginning of the 21st century using, e.g., non-invasive methods and remote-sensing data. The leading aim of this article is to describe the comprehensive research strategy, with a particular stress on non-invasive geophysical surveys. The integrated archaeological research presented in this paper includes an analysis of archive materials (aerial photos, witness accounts, maps, plans, and sketches), contemporary data resources (orthophotomaps, airborne laser scanning-ALS data), field work (verification of potential objects, ground penetrating radar-GPR surveys, excavations), and the integration, analysis and interpretation of all these datasets using a GIS platform. The results of the presented study included the identification of the burial zone within the Maliszewa Forest area, including six previously unknown graves, creation of a new database, and expansion of the Historical-GIS-Treblinka. Obtained results indicate that the integration and analyses within the GIS environment of various types of remote-sensing data and geophysical measurements significantly contribute to archaeological research and increase the chances to discover previously unknown “graves” from the time when the labour camp Treblinka I functioned.

scholarly journals Allelopathy and Agricultural Sustainability: Implication in weed management and crop protection—an overview

2019 ◽  
Vol 5 (2) ◽  
pp. 54-61
Author(s):  
Zahir Muhammad ◽  
Naila Inayat ◽  
Abdul Majeed ◽  
Hazrat Ali ◽  
Kaleem Ullah ◽  
...  
Keyword(s):  
Weed Management ◽  
Crop Production ◽  
Plant Pathogens ◽  
Crop Protection ◽  
Soil Health ◽  
Crop Plants ◽  
Plant Diseases ◽  
Agricultural Sustainability ◽  
Biotic And Abiotic Stresses ◽  
Diverse Plant

Abstract Crop plants have defined roles in agricultural production and feeding the world. They are affected by several environmental and biological stresses, which range from soil salinity, drought, and climate change to exposure to diverse plant pathogens. These stresses pose risk to agricultural sustainability. To avoid the increasing biotic and abiotic pressure on crop plants, agrochemicals are extensively used in agriculture for attaining desirable yield and production of crops. However, the use of agrochemicals is also challenging the integrity of ecosystems. Thus, to maintain the integrity of ecosystem, sustainable measures for elevated crop production are required. Allelopathy, a process of chemical interactions between plants and other organisms, could be used in the management of several biotic and abiotic stresses if the basic mechanisms of the phenomena and plants with allelopathic potentials are known. Allelopathy has a promising future for its application in agriculture for natural weed management, improving soil health and suppressing plant diseases. The aim of this review is to discuss the importance of allelopathy in agriculture and its role in sustainability with a specific focus on weed management and crop protection.

scholarly journals Association between drought and agricultural productivity using remote sensing data: a case study of Gujarat state of India

2020 ◽  
Vol 11 (S1) ◽  
pp. 189-202 ◽  
Author(s):  
Koyel Sur ◽  
M. M. Lunagaria
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Standardized Precipitation Index ◽  
Remote Sensing Data ◽  
Meteorological Drought ◽  
Drought Indices ◽  
Small Scale ◽  
Agricultural Crop ◽  
Vegetation Condition ◽  
Spatio Temporal

Abstract Drought is a complex hazard which directly affects the water balance of any region. It impacts agricultural, ecological and socioeconomical spheres. It is a global concern. The occurrence of drought is triggered by climatic phenomena which cannot be eliminated. However, its effect can be well managed if actual spatio-temporal information related to crop status influenced by drought is available to decision-makers. This study attempted to assess the efficiency of remote sensing products from space sensors for monitoring the spatio-temporal status of meteorological drought in conjunction with impact on vegetation condition and crop yield. Time series (2000–2019) datasets of the Tropical Rainfall Measuring Mission (TRMM) were used to compute Standardized Precipitation Index (SPI) and MODIS (MODerate resolution Imaging Spectroradiometer) was used to compute Vegetation Condition Index (VCI). Association between SPI and VCI was explored. YAI was calculated from the statistical data records. Final observations are that the agricultural crop yield changed as per the climate variability specific to location. The study indicates drought indices derived from remote sensing give a synoptic view because of the course resolution of the satellite images. It does not reveal the precise relationship to the small-scale crop yield. Remote sensing can be an effective way to monitor and understand the dynamics of the drought and agriculture pattern over any region.

scholarly journals FASAL concept in meeting the requirements of assessment and forecasting crop production affected by extreme weather events

MAUSAM ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 93-104
Author(s):  
JAI SINGH PARIHAR
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Remote Sensing Data ◽  
Extreme Weather ◽  
Crop Damage ◽  
Extreme Weather Events ◽  
Production Forecast ◽  
Sensing Data ◽  
Production Forecasting ◽  
Weather Events

The research in remote sensing application in India started first in agriculture way back in 1969. With the improvement in satellite sensors, data processing algorithms, models and computational power over time, this research culminated into development of operational projects of CAPE and FASAL, tackling an important issue of operationally providing pre-harvest crop production forecast to stakeholders. This review paper details the sequential developments in the use of remote sensing data for crop production forecasting. The scientific developments in the use of single and multi-temporal optical and microwave satellite images for crop identification and yield estimation in India have been reviewed.  The case studies on use of remote sensing data for crop assessment under extreme weather events are also presented. These include the assessment of crop damage due to extreme weather events of floods, drought, and hailstorm. Examples on use of remote sensing for crop damage assessment due to pest and diseases and forecasting their incidence using satellite derived weather parameters are reviewed.

scholarly journals Economic questions of precision maize production on chernozem soil

2012 ◽  
pp. 293-296
Author(s):  
Dénes Sulyok
Keyword(s):  
Information Technology ◽  
Energy Demand ◽  
Crop Production ◽  
Crop Protection ◽  
Modern Technology ◽  
High Plasticity ◽  
Chernozem Soil ◽  
Maize Production ◽  
Technology System ◽  
Production Site

It is one of the main topical objective to establish the conditions of sustainable farming. The sustainable development in crop production also calls for the harmony of satisfying human needs and providing the protection of environmental and natural resources; therefore, the maximum consideratio of production site endowments, the common implementation of production needs and environmental protection aims, the minimum load on the environment and economicalness. Precision farmin encompasses the farming method which is adjusted to the given production site, the changing  technology in a given plot, the integrated crop protection, cutting edge technologies, remote sensing, GIS, geostatistics, the changeof the mechanisation of crop production, and the application of information technology novelties in crop production. Modern technology increases efficiency and reduces costs. The efficiency of crop production increases by reducing losses and the farmer has access to a better decision support information technology system. In addition, we consider it necessary to examine the two currently most important economic issues: “is it worth it?” and “how much does it cost?”. During the analysis of agricultural technologies, we used the precision crop production experiment database of KITE Zrt. and the Institute for Land Utilisation, Regional Development and Technology of the Centre for Agricultural and Applied Economic Sciences of the University of Debrecen.During our analytical work, we examined three technological alternatives on two soil types (chernozem and meadow). The first technology is the currently used autumn ploughing cultivation. We extended our analyses to the economic evaluation of satellite navigationassisted ploughing and strip till systems which prefer moisture saving. On chernozem soil, of the satellite-based technological alternatives, the autumn ploughing cultivation provided higher income than strip till. In years with average precipitation supply, we recommend the precision autumn ploughing technological alternative on chernozem soils in the future. On meadow soil, the strip till cultivation technology has more favourable economical results than the autumn ploughing. On soils with high plasticity – considering the high time and energy demand of cultivation and the short amoung of time available for cultivation – we recommend to use strip till technologies. 

scholarly journals The role of agrarian science in transforming methods of using the Earth remote sensing data into publicly available technology

2021 ◽  
Author(s):  
V.I. Medennikov ◽  
Yu.A. Flerov
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Crop Production ◽  
Remote Sensing Data ◽  
Earth Remote Sensing ◽  
Conceptual Information ◽  
Sensing Data ◽  
The Earth ◽  
Available Technology

Improvement of the Earth remote sensing technology has led to an active implementation of its results in many areas of human activity with a significant expansion of both the number of industries using remote sensing data and the range of problems to be solved. Agriculture is perhaps the only industry where there is a symbiosis of this data obtained from both spacecraft, unmanned aerial vehicles, and ground vehicles with a significant intersection of information used in many sectors of economy, such as cartography, ecology, land management, logistics, construction, weather and climate, etc. Such an integrated use of heterogeneous information from various sources requires developing a digital decryption tool (standard) in the form of a unified geographic information system and a unified conceptual information model of crop production. From such a geographic information system, users could obtain unified digitized images, which would be ready for use and entering into their databases, whereas a unified conceptual information model of crop production, integrating all the knowledge of this industry, should turn into a kind of a publicly available technology. On the other hand, digitalization of the economy has significantly expanded the range of problems to be solved not only in production, but also in science, allowing for purely theoretical scientific research to actively penetrate into production. This also requires appropriate digital standards and managerial structures.

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