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.

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 Früherkennung von Buchdruckerbefall dank Fern erkundung: Was ist schon möglich?

2020 ◽  
Vol 171 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Luzia Götz ◽  
Achilleas Psomas ◽  
Harald Bugmann
Keyword(s):  
Remote Sensing ◽  
Early Detection ◽  
Bark Beetle ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Hyperspectral Data ◽  
Time Saving ◽  
Federal Institute ◽  
Institute Of Technology

Early detection of bark beetle infestations by remote sensing: what is feasible today? Infestation by the Norway spruce (Picea abies) bark beetle (Ips typographus) in uniform forest stands of the high montane and subalpine stage is a major challenge for management. It is impossible to identify in time all susceptible or already infested spruces in the often steep terrain solely by terrestrial observations and to prevent the proliferation of the beetle. A time-saving, cost-effective and effective method for finding these spruces is necessary and remote sensing techniques appear promising. Therefore, we investigated the potential of hyperspectral remote sensing data for the early detection of stressed or infested spruces using a case study in the experimental forest of the Swiss Federal Institute of Technology Zurich (ETHZ) in Sedrun. The approach that we developed is based on a combination of field surveys, hyperspectral data, vegetation indices calculated from these and their classification into the three classes “dead”, “stressed” and “healthy” using Random Forests, a machine-learning approach. We demonstrate that stressed spruces can be identified with this approach, but it is not yet ready for operational use. In particular, a slope-specific calibration of the method is necessary, which makes practical application impossible.

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 Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods?

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 378
Author(s):  
Shayne Magstadt ◽  
David Gwenzi ◽  
Buddhika Madurapperuma
Keyword(s):  
Remote Sensing ◽  
Early Detection ◽  
Spatial Patterns ◽  
Black Bear ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Electromagnetic Spectrum ◽  
Bark Stripping ◽  
Monitoring Method ◽  
Individual Tree

The prevalence of black bear (Ursus americanus) bark stripping in commercial redwood (Sequoia sempervirens (D. Don) Endl.) timber stands has been increasing in recent years. This stripping is a threat to commercial timber production because of the deleterious effects on redwood tree fitness. This study sought to unveil a remote sensing method to detect these damaged trees early and map their spatial patterns. By developing a timely monitoring method, forest timber companies can manipulate their timber harvesting routines to adapt to the consequences of the problem. We explored the utility of high spatial resolution UAV-collected hyperspectral imagery as a means for early detection of individual trees stripped by black bears. A hyperspectral sensor was used to capture ultra-high spatial and spectral information pertaining to redwood trees with no damage, those that have been recently attacked by bears, and those with old bear damage. This spectral information was assessed using the Jeffries-Matusita (JM) distance to determine regions along the electromagnetic spectrum that are useful for discerning these three-health classes. While we were able to distinguish healthy trees from trees with old damage, we were unable to distinguish healthy trees from recently damaged trees due to the inherent characteristics of redwood tree growth and the subtle spectral changes within individual tree crowns for the time period assessed. The results, however, showed that with further assessment, a time window may be identified that informs damage before trees completely lose value.

scholarly journals Detection of Rise Damage by Leaf Folder (Cnaphalocrocis medinalis) Using Unmanned Aerial Vehicle Based Hyperspectral Data

2020 ◽  
Vol 12 (22) ◽  
pp. 9343
Author(s):  
Tao Liu ◽  
Tiezhu Shi ◽  
Huan Zhang ◽  
Chao Wu
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Spectral Index ◽  
Crop Production ◽  
Leaf Roll ◽  
Hyperspectral Data ◽  
Cnaphalocrocis Medinalis ◽  
Crop Pests ◽  
Pests And Diseases ◽  
Aerial Vehicle

Crop pests and diseases are key factors that damage crop production and threaten food security. Remote sensing techniques may provide an objective and effective alternative for automatic detection of crop pests and diseases. However, ground-based spectroscopic or imaging sensors may be limited in practically guiding the precision application and reduction of pesticide. Therefore, this study developed an unmanned aerial vehicle (UAV)-based remote sensing system to detect leaf folder (Cnaphalocrocis medinalis). Rice canopy reflectance spectra were obtained in the booting growth stage by using the UAV-based hyperspectral remote sensor. Newly developed and published multivariate spectral indices were initially calculated to estimate leaf-roll rates. The newly developed two-band spectral index (R490−R470), three-band spectral index (R400−R470)/(R400−R490), and published spectral index photochemical reflectance index (R550−R531)/(R550+R531) showed good applicability for estimating leaf-roll rates. The newly developed UAV-based micro hyperspectral system had potential in detecting rice stress induced by leaf folder. The newly developed spectral index (R490−R470) and (R400−R470)/(R400−R490) might be recommended as an indicator for estimating leaf-roll rates in the study area, and (R550−R531)/(R550+R531) might serve as a universal spectral index for monitoring leaf folder.

scholarly journals Evaluation of Deep Learning Convolutional Neural Network for Crop Classification

2019 ◽  
Vol 8 (2) ◽  
pp. 3960-3963
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Crop Production ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Random Forest Classification

In this paper, we have done exploratory experiments using deep learning convolutional neural network framework to classify crops into cotton, sugarcane and mulberry. In this contribution we have used Earth Observing-1 hyperion hyperspectral remote sensing data as the input. Structured data has been extracted from hyperspectral data using a remote sensing tool. An analytical assessment shows that convolutional neural network (CNN) gives more accuracy over classical support vector machine (SVM) and random forest methods. It has been observed that accuracy of SVM is 75 %, accuracy of random forest classification is 78 % and accuracy of CNN using Adam optimizer is 99.3 % and loss is 2.74 %. CNN using RMSProp also gives the same accuracy 99.3 % and the loss is 4.43 %. This identified crop information will be used for finding crop production and for deciding market strategies

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 Early Detection of Fungal Diseases in Winter Wheat by Multi-optical Sensors

2014 ◽  
Vol 8 ◽  
pp. 199-203 ◽  
Author(s):  
Yuxuan Wang ◽  
Shamaila Zia ◽  
Sebastian Owusu-Adu ◽  
Roland Gerhards ◽  
Joachim Müller
Keyword(s):  
Early Detection ◽  
Winter Wheat ◽  
Optical Sensors ◽  
Fungal Diseases

scholarly journals Feed Composition Differences Resulting from Organic and Conventional Farming Practices Affect Physiological Parameters in Wistar Rats—Results from a Factorial, Two-Generation Dietary Intervention Trial

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 377
Author(s):  
Marcin Barański ◽  
Dominika Średnicka-Tober ◽  
Leonidas Rempelos ◽  
Gultakin Hasanaliyeva ◽  
Joanna Gromadzka-Ostrowska ◽  
...  
Keyword(s):  
Crop Production ◽  
Wistar Rats ◽  
Dietary Intervention ◽  
Growth Parameters ◽  
Crop Protection ◽  
The Body ◽  
Interactive Effects ◽  
Supporting Evidence ◽  
Feed Composition ◽  
Npk Fertilizer

Recent human cohort studies reported positive associations between organic food consumption and a lower incidence of obesity, cancer, and several other diseases. However, there are very few animal and human dietary intervention studies that provide supporting evidence or a mechanistic understanding of these associations. Here we report results from a two-generation, dietary intervention study with male Wistar rats to identify the effects of feeds made from organic and conventional crops on growth, hormonal, and immune system parameters that are known to affect the risk of a number of chronic, non-communicable diseases in animals and humans. A 2 × 2 factorial design was used to separate the effects of contrasting crop protection methods (use or non-use of synthetic chemical pesticides) and fertilizers (mineral nitrogen, phosphorus and potassium (NPK) fertilizers vs. manure use) applied in conventional and organic crop production. Conventional, pesticide-based crop protection resulted in significantly lower fiber, polyphenol, flavonoid, and lutein, but higher lipid, aldicarb, and diquat concentrations in animal feeds. Conventional, mineral NPK-based fertilization resulted in significantly lower polyphenol, but higher cadmium and protein concentrations in feeds. Feed composition differences resulting from the use of pesticides and/or mineral NPK-fertilizer had a significant effect on feed intake, weight gain, plasma hormone, and immunoglobulin concentrations, and lymphocyte proliferation in both generations of rats and in the second generation also on the body weight at weaning. Results suggest that relatively small changes in dietary intakes of (a) protein, lipids, and fiber, (b) toxic and/or endocrine-disrupting pesticides and metals, and (c) polyphenols and other antioxidants (resulting from pesticide and/or mineral NPK-fertilizer use) had complex and often interactive effects on endocrine, immune systems and growth parameters in rats. However, the physiological responses to contrasting feed composition/intake profiles differed substantially between the first and second generations of rats. This may indicate epigenetic programming and/or the generation of “adaptive” phenotypes and should be investigated further.

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