Top PDF Crop Disease and Pest Monitoring by Remote Sensing

Crop Disease and Pest Monitoring by Remote Sensing

Crop Disease and Pest Monitoring by Remote Sensing

determining the disease severity. This might be because it is more rough estimation than DI (%). It is understandable that for the same sample, the less precise the criterion, the greater accuracy it would achieve. Moreover, the 5-class disease severity quantification is enough to guide field applications. We suggest that DI (%) should be used for detecting the disease severity of yellow rust by SKB. For the distance criteria used in the process of matching with SKB, the Mah distance criterion might be more appropriate because it performed better than SA in all the analyses conducted in this study (Figs. 11, 12, Tables 6, 7). Some previous studies have already emphasized the potential of hyperspectral imagery (Bravo et al. 2003; Moshou et al. 2004; Huang et al. 2007) and the high-resolution of multispectral imagery (Franke and Menz 2007) for detecting yellow rust disease. The development of SKB in the present study can be viewed as a scaling up method, which has extended the capability of detecting yellow rust disease from hyper- spectral imagery to the moderate resolution of multispectral imagery. However, it should be noted that the task of monitoring the occurrence and degrees of infection of crop diseases is far more complex than the cases described in this study. The spectral characteristics of yellow rust infection might appear similar to other sources of stress. In addition, the impact of phenology, cultivation methods, fragmentation of farmlands and other environmental conditions would also increase the difficulty and uncertainty of the estimation process. Therefore, the SKB developed in this study should correspond to the situation at the anthesis stage exclusively, and is only suitable for those regions with similar environmental characteristics and cultivation methods. For other regions with significantly different environmental characteristics, this purposed SKB may not work well. The possible solution to these problems may include incorporating suitable priors, which would require integration strategies and understanding of the mechanisms underlying some fundamental processes. Further research is required to address the problems mentioned above.
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Contribution of Remote Sensing on Crop Models: A Review

Contribution of Remote Sensing on Crop Models: A Review

A recent trend in satellite remote sensing are the constellations of nanosatellites (mass <10 kg). These are designed as low-cost orbiting satellites with simple monitoring and communication instruments that can be launched in dozens, each following a consecutive orbit from the previous in order to provide frequent coverage at very high spatial resolution. Their low construction and launch cost promises easy replacement and long-term maintenance of the constellation. An operational example is the constellation of Planet Lab’s ‘Doves’, which are designed to operate in tandem and cover the globe daily at 3–5 m spatial resolution [117]. A different trend is the development of High Altitude Pseudo Satellites (HAPS) for applications in environmental monitoring and other non-defence uses. HAPS are remotely operated aircrafts positioned above 20 km altitude in the stratosphere, for very long duration flights and have the potential to stay over a fixed point on Earth from weeks to months. However, it yet to be proven that these systems could provide an operational and cost-effective source of information, considering the temporal requirements of crop models to cover at least a cropping season.
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Crop Detection and Classification using Remote Sensing Images

Crop Detection and Classification using Remote Sensing Images

Carlos A. Devia et al [50] bestowed a high-throughput technique for AGBE (Above ground estimation of biomass) in rice utilizing multispectral NIR (near-infrared) imagery clicked at disparate scales of the crop. By creating an integrated aerial crop monitoring solution (IACMS) utilizing a UAV (Unmanned Aerial Vehicle), this method computes 7 VI that were combined as multi-variable regressions relying on the rice growth phases: vegetative, ripening or reproductive. By utilizing a minimum sampling area of 1 linear meter of the crop, this concept was measured. Under the lowland and upland production system, a comprehensive experimental test has been carried out over 2 different rice varieties. The output showed that this approach was able to estimate the biomass of large areas of the crop with an average correlation of 0.76 contrasted to the conventional manual destructive method.
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Remote Sensing for Site-Specific Crop Management: Evaluating the Potential of Digital Multi-Spectral Imagery for Monitoring Crop Variability and Weeds within Paddocks

Remote Sensing for Site-Specific Crop Management: Evaluating the Potential of Digital Multi-Spectral Imagery for Monitoring Crop Variability and Weeds within Paddocks

Remote sensing applications for distinguishing between agricultural crop types and internal crop characteristics have been extensively researched during the past decade (Wiegand et al., 1991; Cloutis, et al., 1996; Thenkabail et al., 2000; Metternicht et al., 2000). The trends being developed between specific crop types, maturity, nutrient levels and their reflectance values in spectral bands and relationship to vegetation indices (VI), are becoming well known and useful when limited ground truth data is available (Senay et al., 2000), or when extensive areas need to be mapped in a short time span.
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Use of remote sensing data for broad acre grain crop monitoring in Southeast Australia

Use of remote sensing data for broad acre grain crop monitoring in Southeast Australia

Seasonal effects are a significant challenge when transferring crop discrimination models without prior knowledge from one year to another. It is therefore necessary to build a database that includes many crop seasons to determine a “typical” crop season in south east Australia as a base line to adjust seasons to each other. Aigner (1999), and Aigner et al. (1999) reported building such a crop database with NOAA-AVHRR data (1995-1998) for the Gooroc test site when relating the satellite data to grain crop yields. In his study Aigner observed that the temporal behaviour of the NDVI varied with respect to season onset date and plateau duration. Li and Kafatos (2000) found the biosphere vegetation patterns in AHHRR data in the USA to be related to the El Niño/ La Niña effect. Reed et al (1994) related vegetation phenology to quantified AVHRR NDVI curve properties in the USA and Hill and Donald (2003) used such NDVI metrics in Western Australia to derive information about seasonal agricultural productivity. A regional multi-seasonal database utilizing NDVI metrics of remote sensing data with high temporal resolution (AVHRR or MODIS) together with climatic records needs to be built in future research to use seasonal information for the crop monitoring system in south east Australia.
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Remote sensing time series analysis for crop monitoring with the SPIRITS software: new functionalities and use examples

Remote sensing time series analysis for crop monitoring with the SPIRITS software: new functionalities and use examples

3 Vlaamse Instelling voor Technologisch Onderzoek, Mol, Belgium Monitoring crop and natural vegetation conditions is highly relevant, particularly in the food insecure areas of the world. Data from remote sensing image time series at high temporal and medium to low spatial resolution can assist this monitoring as they provide key information about vegetation status in near real-time over large areas. The Software for the Processing and Interpretation of Remotely sensed Image Time Series (SPIRITS) is a stand-alone flexible analysis environment created to facilitate the processing and analysis of large image time series and ultimately for providing clear information about vegetation status in various graphical formats to crop production analysts and decision makers. In this paper we present the latest functional developments of SPIRITS and we illustrate recent applications. The main new developments include: HDF5 importer, Image re-projection , additional options for temporal Smoothing and Periodicity conversion, computation of a rainfall-based probability index (Standardized Precipitation Index) for drought detection and extension of the Graph composer functionalities. The examples of operational analyses are taken from several recent agriculture and food security monitoring reports and bulletins. We conclude with considerations on future SPIRITS developments also in view of the data processing requirements imposed by the coming generation of remote sensing products at high spatial and temporal resolution, such as those provided by the Sentinel sensors of the European Copernicus programme.
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Crop Drought Stress Monitoring by Remote Sensing (DROSMON) Overview. Werner Schneider

Crop Drought Stress Monitoring by Remote Sensing (DROSMON) Overview. Werner Schneider

3 Institut für Vermessung, Fernerkundung und Landinformation Werner Schneider Aim and Focus of Project z overall goal: adaptation and advancement of remote sensing based methods of drought stress detection and monitoring on agricultural crops exploiting the potentials of present-day satellite-based optical sensors

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OPERATIONAL REMOTE SENSING PROGRAMS IN HUNGARY: FROM CROP MONITORING TO DISASTER MANAGEMENT

OPERATIONAL REMOTE SENSING PROGRAMS IN HUNGARY: FROM CROP MONITORING TO DISASTER MANAGEMENT

The Ragweed Control Program has been operational since 2005 to date, with similar overall technological and administrative background. The implementation of components and the extent of survey have been changed throughout the past years to satisfy the needs arose. Based on the results we can conclude that the immense problems of ragweed and pollen allergy in Hungary could not be efficiently controlled without space technologies. The introduction of four high tech components (RS+GPS+GIS+web) was inevitable to basically improve the traditional ground-based ragweed control system in Hungary. The remote sensing assessment covers the whole arable land and helps in the optimization of in situ measurements and their documentation. The time requirement of ground based components was dramatically decreased by a productive geo-informational provision (remote sensing), surrounded by GPS and GIS techniques, the data exchange and the new legal provisions of the Plant Protection Law. The administrative tasks were also made much more effective.
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Smart electrostatic crop spraying using remote sensing technology

Smart electrostatic crop spraying using remote sensing technology

ABSTRACT For this thesis, smart spraying robot was designed, constructed and tested to validate the concept of smart pest control. Electrostatic charging of sprayed pesticide was realized in a spray nozzle design that improved plant coverage and reduced wasted pesticide as well as soil pollution. A thorough investigation into electrostatic spraying was conducted, which was accompanied by extensive simulations and experimentation. The results obtained from the simulation experimentation on industry standard electrostatic spray system (ESS) nozzles along with laboratory testing of these nozzles, detecting spray coverage using water sensitive paper and additional optical spray visualization methods gave the necessary insight and experience required to develop a new spray nozzle. Additional COMSOL simulation and experimentation were carried out on a Fan Hydraulic Spray Nozzle (FHSN), the results of which allowed for the effective addition of electrostatic induction capabilities, thereby transforming the (FHSN) into Electrostatic Induction Spray Nozzle (EISN) which is one of the prime parts of the smart spraying system. SOLIDWORKS software was used in the designing parts of this nozzle which were then manufactured using a 3D printer. An AL05D robotic manipulator and a TTRK tracked platform from Lynxmotion ™ were the mini mobile robot components selected for the feasibility study of the smart electrostatic crop spraying system. This mobile robot was equipped with a CCD digital camera, a range detector, and path mark detector to provide the necessary sensors required by the smart electrostatic spray system. A Windows™ based mobile computer in addition to an ARDUINO™ based orksmicrocontroller system were chosen to provide the computational power required by the system. These were arranged in a master – slave configuration, with the main processing for images and motion being conducted inside the master computer using programs created by
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ZigBee Technology for Remote monitoring of Crop Field in WSN

ZigBee Technology for Remote monitoring of Crop Field in WSN

Today whole over the world, agriculture is one which has required different number of tools and technologies to improve the quality and efficiency of the production, which in turn to reduce environmental impact on crop. Wireless Sensors Network (WSN) is one of them shows a wide range of applications related to different sectors, as it combines computation, sensing and also one of the vast one i.e. communication. The WSN is the one brings out more contribution towards the precise agriculture. Precise agriculture clears that at the right time at right location the right input is provided to improve quality and enhance the production by protecting environmental conditions [1].
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Using GIS and remote sensing for ecological mapping and monitoring

Using GIS and remote sensing for ecological mapping and monitoring

The latter scenario is of particular interest to researchers involved in large- area habitat monitoring programmes, where many different types of land cover changes can occur and must be accounted for, e.g. pest infestation, logging and wildfire (Rogan and Miller, 2006). While the most common method of habitat monitoring requires the categorical comparison of independently classified maps, this approach has several drawbacks: (a) high cost (and time consumption) of mapping and re-mapping; (b) inability to detect subtle land cover modifications; and (c) categorical and positional errors in both land cover maps are compounded when compared. The production of maps depicting change can facilitate an improved understanding of both the agents of change and the biophysical linkages between surface reflectance and the change agents, e.g. NDVI can be directly linked to multitemporal changes in green vegetation cover (Lunetta et al., 2002). Rogan
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Crop Water Requirements in Egypt Using Remote Sensing Techniques

Crop Water Requirements in Egypt Using Remote Sensing Techniques

The common Soil in Egypt is clay soil so common irrigation system is tradition surface irrigation with 60% irrigation efficiency. Agricultural sector consumes more than 80% of water resources under surface irrigation (tradition methods). In arid and semi-arid regions consumptive use is the best index for irrigation requirements. A large part of the irrigation water applied to farm land is consumed by Evapotranspiration (ET). Irrigation water consumption under each of the physical and climatic conditions for large scale will be easier with remote sensing techniques. In Egypt, Agricultural cycle is often tow agricultural seasons yearly; summer and winter. Common summer crops are Maize, Rice and Cotton while common winter crops are Clover and Wheat. Landsat8 bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate Normalized Deference Vegetation Index (NDVI) and monitoring cultivated areas. The cultivated land area was 3,277,311 ha in August 2013. In this paper K c = 2 * NDVI − 0.2 represents the rela-
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Citizen science and remote sensing for crop yield gap analysis

Citizen science and remote sensing for crop yield gap analysis

This study aimed to use the synergy of remote sensing and crowdsourcing to estimate and explain yields. Sesame production on medium and large farms in Ethiopia was used as a case study. Firstly, the potential of vegetation indices based on remote sensing images for predicting sesame yield at the field level was explored. A total of 14 Landsat-8 images, representing different growth stages, were used to derive vegetation indices (VIs). Secondly, crowdsourced data on crop phenology was used to improve the prediction of yields based on VIs. Thirdly, farmer reported data using the crowdsourcing approach was compared to the predicted yield from the VIs, in order to explain yield variability among and within fields. Results of the study showed that there is a correlation between sesame actual yield as reported by farmers and predicted yield based on VIs. The highest correlation was observed between sesame actual yield and the predicted yield using average NDVI over the growing season (R 2 = 0.65, RMSE = 0.62). Crowdsourced information related to crop phenology per field used to adjust the VIs, could further improve the performance of the model to predict sesame yield. As yield estimation based on measurements or surveys are not always reliable, the increasing spatial resolution of remote sensing images thus provides increased potential for yield estimation. Crowdsourced information could further identify factors that caused the yield variability within a field, and locations with lower yields in the remote sensing images largely overlapped with locations with reported yield limiting and/or reducing factors. According to the perception of farmers, overall soil fertility is the most important factor explaining the yield variability within a field, followed by high presence of weeds. Identifying the variation within a field based on different types of information will assist the farmers in managing their agricultural practices. While the analysis described in this chapter focused on sesame yield in Ethiopia, the approach of coupling remote sensing with crowdsourcing has the potential to support yield monitoring and forecasting efforts in the other parts of the world.
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Monitoring agricultural plantations with remote sensing imagery

Monitoring agricultural plantations with remote sensing imagery

To study terrestrial phenomena are chosen images under favourable weather conditions without clouds. The visual interception for assessing crops status is more difficult than intercepting the visual image of the crops type. It is also difficult to identify the different effects produced by disease, insects’ attacks, and nutrient deficiency because of the variety of plants, plant maturity, the rate of planting soil or various colors. Some problems of interpretation may arise after dry periods, so interpretation must be done on images that are acquired in a short time after rain.
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Remote Sensing based Crop Yield Monitoring and Forecasting

Remote Sensing based Crop Yield Monitoring and Forecasting

information such as start of growing season and leaf growth rate. Results from pilot study sites in South and South East Asian countries suggest that incorporation of remote sensing data (SAR) into process-based crop model improves yield estimation for actual yields and thus offering potential application of such system in a crop insurance program. Remote- sensing data assimilation into crop model effectively capture responses of rice crops to environmental conditions over large spatial coverage, otherwise practically impossible to achieve with crop modeling approach alone. This study demonstrates the two angles of uncertainties reduction in forecasting crop yield: (1) minimizing model uncertainties, in this case by assimilation of remote-sensing data into crop model to recalibrate model parameters based on remotely sensed crop status on the ground, and (2) minimizing uncertainties in seasonal weather conditions by incorporating real-time throughout the forecasting dates. Key Terminology: Crop Yield Monitoring, Crop Yield Forecast, Remote Sensing, Synthetic Aperture Radar (SAR), Crop Growth Modeling, ORYZA2000
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SOME RESULTS OF CROP STRESS MONITORING BY REMOTE SENSING IN NORTHERN MONGOLIA

SOME RESULTS OF CROP STRESS MONITORING BY REMOTE SENSING IN NORTHERN MONGOLIA

climatically suited for agriculture . Global warming is causing decrease in precipitation and an increase in the number of warm days but does not change in crop yields in the irrigated crop land. As far as Mongolia is concerned, many studies have shown that it is desirable to consider changes in vegetative chlorophyll as a monitoring of satellite imagery to monitor the situation of crop land and crop stress. Site Description Data for this study was collected on or near the northern border of Mongolia, in the Selenge image of the central cropping region, Mongolia, which is located in 71°17’01” N, 156°35’48” W, at an elevation of 890-1120 m above sea level. The central cropping region is included in crop land of Selenge, Tuv and Bulgan images. The
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Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

35 integrating SAR into growth modelling, there also have been promising results by using repeat-pass SAR interferometric coherence with one day offset for vegetation biomass estimation (Blaes & Defourny 2003). Reasonable results have been already achieved using Polarimetric SAR Interferometry (POLInSAR) for rice biophysical parameter retrieval with indoor wide-band polarimetric measurements (Ballester-Berman et al. 2005). In the near future, Polarimetric SAR Interferometry for crop monitoring with single pass will be demonstrated by the TanDEM-X mission (Hajnsek et al. 2010). The scattering process and penetration depth into the canopy is highly dependent on the wavelength and the incidence angle (Lim et al. 2007). Inoue et al. (2002) identified typical multi-temporal backscatter signatures of rice for frequencies at around 35, 15, 10, 5 and 1 GHz and at different incidence angles. In terms of electromagnetic interaction between microwaves and canopy, the received radar backscatter is a sum of three main components, including volume scattering, the double bounce scattering from the vegetation–surface interaction and the contribution from the surface itself. At the X-band, experiments conducted by Kim et al. (2000) using ground-mounted scatterometer data have demonstrated that the co-polarised backscatter from a paddy rice field at the beginning of the growing season is dominated by double bounce scattering from the stem–surface (water) interaction. With increasing plant density, the double bounce scattering is replaced by a random scattering from the upper canopy. Inoue et al. (2002) mentioned a typical dual-peak trend for higher frequencies; the first peak at the maximum of double bounce scattering and the second peak with appearance of the top leaf and the heads in top layer of the canopy.
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Remote Sensing for Crop Management

Remote Sensing for Crop Management

Integrating Remote Sensing with Crop Simulation Models Although capabilities to simulate crop growth and develop- ment have increased considerably over the past decades, predicting the effects of management factors, unusual or ex- treme weather events, and pest pressures on crop water and nutrient requirements and final harvestable yields is still far from being an exact science. Remotely sensed imagery is a practical method for providing crop simulation models with canopy state variables which change dynamically in time and space (Wiegand et al., 1979). At the same time, crop models can increase the information that can be derived from remotely sensed images by extrapolating for periods when inclement weather precludes data collection and by providing the ability to predict crop and yield response to changes in management strategies. Various approaches to integrating remotely sensed data into crop models have been the subject of a review on the topic by Moulin et al. (1998). While the objective of these integrated approaches often has been to monitor crop condition and yield at regional scales (e.g., Doraiswamy and Cook, 1995) and at the state and county levels (Doraiswamy et al., 2003; p. 665 this issue), recent efforts have also focused on predicting within-field variability in crop status (Sadler et al., 2002). Coupling the remotely sensed imagery with the models can be done directly through biomass, GLAI, and phenological stages, or indirectly by inferring f APAR , plant water status, nutrient status, disease, insect, or weed pressure. Examples include
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Remote sensing of biotic stress in crop plants and its applications for pest management

Remote sensing of biotic stress in crop plants and its applications for pest management

Devadas et al. ( 2008 ) tested different narrow band indices to discriminate three rust diseases on wheat and found no single index was capable of discriminating all three rust species on, but sequential application of selected indices would provide for the required species discrimination under laboratory conditions and thus, could form the basis for discrimination of rust species in wheat under fi eld conditions. Genc et al. ( 2008 ) tested different hyperspectral indices for detection of sunnpest ( Eurygaster integriceps ) on wheat and found NDVI and SIPI as more suitable for assessing their damage levels. Ray et al. ( 2010 ) demonstrated the use of hyperspectral indices based on narrow bands to differentiate healthy and blight infested potato plants. Ultraviolet, visible, and near infrared refl ectance spectroscopy was used to determine the disease severity of tomato leaves infected with bacterial leaf spot, Xanthomonas perforans and identifi ed wavelengths around 750–760 nm as signifi cant and seem highly related to the disease (Jones et al. 2010 ) . While Liu et al. ( 2008 ) estimated brown spot fungal disease of rice using hyperspectral refl ectance data and identifi ed sensitive bands specifi c to this disease. Jusoff et al. ( 2010 ) developed a signature library profi le of leaf fall disease affecting rubber trees and opined that such studies certainly assists in the development of an early disease warning system using an airborne hyperspectral
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Monitoring crop evapotranspiration and crop coefficients over an almond and pistachio orchard throughout remote sensing

Monitoring crop evapotranspiration and crop coefficients over an almond and pistachio orchard throughout remote sensing

Remote sensing data has been widely used in the assessment of crop ET and crop water stress to obtain spatial information. Reflectance-derived vegetation indices (VI) such as the normalized difference vegetation index (NDVI), have been empirically regressed with basal crop coefficients (K cb ) and applied to a reference ET (ET o ) to estimate crop water use, such as with FAO56 [ 11 , 12 ]. Although simple and widely-applied, this method may be too simplistic for crops undergoing deficit-irrigation scheduling because it only accounts for the potential evapotranspiration that a crop would have with no modification in stomatal conductance or crop coefficient (K c ) due to water stress. In addition, these techniques assume that variations in potential crop evapotranspiration (ET c ) are linearly related to canopy size, and are not sensitive to crop phenology and canopy architecture [ 13 ]. Other techniques are based on combining both thermal and optical data to directly obtain actual crop evapotranspiration (ET a ) through energy balance models [ 14 – 16 ]. Although these models are able to estimate ET a with fine accuracy, they have limitations because they require the retrieval of a large number of physical parameters and modeling options. In addition, some of these models, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC) [ 17 ] or the simplified surface energy balance (SEBAL) [ 18 ] model are sensitive to the definition of hot (zero transpiration) and cold (potential transpiration) pixels, which can lead to user subjectivity in defining those anchor pixels.
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