- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fusion of Sentinel-1 and Sentinel-2 Images for Classification of Agricultural Areas Using a Novel Classification Approach
摘要： A continuously growing world population increases steadily the demand of foods. This results in strong changes that occur on agricultural sites. Remote sensing data provides an excellent opportunity to monitor these changes which is a crucial base to assess the impact of these changes on the climate or the natural resources. In the presented study, we tested the performance of a new crop classification method for a stack of Sentinel-1 (S1) and Sentinel-2 (S2) images taken within one growing season. We proved, that the new PSP method performs better for S1 images revealing an overall accuracy (OA) of 75% compared to 60% for the Random Forest classifier (RF). The PSP method outperformed also for the fused dataset of S1 and S2 images (72% OA for PSP, 62% for RF). The results illustrate the benefits for crop classifications provided by PSP and give crucial insights for the advantages and limits of S1 and S2 data fusion.
关键词： Classification,Fusion,Agriculture,Sentinel-1,Sentinel-2
更新于2025-09-23 15:23:52
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Landsat-8 and Worldview-3 Data for Assessing Crop Residue Cover
摘要： Crop residues on the soil surface provide defense against erosive forces of water and wind. Quantifying crop residue cover is crucial for monitoring extent of conservation tillage practices. Current multispectral satellite sensors either lack appropriate spectral bands to reliably distinguish crop residue from soil or cannot provide global coverage. Our objective was to estimate crop residue cover in corn and soybean fields in central Iowa by combining data from two multispectral satellites - Landsat-8 and WorldView-3. Shortly after planting in 2016, we measured crop residue cover in >45 fields using the line-point transect method. Landsat Normalized Difference Tillage Index (NDTI) required local calibrations to account for variations in soils, crops, and moisture conditions. In contrast, WorldView-3 Shortwave Infrared Normalized Difference Residue Index (SINDRI) reliably estimated crop residue cover with minimal ground truth data. Although WorldView-3 images cannot provide global coverage, they can augment and extend ground truth observations for calibrating Landsat indices.
关键词： Soils,Crops,Conservation tillage,Non-photosynthetic vegetation,Agriculture
更新于2025-09-23 15:23:52
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Operational Agricultural Flood Monitoring with Sentinel-1 Synthetic Aperture Radar
摘要： Agricultural flood monitoring is important for food security and economic stability. Synthetic Aperture Radar (SAR) has the advantage over optical data by operating at wavelengths not impeded by cloud cover or a lack of illumination. This characteristic makes SAR a potential alternative to optical sensors for agricultural flood monitoring during disasters. The purpose of this study is to assess the effectiveness of using freely available Copernicus Sentinel-1 SAR data for operational agricultural flood monitoring in the United States (U.S.). The operational detection of flood inundation was tested during Hurricane Harvey in 2017, which resulted in significant flooding over Texas and Louisiana, U.S. This paper presents 1) the agricultural flood monitoring method that utilizes Sentinel-1 SAR, the NASS 2016 Cultivated Layer, and the NASS 2016 and 2017 Cropland Data Layers; 2) flood detection validation results and 3) inundated cropland and pasture acreage estimates. The study shows that Sentinel-1 SAR is an effective and valuable data source for operational disaster assessment of agriculture.
关键词： Hurricane Harvey,Flood Detection,Synthetic Aperture Radar,Agriculture Flood Monitoring,Sentinel-1
更新于2025-09-23 15:22:29
-
Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping
摘要： Phenotyping is the task of measuring plant attributes for analyzing the current state of the plant. In agriculture, phenotyping can be used to make decisions concerning the management of crops, such as the watering policy, or whether to spray for a certain pest. Currently, large scale phenotyping in fields is typically done using manual labor, which is a costly, low throughput process. Researchers often advocate the use of automated systems for phenotyping, relying on the use of sensors for making measurements. The recent rise of low cost, yet reasonably accurate, RGB-D sensors has opened the way for using these sensors in field phenotyping applications. In this paper, we investigate the applicability of four different RGB-D sensors for this task. We conduct an outdoor experiment, measuring plant attribute in various distances and light conditions. Our results show that modern RGB-D sensors, in particular, the Intel D435 sensor, provides a viable tool for close range phenotyping tasks in fields.
关键词： INTEL D-435,RGB-D sensors,sensors in agriculture,INTEL SR300,empirical analysis,Microsoft Kinect,phenotyping,ORBBEC ASTRA S
更新于2025-09-23 15:22:29
-
EDXRF for elemental determination of nanoparticle-related agricultural samples
摘要： The number of studies dealing with nanoparticles (NPs) and plants has increased. They subsidize the advances of agriculture in the 21st century; however, so far, beneficial as well as detrimental results have been reported. In this context, analytical tools for monitoring macronutrients and micronutrients in plants exposed to NPs, with adequate performance and low cost, are required. This work assesses the use of energy-dispersive X-ray fluorescence (EDXRF) spectrometry for elemental content evaluation in NP-containing agricultural samples. For Phaseolus vulgaris (common bean) seedlings treated with ZnO NP, CuO NP, and Fe3O4 NP, the limits of detection (LODs) were 0.4 mg kg?1 for Zn and Cu and 0.6 mg kg?1 for Fe after dry-ashing digestion, thus being suitable for NP oxide monitoring in seed priming. For submicron suspension fertilizers, Mn, Cu, and Zn were quantified as thin films after sample dilution. The LODs for Mn, Cu, and Zn were 0.09, 0.1, and 0.08 mg L?1, respectively. Finally, for P. vulgaris plants exposed to 300-nm ZnO NP, we monitored P, S, K, Ca, and Zn directly in powdered leaves, whose LODs ranged from 1.3 to 27 mg kg?1. No critical spectral interference was observed, and notable repeatability and suitable trueness were found in the cases of studies. EDXRF revealed itself a simple, fast, and reliable alternative to evaluate the elemental content in suspensions or the uptake of NP by plants.
关键词： agriculture,X-ray fluorescence,elemental determination,EDXRF,nanoparticles
更新于2025-09-23 15:22:29
-
[IEEE 2019 IEEE Conference on Information and Communication Technology (CICT) - Allahabad, India (2019.12.6-2019.12.8)] 2019 IEEE Conference on Information and Communication Technology - LEDCOM: A Novel and Efficient LED Based Communication for Precision Agriculture
摘要： Wireless Sensor Networks and Satellite Remote Sensing are some of the existing techniques that are used to collect, analyze and interpret data from the agricultural crop sites. However, there are certain limitations common to both of these techniques that are concerned with the latency and the resolution of the data collected. UAVs (Unmanned Aerial Vehicles) are becoming another alternative that has become integral nowadays due to its affordable and scalable nature while offering user friendly requirements and customizations. This proposes a novel and cost-effective technique (LEDCOM) that harnesses the capabilities of ground sensors and unmanned UAV while using computer vision methods to produce a qualitative data analysis system that describes the crop site under supervision. An UAV is assumed to collect the ground based sensor node data in the form of binary patterns on LED Arrays that is encoded in the image taken by a camera of a drone. Image processing techniques are used to identify and decode the LED sequences from the arrays. The performance of the proposed system is evaluated under different features and image resolutions within the same lighting conditions. A promising performance is observed for LED pattern identi?cation from the challenging images taken from a height.
关键词： Computer Vision,LED Pattern Identi?cation,UAVs,Wireless Sensor Networks,Precision Agriculture,Remote Sensing
更新于2025-09-23 15:21:01
-
Influence of Carbon Quantum Dots on the Biome
摘要： The latest class of engineered nanomaterials, viz., carbon quantum dots (CQDs), has attracted attention because they are synthesized through green chemical procedures and from organic waste matter. The synthesis of these nano-sized particles synthesized from biomass such as fruit peel and other organic matter results in mixtures of CQD species that di?er in chemical identity, activity and photo-physical properties. Generally used collectively as chemically heterogeneous ensemble, they have already had an impact on multiple sectors of our environment by use as wastewater sensors, switches, model agro-fertilizers, and in biomedicine. The transitioning of their applications to crops is an important crossover point that calls for an accurate and detailed assessment of their genomic, proteomic, and metabolomics impact on agriculturally important crops and produce. We review the current status of CQDs vis-à-vis their impact on the biosphere via recent model studies and comment on the knowledge gaps that need to be bridged to ensure their safe use in agronomy. A detailed knowledge of their impact on aquatic systems and the food-chain is critical for human and environmental safety and sustainability.
关键词： agriculture,environmental implications,Carbon Quantum Dots,toxicity
更新于2025-09-23 15:21:01
-
Testing of Various Monochromatic LED Lights Used in Supplemental Irradiation of Lettuce in Modern Urban Rooftop Polytunnels
摘要： Urban farming could provide both vegetable growers and urban dwellers with more direct access to various fresh vegetables. Nevertheless, certain challenging problems associated with urban farming, including a lack of cultivation space and the effects of urban heat islands, must still be solved. Relatedly, a grower must, in some cases, also know how to utilize various forms of technology, such as lighting systems, as well as factors such as water availability. In this study, an original rooftop polytunnel design for lettuce (Lactuca sativa cv. Lollo Rosso) cultivation equipped with a hydroponic system and light emitting diodes (LEDs) is proposed. Various monochromatic lights were also tested for their effects on different quality parameters of lettuce. Specifically, supplemental red (655 nm), blue (445 nm), green (520 nm), and ultraviolet (380 nm) LED lights were used at night to apply photon fluxes of 150, 150, 150, and 20 μmol·m-2·s-1, respectively. The resulting effects of these different colored LEDs on the pigment concentration and growth response of the lettuce grown inside the roof polytunnel were then investigated. The experiment was then repeated several times with different environmental parameters in order to compare the effects of the different light wavelengths under higher temperatures and higher natural irradiation conditions. The results indicated that supplemental red or blue light at night could be strategically employed to maintain low nitrate levels and enhance the nutritional value and growth of lettuce grown in roof polytunnels.
关键词： Red lettuce,Rooftop polytunnel,Hydroponic,LEDs,Urban agriculture,Light emitting-diodes
更新于2025-09-23 15:21:01
-
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems
摘要： Background: Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, automatic and consistent early detection and quantification of disease symptoms which are important in breeding programs for the development of improved cultivars and in crop production for the implementation of disease control measures for yield protection. Current methods of plant disease phenotyping are predominantly visual and hence are slow and prone to human error and variation. There has been increasing interest in hyperspectral imaging applications for early detection of disease symptoms. However, the high dimensionality of hyperspectral data makes it very important to have an efficient analysis pipeline in place for the identification of disease so that effective crop management decisions can be made. The focus of this work is to determine the minimal number of most effective hyperspectral wavebands that can distinguish between healthy and diseased soybean stem specimens early on in the growing season for proper management of the disease. 111 hyperspectral data cubes representing healthy and infected stems were captured at 3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control specimens from 4 different genotypes. Each hyperspectral image was captured at 240 different wavelengths in the range of 383–1032 nm. We formulated the identification of best waveband combination from 240 wavebands as an optimization problem. We used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for the identification of maximally-effective waveband combination. Results: A binary classification between healthy and infected soybean stem samples using the selected six waveband combination (475.56, 548.91, 652.14, 516.31, 720.05, 915.64 nm) obtained a classification accuracy of 97% for the infected class. Furthermore, we achieved a classification accuracy of 90.91% for test samples from 3 days after inoculation using the selected six waveband combination. Conclusions: The results demonstrated that these carefully-chosen wavebands are more informative than RGB images alone and enable early identification of charcoal rot infection in soybean. The selected wavebands could be used in a multispectral camera for remote identification of charcoal rot infection in soybean.
关键词： Band selection,Soybean disease,Precision agriculture,Hyperspectral,Support vector machines,Genetic algorithm,Charcoal rot
更新于2025-09-23 15:21:01
-
Is it a good time to develop commercial photovoltaic systems on farmland? An American-style option with crop price risk
摘要： Photovoltaic systems require large swaths of land that are currently being used for other purposes, such as farming. One option for developing large photovoltaic systems is converting farms that are currently economically unviable into commercial photovoltaic systems. However, this may not always be an economically rational decision as crop prices have the potential to increase over time. Fluctuations in farm income due to changes in crop prices can alter the optimal choice of whether to continue farming or to convert farmland into commercial photovoltaic systems. This study attempts to resolve this issue by proposing a real options framework to value farm production when crop prices are uncertain. By integrating uncertainty into the decision-making process, the value of keeping unprofitable farms operating prior to developing the area into a commercial photovoltaic system is assessed. This helps decision makers understand the extent to which potential income from developing a photovoltaic system should be greater than potential income from farming when deciding on investing in a photovoltaic system. A case study is conducted to examine this framework and to calculate the net present value of a farm in South Korea. The results indicate that although the money lost from continuing to farm is substantial, farmers should defer conversion to a commercial photovoltaic system until a sufficient drop in crop prices occurs. When applying this strategy, the farmer can gain an additional 100% of expected revenue simply by deferring the development decision until having better information on the market prices of crops.
关键词： Agriculture,Solar,Photovoltaic,Farmland,Life cycle cost,Real options
更新于2025-09-23 15:21:01