- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Experimental Implementation of Non-uniformity Effects in Artificial Media : (Invited)
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： land cover,river basin,time-series analysis,multisource remotely sensed data,phenology,Crop classification,HJ-1/CCD,multiple scales,multiple classifiers
更新于2025-09-19 17:13:59
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Yield and quality of lettuce in response to the plant position in photovoltaic greenhouse
摘要： In recent years there has been an increasing spread of photovoltaic greenhouses, especially in southern European countries, due to the higher incentives recognized to solar photovoltaic (PV) panels integrated on the greenhouse roofs and/or to local laws which limit the photovoltaic systems on the ground. To maximize the income from the production of electricity, often solar panels cover 50% or more of the roof, but the shading caused by these elements on the growing surface seriously limits the productivity and affects crop development. In order to assess the effects of the spatial distribution of the solar radiation inside these structures on yield and quality of leaf vegetable crops, the response of lettuce, grown in two cycles (autumn and winter-spring), in an east-west oriented photovoltaic greenhouse with the 50% of the roof covered by PV modules was analysed. The influence of span orientation, plant position (under plastic or PV roof) and cultivar were analysed as experimental treatments in a split-split plot design with two replications. Total and marketable yield of the lettuce heads and some quality parameters (dry matter and nitrate content) were evaluated. A significant variability of the total and marketable yield due to the plant position and as consequence to the solar radiation distribution inside the structure during the growing cycle was observed. Furthermore, ranges and variability of the nitrate content of lettuce (expressed as NO3 mg kg-1 of fresh weight) were affected by the plant position and harvest season. In order to maximize yield and quality of the crops, the arrangement of plant rows and transit areas, as well as the management of nutrition, should be optimized in relation to the shading caused by the PV roof during the growing cycle.
关键词： light distribution,crop yield,leaf vegetables,nitrate content,PV panels
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - Singapore, Singapore (2019.11.25-2019.11.28)] 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - A Comparative Study of Flexible Power Point Tracking Algorithms in Photovoltaic Systems
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： HJ-1/CCD,multiple classifiers,phenology,river basin,multiple scales,time-series analysis,Crop classification,land cover,multisource remotely sensed data
更新于2025-09-16 10:30:52
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Laser-induced breakdown spectroscopy as a promising tool in the elemental bioimaging of plant tissues
摘要： Laser-Induced Breakdown Spectroscopy (LIBS) is an optical analytical technique with a multi-element capability for element bioimaging in plants. During the years of LIBS development, the major application field has been in industry. However, during the last two decades, LIBS became a useful imaging tool in various biologic matrices, e.g. bones, mammals’ organs, and in the plant science. In this work, we present an overview of LIBS achievements in plant bioimaging which started in 2006. The progress in the assessment of spatial element distribution in plants is documented here with respect to the applications in phytotoxicity testing for the following reason: the information on the spatial distribution of elements can reveal a relationship between the exact location of an element and its toxic effect. This review discusses the state of the art of various elements’ bioimaging in plants using LIBS with a spatial resolution at micrometer scale.
关键词： crop plants,macronutrients,laser ablation,nanoparticles,element spatial distribution,micronutrients,2D-mapping,heavy metals,plants,model organisms,phytotoxicity,elemental imaging
更新于2025-09-12 10:27:22
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Coupling Waveguide-Based Micro-Sensors and Spectral Multivariate Analysis to Improve Spray Deposit Characterization in Agriculture
摘要： The leaf coverage surface is a key measurement of the spraying process to maximize spray efficiency. To determine leaf coverage surface, the development of optical micro-sensors that, coupled with a multivariate spectral analysis, will be able to measure the volume of the droplets deposited on their surface is proposed. Rib optical waveguides based on Ge-Se-Te chalcogenide films were manufactured and their light transmission was studied as a response to the deposition of demineralized water droplets on their surface. The measurements were performed using a dedicated spectrophotometric bench to record the transmission spectra at the output of the waveguides, before (reference) and after drop deposition, in the wavelength range between 1200 and 2000 nm. The presence of a hollow at 1450 nm in the relative transmission spectra has been recorded. This corresponds to the first overtone of the O–H stretching vibration in water. This result tends to show that the optical intensity decrease observed after droplet deposition is partly due to absorption by water of the light energy carried by the guided mode evanescent field. The probe based on Ge-Se-Te rib optical waveguides is thus sensitive throughout the whole range of volumes studied, i.e., from 0.1 to 2.5 μL. Principal Component Analysis and Partial Least Square as multivariate techniques then allowed the analysis of the statistics of the measurements and the predictive character of the transmission spectra. It confirmed the sensitivity of the measurement system to the water absorption, and the predictive model allowed the prediction of droplet volumes on an independent set of measurements, with a correlation of 66.5% and a precision of 0.39 μL.
关键词： principal component analysis (PCA),partial least squares (PLS),precision agriculture,droplet characterization,infrared spectroscopy,optical micro-sensors,crop protection
更新于2025-09-12 10:27:22
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Evaluation of the laser leveled land leveling technology on crop yield and water use productivity in Western Uttar Pradesh
摘要： A study has been conducted for 3 year on impacts of the laser land leveling versus traditional land leveling on water use productivity and crop yields. The major concerns were effectiveness of laser land leveling as a water saving tool in the new context of land use and ownership, affordability of laser land leveling for farmers and the economic viability of this technology. These research questions were studied in a sizable area of laser leveled and neighboring non-leveled (control) fields for 2009 to 2011. The result indicated that with laser leveling, farmers could save irrigation water 21%, energy by 31% and obtained 6.6, 5.4 and 10.9% in rice, wheat and sugarcane higher yields. The total irrigation duration and applied water depth was reduced to 10.9, 14.7% in rice; 13.7, 13.3% in wheat and 13.5, 20.3% in sugar-cane as compared to traditional leveled fields. The laser leveled fields exhibited the highest water use efficiency (WUE), which was 48, 47 and 49% higher in precisely leveled field than control (unleveled), 22, 19 and 20% higher than traditionally leveling fields, respectively. The average water productivity in rice, wheat and sugarcane has improved by 33%. The average annual net income from the laser field was 14, 13.5 and 23.8% in rice, wheat, sugarcane higher than that from the traditional leveled field. It was concluded that the use of laser land leveling increases yield and saves irrigation water as compared to traditional method of leveling in different cropping system prevailing in western U.P.
关键词： Crop productivity,water use efficiency,laser leveled land leveling,water productivity
更新于2025-09-11 14:15:04
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Solar light distribution inside a greenhouse with the roof area entirely covered with photovoltaic panels
摘要： Most photovoltaic (PV) greenhouses in Europe have maximised the energy production without considering the requirements of the crops, by applying PV panels on most part of the roof area. The aim of this work is to calculate the solar light distribution in a photovoltaic (PV) greenhouse where the entire roof area is covered with PV panels (100% cover ratio). The calculation of the incident global was estimated with clear sky conditions on several observation points located inside the greenhouse at 1.5 m from the ground level. The validation of the simulated data was conducted through measurements inside a PV greenhouse complex located in Florinas, Italy (40°38’38”N; 8°39’31”E), composed by 24 single-span greenhouse modules of 837 m2 each and total area of 2.2 ha. The roof area of each module was completely covered with a monocrystalline PV array with slope of 20° and a rated power of 22 kWp. The results were shown through a map of light distribution on the greenhouse area, which highlighted the most penalised zones and the percentage of available global radiation, compared to the same greenhouse without PV array. Good agreement was shown by the simulated data, compared to measurements (mean R2=0.78). The global radiation on the greenhouse area was 33% on yearly basis, compared to the potential value with no PV panels on the roof. The zones close to the gable walls and the south side wall suffered less shading compared to the central portion of the greenhouse area. The map of cumulated light distribution can support the growers to increase the agronomic sustainability of the PV greenhouse, since it will allow in perspective the identification of species and crop management practices for a profitable cultivation.
关键词： agricultural sustainability,PV greenhouse,crop,model,solar radiation,energy
更新于2025-09-11 14:15:04
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Polsar Image Crop Classification Based on Deep Residual Learning Network
摘要： PolSAR image classification is one of the most basic applications of polarimetric synthetic aperture radar (PolSAR) data, which is of great significance to the research and subsequent application of PolSAR data. Traditional PolSAR image classification methods, mainly based on a single type of target decomposition method, the dimension of feature used in the process of PolSAR image classification process is relatively less and cannot make full use of the abundant feature of the PolSAR image, which is the one of the most essential characteristics of PolSAR data. With the development of deep learning, an amount of excellent deep learning models is proposed, such as deep brief net, AlexNet, deep residual network (ResNet) and so on. The classification method based on deep learning is proved to be better than traditional methods in the classification of optical and SAR images. This paper mainly analyzes the application of ResNet model in PolSAR image classification, the effectiveness of the method was proved by comparing the classical PolSAR image classification method. Firstly, some target decomposition methods were selected to calculate the multi-dimensional feature image. Secondly, the sample points of different land cover types were manually selected, and the multi-dimensional features were extracted to form the experimental data samples. Then, the PolSAR classification model based on ResNet was constructed, and the model parameters were adjusted dynamically according to the experimental sample data. Finally, the trained model was applied to the classification of experimental data, and the accuracy of the model was assessed by calculating the Kappa index of the classification result. In this paper, a quantitative index is proposed to calculate the ability of each feature to distinguish different land cover types, and the weak distinguishing feature was deleted to reduce the influence of classification independent features on the model and to improved classification accuracy. As for the speckle noise, the PolSAR image was preprocessed by simple linear iterative clustering the experimental image was divided into a determined number of superpixel blocks, and the PolSAR image classification based on super-pixel blocks. Experimental results show that the PolSAR image classification method based on ResNet is conducive the comprehensive utilization of multi- dimensional features of PolSAR image, the classification accuracy of PolSAR image is better than that of the classic classification method. The optimization of feature sets is beneficial to reduce model training time and improve the classification accuracy of PolSAR image as well. The superpixel segmentation is beneficial to reduce speckle noise and further improves the accuracy of classification.
关键词： Simple linear iterative cluster,PolSAR image,Crop classification,Deep residual network,Feature optimization
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Combination of Crop Growth Model and Radiation Transfer Model with Remote Sensing Data Assimilation for Fapar Estimation
摘要： Accurate assessment of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in large scale is significant for crop productivity estimation and climate change analysis. The object of study is to simulate FAPAR in the rice growth period for exploring photosynthetic capacity of rice in large-scale. The daily FAPAR is calculated based on a coupled model consisting of the leaf-canopy radiative transfer model (PROSAIL) and the World Food Study Model (WOFOST). Due to the limitation of the PROSAIL and WOFOST model, we introduced the remote sensing data assimilation method, which assimilated the Normalized Difference Vegetation Index (NDVI) into the coupled model, to improve the prediction accuracy and carry out the large-scale application. The results show high correlation between the simulated FAPAR and the measured data, with the determinate coefficient (??2) of 0.75 in the study area. The spatial distribution of FAPAR is uniform in flat area, which indicates that the rice in the whole study area has well growth condition and photosynthetic capacity. This study suggest that the coupled model (PROSAIL + WOFOST) assimilated with remote sensing data could accurately simulate daily FAPAR during the crop growth period.
关键词： crop model,assimilation,FAPAR,coupled model,PROSAIL
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Image Classification Based on Spectral Mixture Analysis for Crop Type Determination
摘要： For the application of agricultural area, remote sensing techniques were studied and applied for its advantages for continuous and quantitative monitoring. Especially, hyperspectral images have been studied for the precise agriculture since they provide chemical and physical information of vegetation. In this study, we analyzed crop types using hyperspectral image data collected by a ground scanner. Spectral mixture analysis, which is widely used for processing hyperspectral images, was adopted for the crop discrimination. Endmember extraction algorithms used in this study were N-FINDR, Vertex Component Analysis (VCA), and Simplex Identification via variable Splitting and Augmented Lagrangian (SISAL), and classification was processed using fully constrained linear spectral unmixing (FCLSU). This study presents the application of spectral mixture analysis for hyperspectral scanner data at canopy level and optimal endmember extraction algorithms for different crop types for precise agriculture.
关键词： Hyperspectral images,crop types,classification,spectral mixture analysis
更新于2025-09-10 09:29:36