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Remote Sensing: An Automated Methodology for Olive Tree Detection and Counting in Satellite Images
摘要： Cultivation of olive trees for the past few years has been widely spread across Mediterranean countries, including Spain, Greece, Italy, France, and Turkey. Among these countries, Spain is listed as the largest olive producing country with almost 45% of olive oil production per year. Dedicating land of over 2.4 million hectares for the olive cultivation, Spain is among the leading distributors of olives throughout the world. Due to its high signi?cance in the country’s economy, the crop yield must be recorded. Manual collection of data over such expanded ?elds is humanly infeasible. Remote collection of such information can be made possible through the utilization of satellite imagery. This paper presents an automated olive tree counting method based on image processing of satellite imagery. The images are pre-processed using the unsharp masking followed by improved multi-level thresholding-based segmentation. Resulting circular blobs are detected through the circular Hough transform for identi?cation. Validation has been performed by evaluating the proposed scheme for the dataset formed by acquiring images through the ‘‘El Sistema de Información Geográ?ca de Parcelas Agrícolas’’ viewer over the region of Spain. The proposed algorithm achieves an accuracy of 96% in detection. Computation time was recorded as 24 ms for an image size of 300 × 300 pixels. The less spectral information is used in our proposed methodology resulting in a competitive accuracy with low computational cost in comparison to the state-of-the-art technique.
关键词： crop estimation,multi-spectral imagery,Remote sensing,olive,Hough transform,satellite imagery
更新于2025-09-23 15:23:52
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Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS
摘要： As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R2 of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R2 of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.
关键词： remote sensing,plant breeding,crop,proximal sensing,phenotyping
更新于2025-09-23 15:21:21
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GreenLight a?? An open source model for greenhouses with supplemental lighting: Evaluation of heat requirements under LED and HPS lamps
摘要： Greenhouse models are important tools for the analysis and design of greenhouse systems and for offering decision support to growers. While many models are available, relatively few include the influence of supplementary lighting on the greenhouse climate and crop. This study presents GreenLight, a model for greenhouses with supplemental lighting. GreenLight extends state of the art models by describing the qualitative difference between the common lighting system of high-pressure sodium (HPS) lamps, and the newest technology for horticultural lighting - the light-emitting diodes (LEDs). LEDs differ from HPS lamps in that they operate at lower temperatures, emit mostly convective heat and relatively little radiative heat, and can be more efficient in converting electricity to photosynthetically active radiation (PAR). These differences can have major implications on the greenhouse climate and operation, and on the amount of heat that must be supplied from the greenhouse heating system. Model predictions have been evaluated against data collected in greenhouse compartments equipped with HPS and LED lamps. The model predicted the greenhouse's heating needs with an error of 8e51 W m-2, representing 1e12% of the measured values; the RMSE for indoor temperature was 1.74e2.04 °C; and the RMSE for relative humidity was 5.52e8.5%. The model is freely available as open source MATLAB software at https://github.com/davkat1/GreenLight. It is hoped that it may be further evaluated and used by researchers worldwide to analyse the influence of the most recent lighting technologies on greenhouse climate control.
关键词： Energy use,Greenhouse lighting,LEDs,Greenhouse models,Crop models,Open source
更新于2025-09-23 15:21:01
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Application of infrared thermography to assess cassava physiology under water deficit condition
摘要： Water deficit stress is a major factor that inhibits the overall growth and development in cassava (Manihot esculenta), leading to decreased storage root yield. We conducted a study to investigate whether thermal sensing could be used to indicate water deficit stress and the health and yield of cassava crops in field. The objective of the study was to use thermal imaging to determine relationship between crop water stress index (CWSI) and physiological changes, and to identify the critical CWSI point in fields of cassava cv. Rayong 9 under well-irrigated and water-deficit conditions. At the time of storage root initiation (85 DAP [day after planting]), thermal imagery was collected and the physiological changes and growth characters were measured prior to storage root harvesting (162 DAP). Thermal infrared imager was used to measure the canopy temperature and CWSI of cassava plants. Net photosynthetic rate (Pn), stomatal conductance (gs) and transpiration rates (Tr) of cassava plants under water deficit conditions for 29 d (114 DAP) were significantly decreased, leading to delayed plant growth as compared to those under well-irrigated conditions. In contrast, air vapor pressure deficit (VPDair) and CWSI in drought-stressed plants were higher than well irrigated plants. High correlations between Tr/gs/Pn and CWSI were observed. The study concludes that CWSI is a sensitive indicator of water deficit stress caused due to stomatal function.
关键词： net photosynthetic rate,crop water stress index,thermal imagery,Cassava,stomatal conductance
更新于2025-09-23 15:21:01
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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 - Monitoring Key Agricultural CROPS in the Netherlands using Sentinel-1
摘要： In this study, we performed ground validation to support the interpretation of Sentinel-1 imagery during a full growing season of five key crop types in the Netherlands. Crop height and growth stage were monitored weekly in a total of 25 parcels of maize, potato, sugar beet, wheat and English ryegrass in the province of Flevoland. Hydrometeorological data were collected throughout the season. Here, these results are used to interpret time series of Sentinel-1 data processed for the province of Flevoland. Results demonstrate that Sentinel-1 data follow the phenological stages and can be used to identify key moments in crop development. Combined with the guaranteed availability of observations regardless of cloud cover, this makes Sentinel-1 data a valuable resource for agencies and commercial entities providing advice to farmers and agro-industrial co-operatives.
关键词： SAR,vegetation,crop monitoring,radar,agriculture
更新于2025-09-23 15:21:01
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Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal
摘要： The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in S?o Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.
关键词： vegetation index,satellite images,yield monitoring,hyperspectral sensor,crop residue management,remote sensing
更新于2025-09-19 17:15:36
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Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm
摘要： Novel hyperspectral indices, which are the first derivative normalized difference nitrogen index (FD-NDNI) and the first derivative ratio nitrogen vegetation index (FD-SRNI), were developed to estimate the leaf nitrogen content (LNC) of wheat. The field stress experiments were conducted with different nitrogen and water application rates across the growing season of wheat and 190 measurements were collected on canopy spectra and LNC under various treatments. The inversion models were constructed based on the dataset to evaluate the ability of various spectral indices to estimate LNC. A comparative analysis showed that the model accuracies of FD-NDNI and FD-SRNI were higher than those of other commonly used hyperspectral indices including mNDVI705, mSR, and NDVI705, which was indicated by higher R2 and lower root mean square error (RMSE) values. The least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms were then used to optimize the models constructed by FD-NDNI and FD-SRNI. The p-R2 values of the FD-NDNI_RFR and FD-SRNI_RFR models reached 0.874 and 0.872, respectively, which were higher than those of the exponential and SVR model and indicated that the RFR model was accurate. Using the RFR inversion model, remote sensing mapping for the Operative Modular Imaging Spectrometer (OMIS) image was accomplished. The remote sensing mapping of the OMIS image yielded an accuracy of R2 = 0.721 and RMSE = 0.540 for FD-NDNI and R2 = 0.720 and RMSE = 0.495 for FD-SRNI, which indicates that the similarity between the inversion value and the measured value was high. The results show that the new hyperspectral indices, i.e., FD-NDNI and FD-SRNI, are the optimal hyperspectral indices for estimating LNC and that the RFR algorithm is the preferred modeling method.
关键词： derivative,spectral index design,hyperspectral remote sensing,algorithm optimization,crop parameter inversion
更新于2025-09-19 17:15:36
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Hypercube States for Sub-Planck Sensing
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： Crop classification,river basin,multisource remotely sensed data,phenology,time-series analysis,multiple classifiers,multiple scales,HJ-1/CCD,land cover
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Ultra-Stable Optical Oscillator Transfer to the UV for Primary Thermometry
摘要： To improve the performance of crop models for regional crop yield estimates, a particle filter (PF) was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)—Wheat model. Two experiments involving winter wheat yield estimations were conducted at a field plot and on a regional scale to test the feasibility of the PF-based data assimilation strategy and to analyze the effects of the PF parameters and spatio-temporal scales of assimilating observations on the performance of the crop model data assimilation. The significant improvements in the yield estimation suggest that PF-based crop model data assimilation is feasible. Winter wheat yields from the field plots were forecasted with a determination coefficient (R2) of 0.87, a root-mean-square error (RMSE) of 251 kg/ha, and a relative error (RE) of 2.95%. An acceptable yield at the county scale was estimated with a R2 of 0.998, a RMSE of 9734 t, and a RE of 4.29%. The optimal yield estimates may be highly dependent on the reasonable spatiotemporal resolution of assimilating observations. A configuration using a particle size of 50, LAI maps with a moderate spatial resolution (e.g., 1 km), and an assimilation interval of 20 d results in a reasonable tradeoff between accuracy and effectiveness in regional applications.
关键词： particle filter (PF),yield estimation,data assimilation,Crop model,leaf area index,remote sensing
更新于2025-09-19 17:13:59
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Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images
摘要： This work aims to provide daily estimates of the evolution of popcorn dry masses at the field scale using an agro-meteorological model, named the simple algorithm for yield model combined with a water balance model (SAFY-WB), controlled by the Green Area Index (GAI), derived from satellite images acquired in the microwave and optical domains. Synthetic aperture radar (SAR) satellite information (σ? VH/VV) was provided by the Sentinel-1A (S1-A) mission through two orbits (30 and 132), with a repetitiveness of six days. The optical data were obtained from the Landsat-8 mission. SAR and optical data were acquired over one complete agricultural season, in 2016, over a test site located in the southwest of France. The results show that the total dry masses of corn can be estimated accurately (R2 = 0.92) at daily time steps due to a combination of satellite and model data. The SAR data are more suitable for characterizing the first period of crop development (until the end of flowering), whereas the optical data can be used throughout the crop cycle. Moreover, the model offers good performances in plant (R2 = 0.90) and ear (R2 = 0.93) mass retrieval, irrespective of the phenological stage. The results also reveal that four phenological stages (four to five leaves, flowering, ripening, and harvest) can be accurately predicted by the proposed approach (R2 = 0.98; root-mean-square error (RMSE) = seven days). Nevertheless, some important points must be taken into account before assimilation, namely the SAR signal must be corrected with respect to thermal noise before being assimilated, and the relationship estimated between the GAI and SAR signal must be performed over fields cultivated without intercrops. These results are unique in the literature and provide a new way to better monitor corn production over time.
关键词： remote sensing,SAR,maize,crop production,Sentinel-1,SAFY–WB
更新于2025-09-19 17:13:59