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Gerrit Hoogenboom - One of the best experts on this subject based on the ideXlab platform.
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Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A Review
Remote Sensing, 2010Co-Authors: Sudhanshu S. Panda, Gerrit HoogenboomAbstract:Site-specific Crop management (SSCM) is one facet of precision agriculture which is helping increase production with minimal input. It has enhanced the cost-benefit scenario in Crop production. Even though the SSCM is very widely used in row Crop agriculture like corn, wheat, rice, soybean, etc. it has very little application in cash Crops like fruit and nut. The main goal of this review paper was to conduct a comprehensive review of advanced technologies, including geospatial technologies, used in site-specific management of fruit and nut Crops. The review explores various remote sensing data from different platforms like satellite, LIDAR, aerial, and field imaging. The study analyzes the use of satellite sensors, such as Quickbird, Landsat, SPOT, and IRS imagery as well as hyperspectral narrow-band remote sensing data in study of fruit and nut Crops in blueberry, citrus, peach, apple, etc. The study also explores other geospatial technologies such as GPS, GIS spatial modeling, advanced image processing techniques, and information technology for suitability study, orchard delineation, and classification accuracy assessment. The study also provides an example of a geospatial model developed in ArcGIS ModelBuilder to automate the blueberry production suitability analysis. The GIS spatial model is developed using various Crop Characteristics such as chilling hours, soil permeability, drainage, and pH, and land cover to determine the best sites for growing
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using pattern recognition for estimating cultivar coefficients of a Crop simulation model
Field Crops Research, 2009Co-Authors: Mohammad Bannayan, Gerrit HoogenboomAbstract:Abstract The introduction of a new cultivar in a process-based Crop simulation model requires the estimation of cultivar coefficients that define its growth and development Characteristics. An accurate estimation of these coefficients requires replicated field experiments that, in many cases, are not available to Crop model users. The objective of this study was to employ a pattern recognition approach to estimate cultivar coefficients from a minimum set of experimental data for use with a Crop simulation model. The pattern recognition approach is based on similarity measures. Its main goal is to classify groups of data or patterns based on either a priori knowledge or on statistical information extracted from the patterns. Based on the similarity measure as the central calculation of the pattern recognition approach, the algorithm searches the space of features of other cultivars in the database to find the most similar cultivar as the best match to the target cultivar. The approach of this study was based on a few key Characteristics of maize Crop growth and development, including anthesis and harvest maturity dates, maximum leaf area index (LAImax), final above ground biomass, and grain yield, which were used as the features vector. To construct the feature database, 27,789 hypothetical cultivars were constructed by combining different values of the six cultivar coefficients of the Cropping System Model (CSM)-CERES-Maize. Experiments performed in Florida (FL) and Iowa (IA) USA, Spain, central Punjab, Pakistan, and in Piracicaba, SP, Brazil were selected and later modified to provide a full potential production environment. The Crop model was run for potential production for all 27,789 hypothetical cultivars and the outputs of these simulations were used as the feature database. For evaluation of this approach, we used the features for 29 different maize cultivars as reported from field experiments that are available in DSSAT maize cultivar database and also for four additional cultivars of which two had not been used in any aspect of this study. The model was run for all 33 cultivars, using the best match cultivar coefficients, for the conditions of the three study sites and locations where the latter four cultivars have been grown. The simulated Crop Characteristics were compared with the same simulated Crop Characteristics based on the original coefficients used to run the simulation model. We found that the approach based on pattern recognition was able to estimate the cultivar coefficients with a reasonable accuracy. The coefficient of determination (r2), root mean square difference (RMSD), and relative root mean square of difference (RMSDr) confirmed that this approach provided reliable estimates for the maize cultivar coefficients. The highest R2 (0.98) was obtained for anthesis in Florida and the lowest (0.57) was obtained for grain yield in Spain. The highest RMSD (8.8) was obtained for maturity in Spain, while the lowest RMSD (1.1) was obtained for aboveground biomass in Florida. Although the values for RMSD were different across the different sites, this approach provided a level of accuracy that might be acceptable, especially for users who only have one year of experimental data and demand the best possible initial guess for the coefficients of their specific cultivar. This approach has been implemented in a simple tool that can be easily applied by users of DSSAT and the CSM-CERES-Maize model.
Erik S Runkle - One of the best experts on this subject based on the ideXlab platform.
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growth responses of red leaf lettuce to temporal spectral changes
Frontiers in Plant Science, 2020Co-Authors: Qingwu Meng, Erik S RunkleAbstract:Lighting is typically static for indoor production of leafy greens. However, temporal spectrum differentiation for distinct growth phases can potentially control age-specific desirable traits. Spectral effects can be persistent yet dynamic as plants mature, necessitating characterization of time-dependent responses. We grew red-leaf lettuce (Lactuca sativa L.) ‘Rouxai’ in a growth room at 23 °C and under a 20-h photoperiod created by warm-white (WW), blue (B; peak = 449 nm), green (G; peak = 526 nm), red (R; peak = 664 nm), and/or far-red (FR; peak = 733 nm) light-emitting diodes. From day 0 to 11, plants received six static lighting treatments with the same total photon flux density (400–800 nm): WW180, R180, B20R160, B20G60R100, B20R100FR60, or B180 (subscripts denote photon flux densities in µmol∙m–2∙s–1). On day 11, plants grown under each of the six treatments were transferred to all treatments, which created 36 temporal spectrum alternations. Plant growth, morphology, and coloration were measured on days 11 and 25. Increasing B radiation from 0 to 100% in static treatments decreased shoot fresh and dry weights and increased foliage redness of seedlings and mature plants. Compared to B20R160, B20R100FR60 increased shoot fresh weight, but not dry weight, on both days. However, other phenotypic responses under static treatments changed over time. For example, leaf length under B180 was 35% lower on day 11 but similar on day 25 compared to that under R180. In the B20 background, substituting G60 for R radiation did not influence shoot weight on day 11 but decreased it by 19% on day 25. When plants were switched from one treatment to another on day 11, the treatments applied before day 11 influenced final shoot weight and, to a lesser extent, leaf length and foliage coloration on day 25. In comparison, effects of the treatments applied after day 11 were more pronounced. We conclude some phenotypic responses to light quality depend on time and sequential light quality treatments had cumulative effects on lettuce growth. The temporal complexity of spectral responses is critical in photobiological research and creates opportunities for time-specific spectrum delivery to optimize Crop Characteristics.
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photosynthetic daily light integral influences flowering time and Crop Characteristics of cyclamen persicum
Hortscience, 2009Co-Authors: In Hye Cheon, Ki Sun Kim, Erik S RunkleAbstract:This study was carried out to examine the effect of photosynthetic daily light integral (DLI) on the growth and flowering of cyclamen (Cyclamen persicum Mill. 'Metis Scarlet Red'). Plants with six fully unfolded leaves were grown at 24/16 8C (12 h/12 h) under an 8- or 16-h photoperiod at a photosynthetic photon flux of 50, 100, 150, 200, and 300 mmolm -2 s -1 , which provided seven DLIs: 1.4, 2.9, 4.3, 5.8, 8.6, 11.5, and 17.3 molm -2 d -1 . Days to first flower decreased from 133 to 75 as DLI increased from 1.4 to 17.3 molm -2 d -1 , although the acceleration of flowering was less pronounced when the DLI wasgreater than5.8 molm -2 d -1 . Meanleaf andflowernumberincreased from8.7 to 28.0 and from 0 to 14.7, respectively, as DLI increased from 1.4 to 11.5 molm -2 d -1 , but there was no further increase under a DLI of 17.3 molm -2 d -1 . Total dry weight and net photosynthetic rate showed a similar trend as leaf and flower number. We conclude that supplemental lighting can accelerate greenhouse production of potted cyclamen under a low ambient DLI (i.e., less than 12 molm -2 d -1 ).
John H Prueger - One of the best experts on this subject based on the ideXlab platform.
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value of using different vegetative indices to quantify agricultural Crop Characteristics at different growth stages under varying management practices
Remote Sensing, 2010Co-Authors: Jerry L Hatfield, John H PruegerAbstract:The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural Crop Characteristics at different growth stages. Research was conducted on four Crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management practices that varied rate and timing. Six different vegetation indices were found most useful, depending on Crop phenology and management practices: (a) simple ratio for biomass, (b) NDVI for intercepted PAR, (c) SAVI for early stages of LAI, (d) EVI for later stages of LAI, (e) CIgreen for leaf chlorophyll, (f) NPCI for chlorophyll during later stages, and (g) PSRI to quantify plant senescence. There were differences among varieties of corn and soybean for the vegetation indices during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural Crop Characteristics.
Eddy De Pauw - One of the best experts on this subject based on the ideXlab platform.
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hyperspectral vegetation indices and their relationships with agricultural Crop Characteristics
Remote Sensing of Environment, 2000Co-Authors: Prasad S Thenkabail, Ronald B Smith, Eddy De PauwAbstract:The objective of this paper is to determine spectral bands that are best suited for characterizing agricultural Crop biophysical variables. The data for this study comes from ground-level hyperspectral reflectance measurements of cotton, potato, soybeans, corn, and sunflower. Reflectance was measured in 490 discrete narrow bands between 350 and 1,050 nm. Observed Crop Characteristics included wet biomass, leaf area index, plant height, and (for cotton only) yield. Three types of hyperspectral predictors were tested: optimum multiple narrow band reflectance (OMNBR), narrow band normalized difference vegetation index (NDVI) involving all possible two-band combinations of 490 channels, and the soil-adjusted vegetation indices. A critical problem with OMNBR models was that of “over fitting” (i.e., using more spectral channels than experimental samples to obtain a highly maximum R2 value). This problem was addressed by comparing the R2 values of Crop variables with the R2 values computed for random data of a large sample size. The combinations of two to four narrow bands in OMNBR models explained most (64% to 92%) of the variability in Crop biophysical variables. The second part of the paper describes a rigorous search procedure to identify the best narrow band NDVI predictors of Crop biophysical variables. Special narrow band lambda (λ1) versus lambda (λ2) plots of R2 values illustrate the most effective wavelength combinations (λ1 and λ2) and bandwidths (Δλ1 and Δλ2) for predicting the biophysical quantities of each Crop. The best of these two-band indices were further tested to see if soil adjustment or nonlinear fitting could improve their predictive accuracy. The best of the narrow band NDVI models explained 64% to 88% variability in different Crop biophysical variables. A strong relationship with Crop Characteristics is located in specific narrow bands in the longer wavelength portion of the red (650 nm to 700 nm), with secondary clusters in the shorter wavelength portion of green (500 nm to 550 nm), in one particular section of the near-infrared (900 nm to 940 nm), and in the moisture sensitive near-infrared (centered at 982 nm). This study recommends a 12 narrow band sensor, in the 350 nm to 1,050 nm range of the spectrum, for optimum estimation of agricultural Crop biophysical information.
Sudhanshu S. Panda - One of the best experts on this subject based on the ideXlab platform.
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Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A Review
Remote Sensing, 2010Co-Authors: Sudhanshu S. Panda, Gerrit HoogenboomAbstract:Site-specific Crop management (SSCM) is one facet of precision agriculture which is helping increase production with minimal input. It has enhanced the cost-benefit scenario in Crop production. Even though the SSCM is very widely used in row Crop agriculture like corn, wheat, rice, soybean, etc. it has very little application in cash Crops like fruit and nut. The main goal of this review paper was to conduct a comprehensive review of advanced technologies, including geospatial technologies, used in site-specific management of fruit and nut Crops. The review explores various remote sensing data from different platforms like satellite, LIDAR, aerial, and field imaging. The study analyzes the use of satellite sensors, such as Quickbird, Landsat, SPOT, and IRS imagery as well as hyperspectral narrow-band remote sensing data in study of fruit and nut Crops in blueberry, citrus, peach, apple, etc. The study also explores other geospatial technologies such as GPS, GIS spatial modeling, advanced image processing techniques, and information technology for suitability study, orchard delineation, and classification accuracy assessment. The study also provides an example of a geospatial model developed in ArcGIS ModelBuilder to automate the blueberry production suitability analysis. The GIS spatial model is developed using various Crop Characteristics such as chilling hours, soil permeability, drainage, and pH, and land cover to determine the best sites for growing