Showing papers on "Precision agriculture published in 2001"
TL;DR: This paper summarizes the work completed to date to investigate the use of airborne remote-sensing for weed mapping in crops, and discusses application of the technology in precision weed management practices.
726 citations
TL;DR: Moran et al. as discussed by the authors used remotely sensed imagery to compare differ- crop management methods is difficult and expensive ent vegetation indices as a means of assessing canopy variation and where available, remotely sensed images its resultant impact on corn (Zea mays L.) grain yield.
Abstract: ground-based booms, aircraft, or satellites—is a potentially important source of data for site-specific crop manRemote sensing—the process of acquiring information about obagement, providing spatial and temporal information jects from remote platforms such as ground-based booms, aircraft, or satellites—is a potentially important source of data for site-specific (NRC, 1997). Obtaining temporal information that is crop management, providing both spatial and temporal information. detailed and spatially distributed from other site-specific Our objective was to use remotely sensed imagery to compare differ- crop management methods is difficult and expensive ent vegetation indices as a means of assessing canopy variation and (NRC, 1997). Where available, remotely sensed images its resultant impact on corn (Zea mays L.) grain yield. Treatments show spatial and spectral variations resulting from soil consisted of five N rates and four hybrids, which were grown under and crop characteristics. One potential advantage of irrigation near Shelton, NE on a Hord silt loam in 1997 and 1998. remote-sensing imagery is that it is not limited by samImagery data with 0.5-m spatial resolution were collected from aircraft pling interval or geostatistical interpolation, as has been on several dates during both seasons using a multispectral, four-band implied for grid-sampled soil test data (Moran et al., [blue, green, red, and near-infrared reflectance] digital camera system. 1997). Imagery was imported into a geographical information system (GIS) and then georegistered, converted into reflectance, and used to com- For more than 30 yr, remote sensing has been envipute three vegetation indices. Grain yield for each plot was determined sioned as a valuable source of information for crop at maturity. Results showed that green normalized difference vegeta- management. The pioneering research of Colwell (1956) tion index (GNDVI) values derived from images acquired during showed that infrared aerial photography could be used midgrain filling were the most highly correlated with grain yield; to detect loss of vigor from disease in wheat (Triticum maximum correlations were 0.7 and 0.92 in 1997 and 1998, respectively. aestivum L.) and other small grains. One of the earliest Normalizing GNDVI and grain yield variability within hybrids im- digital remote-sensing analysis procedures developed to proved the correlations in both years, but more dramatic increases identify the vegetation contribution in an image was the were observed in 1997 (0.7 to 0.82) than in 1998 (0.92 to 0.95). This ratio vegetation index (RVI), created by dividing nearsuggested GNDVI acquired during midgrain filling could be used infrared reflectance (NIR) by red reflectance (Jordan, to produce relative yield maps depicting spatial variability in fields,
387 citations
TL;DR: In this article, a mobile data acquisition system for ECa was developed using the Geonics EM38 1 sensor, which was mounted on a wooden cart pulled behind an all-terrain vehicle, which also carried a GPS receiver and data collection computer.
371 citations
TL;DR: In this article, the satellite positioning system and electronic communication standards are integrated into all procedures connected to precision farming, such as site-specific application of fertilisers, with the resulting cost advantages being quite small.
317 citations
TL;DR: In this paper, an unsupervised classification of topographic attributes and soil electrical conductivity was used to identify management zones for use in precision agriculture using data collected in two fields located in central Missouri were used to test the proposed methodology.
Abstract: The objective of this research was to determine if unsupervised classification of topographic attributes and soil electrical conductivity could identify management zones for use in precision agriculture. Data collected in two fields located in central Missouri were used to test the proposed methodology. Principal component analysis was used to determine which layers of data were most important for representing within-field variability. Unsupervised clustering algorithms implemented in geographic information system (GIS) software were then used to divide the fields into potential management zones. Grain yield data obtained using a full-size combine equipped with a commercial yield sensing system and global positioning system (GPS) receiver were used to analyze the "goodness" of the potential management zones defined for each field. Principal component analysis of input variables for Field 1 indicated that elevation and bulk soil electrical conductivity (EC) were more important attributes than slope and Compound Topographic Index (CTI) for defining claypan soil management zones. The optimum number of zones to use when dividing a field may vary from year to year and was mainly a function of weather and the crop planted. The number of zones decreased if adequate moisture conditions were present throughout the cropping season (unpredictable) or if crops tolerant to water stress were planted (predictable). This classification procedure is fast, can be easily automated in commercially available GIS software, and has considerable advantages when compared to other methods for delineating within-field management zones.
252 citations
TL;DR: In this article, the authors evaluated the utility of the CROPGRO-Soybean simulation model and remote sensing in the interpretation of a soybean yield map, which was executed on areas within the field defined as reasonably uniform by a NDVI analysis.
202 citations
TL;DR: In this article, the authors present a review of the state of site-specific management at the turn of the millennium and offer some speculation as to its future course and the potential effects of large-scale adoption of SSM, should this adoption occur.
199 citations
01 Jan 2001
TL;DR: This article examined the factors that influence the adoption of two emerging agricultural technologies, genetically engineered crops and precision agriculture in corn and soybean production, and contrasted the relative influence of various factors on the adoption decision for these two technologies, with special emphasis on the role of farm size.
Abstract: This paper examines the factors that influence the adoption of two emerging agricultural technologies, genetically engineered crops and precision agriculture in corn and soybean production, and contrasts the relative influence of various factors on the adoption decision for these two technologies, with special emphasis on the role of farm size.
128 citations
01 Jan 2001
TL;DR: There are multiple technological barriers that relate to machinery, sensor, GPS, software, and remote sensing, however, these barriers will be progressively lifted and precision agriculture will be a significant component of the agricultural system of the future.
92 citations
TL;DR: The proposed online system distinguishes crop from weeds based on multi-spectal reflectance gathered with an imaging spectrograph under field conditions were recognized herbicide reductions of up to 90%.
89 citations
TL;DR: In this paper, the need for spatial information on soil properties at the field level is increasing, particularly for its applications in precision agriculture and environmental management, and one important soil property is clay content.
Abstract: The need for spatial information on soil properties at the field level is increasing, particularly for its applications in precision agriculture and environmental management. One important soil property is clay content; however, costs involved with obtaining soil data at the field scale are prohibit
TL;DR: The concept of precision agriculture, based on information technology, is becoming an attractive idea for managing natural resources and realizing modern sustainable agricultural development as mentioned in this paper, which is bringing agriculture into the digital and information age.
TL;DR: In this paper, tools for managing spatial variability are demonstrated in combination with tools to optimize management in environmental and economic terms.
TL;DR: In this article, the authors demonstrate proof of concept of how precision agriculture coupled with crop simulation models and geographic information systems technology can be used in the cotton production system in the Mid South to optimize yields while minimizing water and nitrogen inputs.
TL;DR: In this article, the authors developed an efficient procedure for estimating spatially variable soil properties for the CROPGRO-Soybean model, and demonstrated its use in diagnosing areas in the field where excess water or water stress could reduce soybean yield.
Abstract: Crop models have been useful for identifying underlying causes of yield variability and evaluating management
prescriptions. However, estimating the spatial soil inputs required to calibrate crop models to historic yields has proven to
be challenging and time consuming. Currently, calibration techniques require excessive computer time when applied over
many grid points within a field, and procedures for estimating unknown inputs are not well defined. The objectives of this
research were: (1) to develop an efficient procedure for estimating spatially variable soil properties for the
CROPGRO–Soybean model, and (2) to demonstrate its use in diagnosing areas in the field where excess water or water stress
reduce soybean yield.
A study was conducted for a 12–ha field in Linn County, Iowa, using soybean data collected during two years (1996 and
1998). Yield, soil type, topography, and soil characterization data were used to estimate spatial variations in soil drainage
factors (saturated hydraulic conductivity of an impeding layer and tile drainage spacing), water availability (SCS curve
number and maximum rooting depth), and a soil fertility factor. A procedure was developed to create a database of predicted
yields for combinations of coefficients, and to search the database using rules based on soil classification, drainage class,
and topography to guide the parameter estimation process. When rules based on drainage class were used, the
CROPGRO–Soybean model explained 45% to 70% of the yield variability for 1996 and 1998, respectively. When rules based
on soil water availability, drainage characteristics, and topography were used, good predictions were obtained in both years
(r2 = 0.70 for 1996 and 0.80 for 1998), and RMSE was 2.8% of grid level yields. The data base approach required less than
half the time that simulated annealing required for the field with 48 grids.
TL;DR: In this paper, a theoretical example based on sugar cane production in a 77 ha watershed located in the southeastern part of Brazil is presented. But the theoretical results expressed in terms of economic feasibility or advantages for crop production are controversial, the basic concepts of precision agriculture applied to other inputs such as time and labor, have theoretical advantages and may have a greater possibility to develop into profitable technology.
Abstract: The variability of most soil properties is expressed at small scales. Agronomic practices and treatments are usually designed to be extremely uniform at this scale. The contradiction of applying uniform treatments to variable conditions is the key issue of precision agriculture. Traditionally, precision agriculture means variable application of material inputs such as fertilizers, pesticides and amendments. Although the practical results expressed in terms of economic feasibility or advantages for crop production are controversial, the basic concepts of precision agriculture applied to other inputs, such as time and labor, have theoretical advantages and may have a greater possibility to develop into profitable technology. This paper describes a theoretical example based on sugar cane production in a 77 ha watershed located in the southeastern part of Brazil. Precision agriculture concepts focusing on P-fertilizer inputs were compared with optimization of mechanical operations such as planting and harvesting. Measurable advantages for precision agriculture compared with traditional treatments were observed for mechanical operation efficiency but not for P-fertilizer. Recent practical experiences in Brazilian sugar cane production of treating soil conservation as spatially variable to gain efficiency in mechanical operations is a clear example of the economic feasibility of implementing precision agriculture by increasing efficiency instead of decreasing materials or increasing yields.
TL;DR: In this paper, the variability of yield and quality of irrigated cotton within and across three growing seasons was evaluated on a 5.3 ha irrigated field at the Erskine Research Farm at Texas Tech University, Lubbock, TX.
Abstract: Precision agriculture technologies offer an opportunity to vary production inputs within a field. Variable rate application offers the potential to increase production efficiency and minimize potential adverse environmental effects of agricultural chemicals. As an initial step in the development of precision agriculture technologies for cotton, studies are needed to document variability of cotton. The primary objective of this study was to document variability of yield and quality of irrigated cotton within and across three growing seasons. This study was conducted on a 5.3 ha irrigated field located at the Erskine Research Farm at Texas Tech University, Lubbock, TX. The crop was grown under a conventional tillage system with a 1.0 m row spacing. With the exception of sample collection, the field was managed traditionally with respect to production inputs. A grid system (57 points) was established on 30.5 m (approximately 0.1 ha) intervals. Production of fruiting sites, fruit retention, lint yield, fiber ...
TL;DR: In this paper, the authors used spatial regression analysis of yield monitor data to estimate the site-specific crop Nitrogen (N) response needed to fine tune variable rate fertilizer strategies in Argentine maize and soybean growing areas.
Abstract: SUMMARY: Adapting variable rate technology (VRT) to Argentine conditions requires methods that use inexpensive information and that focus on the inputs and variability common to Argentine maize and soybean growing areas. The goal of this study is to determine if spatial regression analysis of yield monitor data can be used to estimate the site-specific crop Nitrogen (N) response needed to fine tune variable rate fertilizer strategies. N has been chosen as the focus of this study because it is the most commonly used fertilizer by corn farmers in Argentina. The methodology uses yield monitor data from on-farm trials to estimate site-specific crop response functions. The design involves a strip trial with a uniform N rate along the strip and a randomized complete block design, with regression estimation of N response curves by landscape position. Spatial autocorrelation and spatial heterogeneity are taken into account using a spatial error model and a groupwise heteroskedasticity model. A partial budget is used to calculate uniform rate and VRT returns. First year data indicate that N response differs significantly by landscape position, and that VRA for N may be modestly profitable on some locations depending on the VRT fee level, compared to a uniform rate of urea of 80kg ha -1 . A more complete analysis will pool data over many farms and several years to determine if reliable differences exist in N response by landscape position or other type of management zone. The study is planned for four years. The purpose of this preliminary analysis is to show how spatial regression analysis of yield data could be used to fine tune input use.
TL;DR: In this paper, an extended Kalman filter (EKF) based tracking algorithm was used to track the grid from image to image, thus improving precision and robustness to weeds and missing crop plants.
TL;DR: In this paper, a 30.4-ha dryland field was divided into two conventional and two conservation minimum tillage strips and used for crop tillage, and ground observations (plant populations, height, yield) were made at 29 sites within the field and a yield monitor was used to record yields at harvest for grain sorghum and corn.
Abstract: Airborne remote sensing is becoming increasingly useful for mapping plant growth and yield variations in
precision agriculture, but operational methodologies are not well developed nor tested. The main objective of this study was
to integrate airborne multispectral imagery, ground observations, global positioning systems (GPS), geographic information
systems (GIS), image processing, and yield monitoring for mapping spatial variations in plant growth and yield. A 30.4–ha
dryland field that was divided into two conventional tillage strips and two conservation minimum tillage strips was the study
site. The field was planted to cotton (Gossypium hirsutum L.) in 1996, to grain sorghum (Sorghum vulgare Moench) in 1997,
and to corn (Zea mays L.) in 1998. Airborne color–infrared (CIR) digital imagery was acquired from the field on three dates
in each growing season and ground observations (plant populations, height, yield) were made at 29 sites within the field. A
yield monitor was also used to record yields at harvest for grain sorghum and corn. These images and the ground
measurements were integrated within a GIS to document, interpret, and map within–season and across–season plant growth
and yield variability. The images clearly revealed plant growth patterns within and across the three growing seasons as well
as differences between the two tillage systems. Yields of cotton, grain sorghum, and corn were related to the image data for
the three spectral bands and four vegetation indices (two band ratios and two normalized differences) extracted at the
29 sampling sites. Regression equations for yield as a function of a spectral band or a vegetation index for each crop were
developed and the best equations with R 2 values of 0.57, 0.59, and 0.76 for cotton, grain sorghum, and corn, respectively,
were used to estimate the yields for each of the approximately 30,000 pixels. The yield maps generated from the image data
based on the regression equations corresponded closely with yield monitor data maps. Recommended operational procedures
are summarized. This study illustrates practical ways to integrate airborne digital imagery with spatial information
technology and ground observations to map plant growth conditions and yield variations within crop fields.
TL;DR: This research suggests that producer-owned equipment can be used to compare treatments, but the accuracy and subsequent power of such comparisons are likely to be low.
Abstract: The adoption of precision technologies that spatially register measurements using global positioning systems (GPS) greatly facilitates conducting large-scale on-farm research by farmers. On-farm experiments that utilize producer equipment include variations in agronomic practices that occur in situations where we want to predict the effect of inputs on yield. The domain of inference for such on-farm studies therefore more closely matches that desired by researchers. To investigate the feasibility of on-farm research using GPS, a study was conducted to evaluate the potential benefit of site-specific weed management. The study utilized producer-maintained field-scale equipment on four Montana farms in dryland spring wheat production. Paired site-specific and whole-field herbicide treatment areas were established in 0.9 to 1.9-ha blocks using consultant weed maps and a geographic information system (GIS). Yield was unaffected by herbicide treatment strategy (site-specific or broadcast). Minimal dete...
TL;DR: In this paper, the authors used information gathered automatically during the major soil tillage operation, ploughing, is used to improve the spatial resolution of sampled topsoil clay content.
TL;DR: Geographic information system technology was used to extend the uniform potassium recommendation system into a system for mapping spatially variable potassium requirement that takes account of crop demand and soil available potassium and showed that the decision support system performed satisfactorily.
Abstract: The intensely weathered nature of Western Australian cropping soils and the long history of potassium depletion by the farming system has resulted in increased incidence of potassium deficiency in wheat. There is currently no scientifically based method for potassium recommendation in Western Australia. This paper describes the use of site-specific plot-scale field trials carried out in 1995–98 and a crop response model to develop a generally applicable potassium recommendation system. Geographic information system technology was used to extend the uniform potassium recommendation system into a system for mapping spatially variable potassium requirement that takes account of crop demand and soil available potassium. The field trials were carried out on a range of soil types and showed that wheat response to potassium can be described by the Mitscherlich equation. The size of the response was dependent on the soil test value for plant available potassium and the yield of the crop. The latter is mainly dependent on rainfall in the water-limited Mediterranean environment of Western Australia. The relationships between the maximum achievable yield, crop response and soil available potassium values were quantified in order to allow the decision support system to be developed for uniform whole-paddock fertiliser recommendation. Both soil available potassium and yield are very spatially variable in Western Australia and for wheat, the coefficient of variation of yield within the paddock is often of the order of 30%. Soil property variation can be of a similar order. Maps of soil available potassium values and of spatially variable target yield determined either from (i) farmer’s estimate, (ii) yield monitors and (iii) remotely sensed normalised difference vegetation index measurements allow this decision system to map spatially variable potassium requirement. Comparison of the map of potassium requirement with measured spatially variable response to potassium showed that the decision support system performed satisfactorily.
TL;DR: A field-level geographic information system (FIS) designed specifically for research in precision agriculture has been under development at Kansas State University for several years as mentioned in this paper, and two methods for delineating management zones.
Abstract: A field–level geographic information system (FIS) designed specifically for research in precision agriculture has
been under development at Kansas State University for several years. This article summarizes the analytical functions
provided by FIS and gives two examples to illustrate its applications in precision agriculture. The first example studies yield
response to soil electric conductivity using mathematical/logic query and simple statistics functions. The second example
demonstrates two methods for delineating management zones. The first method uses the buffer function of FIS to form
morphological opening and closing filters. The second method is based on spectral analysis of the grid maps. Low–pass filters
in the frequency domain and query functions are used to delineate the management zones. The management zones derived
using the two methods were similar.
TL;DR: In this article, a quantitative analysis of patterns visible in high-resolution NDVI images obtained from airborne remote sensing is presented and applied to a single field in the Netherlands, monitored at four different days during one growing season.
TL;DR: In this paper, the consequences of three management scenarios (no lime, single-rate liming and site-specific lime applications to acidic field soil) were assessed in terms of production and economic risks.
Abstract: Precision agriculture (PA) offers the potential to improve the efficiency and environmental impact of conventional crop production systems However, its implementation will depend on perceptions of how the adoption of technology will increase their yields and profit, and lower their production risk This article presents an approach to help with this type of decision making In this instance the consequences of three management scenarios (no lime, single-rate liming and site-specific lime applications to acidic field soil) were assessed in terms of production and economic risks The methodology involved modelling the uncertainty about wheat yield, accounting for the local uncertainties about soil pH and lime requirement, and the uncertainties about crop model parameters used in the simulations Indicator kriging (IK) was used together with Latin hypercube sampling (LHS) of the probability distributions of variables and model parameters for the propagation of uncertainties through to the output yield and net profit maps These maps, together with a sensitivity analysis, were used to aid with decision making Comparison of the three scenarios showed that, under the economic conditions of the analysis, the optimum was reached for a single-rate application of 35 Mg/ha over the entire field instead of site-specific lime applications Copyright © 2001 John Wiley & Sons, Ltd
01 Jan 2001
TL;DR: In this paper, the authors describe tools and procedures used to structure and implement an automatic data acquisition and control mobile laboratory network for crop production systems in the Brazilian Center-West Region.
Abstract: This paper describes all tools and procedures used to structure and implement an
automatic data acquisition and control mobile laboratory network for crop production systems
data management and spatial variability studies in the Brazilian Center-West Region. It was set
up with base in remote sensing and microprocessor techniques, microelectronic devices,
sensors, controls, sampling systems, data loggers, portable microcomputers, automatic data
acquisition, RF communications link, and the global positioning system (GPS), allowing for the
acquisition of spatially related data during tilling, planting, and harvesting. Physical chemistries
and biological characteristics of the continuous soil-water-plant-atmosphere were monitored
along of the crop growth period, at field conditions, seeking the rationalization of the agricultural
inputs use and the performance improvement of agricultural systems. Some results are
presented.
TL;DR: Results of these case studies suggest that N can be managed site-specifically using a variety of management practices, including soil sampling, variable yield goals, and cropping history.
Abstract: Soil texture varies significantly within many agricultural fields. The physical properties of soil, such as soil texture, have a direct effect on water holding capacity, cation exchange capacity, crop yield, production capability, and nitrogen (N) loss variations within a field. In short, mobile nutrients are used, lost, and stored differently as soil textures vary. A uniform application of N to varying soils results in a wide range of N availability to the crop. N applied in excess of crop usage results in a waste of the grower’s input expense, a potential negative effect on the environment, and in some crops a reduction of crop quality, yield, and harvestability. Inadequate N levels represent a lost opportunity for crop yield and profit. The global positioning system (GPS)-referenced mapping of bulk soil electrical conductivity (EC) has been shown to serve as an effective proxy for soil texture and other soil properties. Soils with a high clay content conduct more electricity than coarser textured soils, which results in higher EC values. This paper will describe the EC mapping process and provide case studies of site-specific N applications based on EC maps. Results of these case studies suggest that N can be managed site-specifically using a variety of management practices, including soil sampling, variable yield goals, and cropping history.
TL;DR: In this paper, the authors used information gathered automatically during the major soil tillage operation, ploughing, is used to improve the spatial resolution of sampled topsoil clay content.