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The world we live in is an increasingly spatial and temporal data-rich environment, and agriculture is no exception. However, data needs to be processed in order to first get information and then make informed management decisions. The concepts of ‘Precision Agriculture’ and ‘Smart Agriculture’ are and will be fully effective when methods and tools are available to practitioners to support this transformation. An open-source software called GeoFIS has been designed with this objective. It was designed to cover the whole process from spatial data to spatial information and decision support. The purpose of this paper is to evaluate the abilities of GeoFIS along with its embedded algorithms to address the main features required by farmers, advisors, or spatial analysts when dealing with precision agriculture data. Three case studies are investigated in the paper: (i) mapping of the spatial variability in the data; (ii) evaluation and cross-comparison of the opportunity for site-specific management in multiple fields; and (iii) delineation of within-field zones for variable-rate applications when these latter are considered opportune. These case studies were applied to three contrasting crop types, banana, wheat and vineyards. These were chosen to highlight the diversity of applications and data characteristics that might be handled with GeoFIS. For each case-study, up-to-date algorithms arising from research studies and implemented in GeoFIS were used to process these precision agriculture data. Areas for future development and possible relations with existing geographic information systems (GIS) software is also discussed.
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... Thus, these ancillary data are assumed to present the same spatial pattern even in a different year. Ancillary data were interpolated by ordinary kriging using GeoFIS (Leroux et al., 2018) (Fig. 1B). ...
... field scale), thus WaLIS will be used in a spatialization process (Pasquel et al., 2022). The ER, TC and NDVI data were used for the realization of within-field zones via a segmentation algorithm (Pedroso et al., 2010) included in the GeoFIS software (Leroux et al., 2018) with the aim to define a base grid for the analyses. All three types of ancillary data were considered as potential surrogate to explain ΨPD spatial variability. ...
Conference Paper
Most current crop models are point-based models, i.e. they simulate agronomic variables at the spatial footprint on which they were initially designed (e.g. plant, field, region scale). Spatialization (i.e. using point-based crop models on a different scale than its native spatial footprint) represents a solution to use these crop models on a different scale. This is particularly interesting in a precision agriculture context where downscaling processes are involved to model agronomic variables on finer scale (e.g. within-field scale). To assess their performances, many indicators based on the comparison of estimated vs observed data, can be used. However, the use of classical, aspatial indicators may not be relevant to evaluate spatialized crop model performances. The objective of this work was to compare how different model performance indicators are able to evaluate the performance of a spatialized crop model at various within-field scales. The crop model spatialization processes were based on a spatial calibration of model parameters. This work focused on a case study using the crop model WaLIS (Water baLance for Intercropped Systems) to simulate vine water restriction (estimated through the predawn leaf water potential - ΨPD) for a vineyard in the South of France. The WaLIS model was employed at different spatial scales (field, site, within-field zone) to generate ΨPD maps. The management zones were generated from soil and vine ancillary data that are correlated with or directly influence vine water stress. Aspatial (RRMSE and D-index) and spatial (Cambardella index and Z-score) indicators were used to evaluate model performances at these different spatial scales. Results showed that these different indicators generated different ‘best’ simulation scales and there was no clear result of model performance from the spatial and aspatial indicators. This confirmed that current approaches to crop model evaluation were not well suited to evaluation the performance of spatialized crop models in a precision agricultural context. Evaluation in an operational context through decision-making evaluation and map comparison approaches provided a clearer understanding of model behavior and appeared to be a relevant method for evaluating downscaled spatialized crop model predictions for tactical, in-season and differential crop management.
... The characterisation of the spatial structure via semivariograms, as well as the realisation of the score maps, was performed using the GeoFis 1.0 software (Open Source Software GeoFIS, http://www.geofis.org, accessed on 24 August 2022) [29]. ...
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Monitoring wine-growing regions and maximising the value of production based on their region/local specificities requires accurate spatial and temporal monitoring. The increasing amount and variability of information from remote sensing data is a potential tool to assess this challenge for the grape and wine industry. This article provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess the value of simultaneously considering spectral and temporal information to highlight site-specific canopy evolution in relation to environmental factors and management practices, which present a large diversity at this regional scale. Parallel Factor Analysis (PARAFAC) was used as an unsupervised technique to recover pure spectra and temporal signatures from multi-way spectral imagery of vineyards in the Languedoc-Roussillon region in the south of France. The model was developed using a time series of Sentinel-2 satellite imagery collected over 4978 vineyard blocks between May 2019 and August 2020. From the Sentinel-2 (spectral and temporal) signal, the PARAFAC analysis allowed the identification of spectral and temporal profiles in the form of pure components, which corresponded to vegetation and soil. The PARAFAC analysis also identified that two of the pure spectra were strongly related to characteristics and dynamics of vineyard cultivation at a regional scale. A conceptual framework was proposed in order to simultaneously consider both vegetation and soil profiles and to summarise the mass of data accordingly. This methodology allowed the computation of a concentration index that characterised how close a field was to a vegetation or a soil profile over the season. The concentration indices were validated for the vegetation and the soil over two growing seasons (2019 and 2020) with geostatistical analysis. A non-random distribution of the concentration index at the regional scale was assumed to highlight a strongly spatially organised phenomenon related to spatially organised environmental factors (soil, climate, training system, etc.). In a second step, spatial patterns of indices were subjected to the expertise of a panel of advisors of the wine industry in order to validate them in relation to vine-growing conditions. Results showed that the introduction of the PARAFAC method opened up the possibility to identify relevant spectro-temporal profiles for vine monitoring purposes.
... These maps usually are produced using some form of spatial interpolation. Ordinary kriging (OK) and inverse distance weighting (IDW) are the spatial interpolation methods most commonly found in precision agriculture (PA) softwares (Leroux et al., 2018;Michelon et al., 2019;Whelan et al., 2002). There are a few reasons for this popularity. ...
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This study aimed to evaluate the use of multiple covariates in robust geostatistical modeling of soil chemical properties characterized by the presence of outliers. Different spatial prediction methods were compared using data from two agricultural areas located in Brazil´s Southeast: one with rotational grazing and one cultivated with sugarcane. Considering the variable-rate fertilizer prescription in the context of precision agriculture, the use of multiple covariates for the prediction of four chemical soil properties (phosphorus (P), potassium (K), cation exchange capacity (CEC) and base saturation (V)) was evaluated. The covariates data set was divided into five categories representing soil, vegetation, relief, management of the area and geographic. Five methods were used: inverse distance weighting (IDW), robust multiple linear regression (RMLR), robust ordinary kriging (ROK), robust universal kriging with spatial co-ordinates in the trend (RUKcoord) and robust universal kriging with environmental and management covariates in the trend (RUKcovars). The model based on the mean was used as a null reference. In general, the use of covariates in robust prediction methods improves the accuracy of spatial prediction of soil properties in the presence of outliers. However this effect was not observed in all situations, depending on the dataset characteristics and the spatial variability of the fields. The management practices are important information for modeling the trend in digital soil mapping for fertilizer prescription purposes. RMLR produces prediction results that are, at least, equivalent to that of robust geoestatistics.
... Maps were obtained using point kriging interpolation. Kriging was performed with the GeoFis 1.0 software [29], which was used for: (1) the modelling of semivariograms and calculations of their featured parameters, C 0 (nugget effect), C 1 (sill) and r (range), and (2) the kriging interpolation. The latter was performed on a grid of regularly spaced points 1000 m apart within the geographical boundary of the LR region. ...
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Unexpected climatic conditions or extreme climatic events in vineyards are a worldwide problem that requires accurate spatial and temporal monitoring. Satellite-based remote sensing is an important source of data to assess this challenge in a climate-change context. This paper provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess heatwave impacts in vineyards at a regional scale. Multi-way partial least squares (N-PLS) regression was used as a supervised technique to predict the intensity of damage caused to vineyards by the heatwave phenomenon that impacted the vineyards in the south of France in 2019. The model was developed based on available ground truth data of yield losses for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The model showed a performance accuracy (R2) of 0.56 in the calibration set and of 0.66 in the validation set, with a standard error of cross-validation in the calibration set of 12.4% and a standard error of the prediction of yield losses in the validation set of 10.7. The model was applied at a regional scale on 4978 vineyard blocks to predict yield losses using spectral and temporal attributes. The prediction of the yield loss due to heat stress at a regional scale was related to the spatial pattern of maximum temperatures recorded during the extreme weather event. This relation was confirmed by a chi-square test (p < 5%). The introduction of N-PLS insights into the analysis enables the characterisation of heat stress responses in vineyards and the identification of spectro-temporal profiles relevant for understanding the effects of heatwaves on vine blocks at a regional scale.
... To determine blocks that would be most appropriate for site-specific management, the Technical Opportunity Index (TOi) (Tisseyre and McBratney, 2008) was used to rank them. It was computed for each block with the GeoFIS freeware (Leroux et al. 2018). The TOi ranks were used to meet two objectives: 1) to identify blocks that could justify zone-specific management and 2) to target blocks to collect additional data from. ...
Chapter
This chapter presents case studies that focus on canopy sensing using proximal and unmanned aerial vehicle (UAV)-mounted optical sensors, rather than satellite-based optical sensing applications. The potential use of optical canopy sensing for crop quality and quantity is explored across four varied case studies. The case studies have been chosen to represent a diversity of crops, countries and stages of sensor development and translation (from emerging research to near commercial applications). In each case study, optical sensing is shown to be relevant to assessing productivity, either directly or through an indicator of crop health. It represents a powerful tool for crop management; however, across all the case studies, the optical sensing solution could only be used directly to address local issues. A clear message is that the suitability and adaptability of this technology to a variety of end-uses in cropping systems depends on local calibration and interpretation. The need for these is a limitation to technology adoption despite the widespread potential applications of optical sensors.
... Current, accurate, and detailed land cover information is vital for land managers and policy makers responsible for developing and implementing conservation strategies and policies (Kitalika et al., 2018). Despite the need for quality land cover information, mapping of large-area, especially high spatial-resolution land cover mapping, is a difficult task for a variety of reasons including large data volumes (Köhl et al., 2006), complexity of developing training and validation datasets (Köhl et al., 2006), data availability (Koldunov and Cristini, 2018), heterogeneity in data (Leroux et al., 2018) and landscape conditions (Medjahed et al., 2016). The challenges of analytical workflow of large data for landcover mapping are arguably addressed by few dedicated software programs that intend to develop precision LULC while incorporating expert knowledge into the process (Bartłomiej et al., 2017;Köhl et al., 2006). ...
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Freely available satellite imagery and geospatial data sets and rapid advancement in data analysis capabilities provide immense opportunities to understand and solve the real-world environmental problems. Open-source platforms such as Google Earth Engine (GEE) provide a planetary-scale environmental science data and analyses capability at much greater efficiency and accuracy than the traditional workflows. We evaluated the GEE Python API utility for classifying the freely available NAIP aerial imagery of 2017 to derive the land use land cover (LULC) information of a Panhandle area of Florida, USA. We identified eight major LULC classes with an overall accuracy of 86% and Kappa value of 79%. We completed all remote sensing data analyses procedures including data retrieval, classification, and report preparation in the Jupyter notebook, an open-source web application. Computation time for the procedure was less than 15 min. The open-source nature of GEE Python API and its library of remote sensing data could benefit remote sensing projects throughout the world, especially where access to commercial image processing software packages and remote sensing data is limited.
... For example, open-source software such as the statistical software R [21] is used for data analysis and GeoFIS as a decision-support tool for precision agriculture data [22]. Open-source hardware examples are found on the notion of precision agriculture and SmartFarm by integrating open-source technologies such as smart sensors, recording devices, and drones [23][24][25][26]. ...
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Three-dimensional (3D) printing in soil science is relatively rare but offers promising directions for research. Having 3D-printed soil samples will help academics and researchers conduct experiments in a reproducible and participatory research network and gain a better understanding of the studied soil parameters. One of the most important challenges in utilizing 3D printing techniques for soil modeling is the manufacturing of a soil structure. Until now, the most widespread method for printing porous soil structures is based on scanning a real sample via X-ray tomography. The aim of this paper is to design a porous soil structure based on mathematical models rather than on samples themselves. This can allow soil scientists to design and parameterize their samples according to their desired experiments. An open-source toolchain is developed using a Lua script, in the IceSL slicer, with graphical user interface to enable researchers to create and configure their digital soil models, called monoliths, without using meshing algorithms or STL files which reduce the resolution of the model. Examples of monoliths are 3D-printed in polylactic acid using fused filament fabrication technology with a layer thickness of 0.20, 0.12, and 0.08 mm. The images generated from the digital model slicing are analyzed using open-source ImageJ software to obtain information about internal geometrical shape, porosity, tortuosity, grain size distribution, and hydraulic conductivities. The results show that the developed script enables designing reproducible numerical models that imitate soil structures with defined pore and grain sizes in a range between coarse sand (from 1 mm diameter) to fine gravel (up to 12 mm diameter).
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The site-specific management is the technology that considers the natural variability within the same field of factors related to crop growth to improve its management practices such that the agricultural treatments are varied for field's small production zones saving resources and environment, and improving crop quality and size. Since site-specific decisions are not far from the Fourth Industrial Revolution and the concept of processes automation, this work addresses improving the process of spatial variability analysis and thus supporting management decisions by developing a system—entitled EGYPADS—based on the Internet of Things and its enabling technologies. EGYPADS automates data collection, zones delineation according to their land suitability evaluation, and maps generation. The paper addresses a case study of potato crop in a specific area in Egypt, El-Salhia, in which eighty-five sites were chosen as main dataset for the modeling process during different stages of crop growth. Three management zones were recognized of the selected field based on the differentiation in their land suitability characteristics, representing about 5%, 65%, and 30% of the total area, respectively. The structure, screens, and services of EGYPADS are described in this paper. EGYPADS offered services include: management zones delineation using absolute and virtual coordinates, Land Suitability Assessment (LSA), data entry from field in real-time as well as from excel sheets, saving maps in suitable format for variable rate application, real-time and historical data processing, centralized management, and flexible formulation of events and related actions. The implementation of EGYPADS was verified. The system dynamically produces non-contiguous isobands, each representing a specific range of parameter values, and can be properly exported for use by other programs or smart machinery. It was proven that EGYPADS supports more than one land with different geometry, area, location, and number of nodes. EGYPADS was compared with the traditional LSA method, and was found to produce similar management zones.
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A GIS and remote sensing-based decision support tool called SAMZ-Desert was developed for management zones (MZs) delineation of a total of 6852 fields in the Imperial County region of southern California using Landsat-8 NDVI data acquired on 27/4 2018. In addition, a total number of 11 cloud-free images in 2018–2020 were statistically analyzed to determine the extent of within-field NDVI variability and temporal stability of MZs at the regional level. A majority (approx. 37%) of the fields had four zones as an optimum number of zones in the region, which could explain>85% of the within-field NDVI variance. Around 13% (n = 873) of the fields in the region were strongly spatially-clustered in at least half the Landsat-8 cloud-free scenes and can benefit from variable rate technologies. Our results suggest that dynamic zoning over time might be necessary for most of these fields. SAMZ-Desert can be accessed from the Haghverdi Water Management Group website: http://www.ucrwater.com/software-and-tools.html.
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Thesis
Crowdsourcing is an approach consisting in answering a question defined by an organisation (research laboratory, company, etc.) by relying on the collective intelligence of a community of contributors. To date, crowdsourcing is not widely spread in agriculture, but it has great potential for collecting georeferenced observations to monitor phenomena at regional scale (e.g. diseases, pests or abiotic stresses monitoring). These crowdsourcing projects in agriculture have specificities in terms of participants (professional contributors, importance of the role of advisors), studied phenomena (with strong spatial and temporal covariances) and datasets collected (asynchronous and heterotopic) that have led some authors to coin the concept of farmsourcing to describe them. These specificities of farmsourcing projects influence the design of the projects and the involvement of the different stakeholders. They also influence the criteria and indicators for evaluating the success of such projects. Finally, they influence the methods for identifying outliers and surprising observations in corresponding datasets. To date, there is no existing approach taking into account the specificities of farmsourcing projects. The objective of this thesis is to propose tools and methods to develop a farmsourcing approach in both the design and the evaluation of the project (How to foster the contribution of participants? How to evaluate the success of a project?) and then in the characterisation of the quality of the resulting observations (How to identify outliers and surprising observations? How can these approaches be automated?) The thesis is based on a systemic approach with the implementation of a case study. This case study is the monitoring of the vine water status at regional scale using i) an indicator (iG-Apex) based on observations of vine shoot growth and ii) the development of a dedicated farmsourcing application (ApeX-Vigne). Firstly, the work demonstrated the value of a simple but noisy approach, such as the one based iG-Apex, for characterising an agronomic variable of interest (in this case, the vine water status) at the field and intra-field levels in a decision support context. They demonstrated how an approach like this could be used to promote participation in farmsourcing projects. The work carried out explored the technological and methodological choices for designing and deploying on a large scale a mobile application promoting the gathering of georeferenced farmsourcing observations. It also proposed a simple approach based on the study of spatial structure to assess the capacity of these projects to provide relevant information at the regional scale. Finally, the work carried out explored an approach for automatically identifying outliers and surprising observations in farmsourcing datasets. This approach is based on density-based clustering methods taking into account spatial, temporal and attribute characteristics of observations. In the coming years, this work should enable the development of farmsourcing tools and projects giving access to new sources of information for decision support at different spatial scales.
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Yield maps are recognized as a valuable tool with regard to managing upcoming crop production but can contain a large amount of defective data that might result in misleading decisions. These anomalies must be removed before further processing to ensure the quality of future decisions. This paper proposes a new holistic methodology to filter out defective observations likely to be present in yield datasets. The notion of spatial neighbourhood has been refined to embrace the specific characteristics of such on-the-go vehicle based datasets. Observations are compared with their newly-defined spatial neighbourhood and the most abnormal ones are classified as defective observations based on a density-based clustering algorithm. The approach was conceived to be as non-parametric and automated as far as possible to pre-process a growing number of datasets without supervision. The proposed approach showed promising results on real yield datasets with the detection of well-known sources of errors such as filling and emptying times, speed changes and non-fully used cutting bar.
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In the spatial variability management of fields, the approach based on management zones (MZs) divides the area into sub-regions, which have spatially homogeneous topography and soil conditions. Such MZs should lead to the same potential yields. Farmers understand which areas of a field have high and low yields, and use of this knowledge may allow the identification of MZs in a field based on production history. The objective of the present study was to evaluate the application of farmer's experience to determine MZs. The study was conducted in three agricultural fields located in the west of the Paraná State in Brazil, and the MZs were generated considering three cases: a) without the use of the farmer’s experience variable; b) with the variable of farmer’s experience and the stable soil properties selected at the variable selection stage; and c) only with the farmer’s experience variable. The generated MZs were evaluated using the Variance Reduction (VR) index, Fuzziness Performance Index (FPI), Modified Partition Entropy (MPE), Smooth Index (SI), and Analysis of Variance (ANOVA). The study showed that the use of farmer’s experience to set MZs could be an efficient and simple tool, that it could reduce costs for the processes of setting MZs, compared to the traditional method of using stable soil variables and relief.
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Management zones can be defined as homogeneous regions for which specific management decisions are to be considered. The delineation of these management units is important because it enables or at least facilitate growers and practitioners performing site specific management. The delineation of management zones has essentially been performed by (i) clustering techniques or (ii) segmentation algorithms arising from the image processing domain. However, the first approach does not take into account the spatial relationships in the data, and is prone to generate a large number of fragmented zones while he second methodology has only been dedicated to regularly-spaced, within-field data. This work proposes a new approach to generate contiguous management zones from irregularly-spaced within-field observations, e.g. within-field yield, soil conductivity, soil samples, which are a very important source of data in precision agriculture studies. A seeded region growing and merging algorithm has been specifically designed for these irregularly-spaced observations. More specifically, a Voronoi tessellation was implemented to define spatial relationships between neighbouring observations. Seeds were automatically placed at specific locations across the fields and management zones were first expanded from these seeds. The merging procedure aimed at generating more manageable and interpretable zones. The merging algorithm was defined in a way that made it possible to incorporate machinery and technical management constraints. Experiments demonstrated that the proposed methodology was able to generate relatively compact and contiguous management zones. Furthermore, machinery and technical constraints were shown to significantly influence the results of the delineation which proved the importance of accounting for these considerations.
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Precision agriculture for banana crops has been little investigated so far. The main difficulty to implement precision agriculture methods lies in the asynchronicity of this crop: after a few cycles, each plant has its own development stage in the field. Indeed, maps of agronomical interest are difficult to produce from plant responses without implementing new methods. The present study explores the feasibility to derive a spatially relevant indicator from the date of flowering and the date of maturity (time to harvest). The time between these dates (TFM) may give insight in spatial distribution of vigor. The study was carried out using production data from 2015 acquired in a farm from Cameroon. Data from individual plants that flowered at different weeks were gathered so as to increase the density of TFM sampling. The temporal variability of TFM, which is induced by weather and operational constraints, was compensated by centering TFM data on their medians (TFMc). The mapping of TFMc was obtained using a classical kriging method. Spatial structures highlighted by TFMc either at the farm level or at the plot level, suggest that such maps could be used to support agronomic decisions.
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Conference Paper
By providing high spatial resolution images of vine fields througout the vine growing season, UAVs could provide useful information, different than those normally considered in the literature. This study aimed at identifying i) relevant information that can be observed from UAV images by two kind of stakeholders : growers and advisers (G&A) ii) the most suitable periods for this observation iii) and the added value this information can have for both G&A daily tasks. This approach has been conducted on an 11.3 ha commercial vineyard representative of the south of France vineyards. UAV-based visible images (2.5 cm resolution) have been acquired in commercial conditions every two weeks from budburst to harvest. Images have been presented to two groups of G&A six times during the growing season. Every expert gathering was conducted with i) an individual period where images were presented to each expert ii) a collective period where G&As were invited to share information. Application of this methodology demonstrated that much information about the vines, the soil and the vineyard environment can be extracted from UAV-based visible images without any image processing. Results showed that this information have added value all along the growing cycle of the vine, particularly for advisers.
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Knowledge and monitoring of the grapevine phenology during the season are important requirements for characterization of productive regions, climate change studies and planning of various production activities at the vine field scale. This work aims at studying the spatial variability of grapevine phenology at the within field scale. It was conducted on two fields, one of cv Cabernet Sauvignon of 1.56 ha and the other of cv Chardonnay of 1.66 ha, both located in Maule Valley, Chile. Within each vine field, a regular sampling grid was designed, to carry out weekly measurements of phenology and maturation. The main results show that there is a significant spatial variability in the phenological development and maturation at the within field scale for both fields. This variability is spatially organised and temporally stable from the beginning of the season (post-budburst) to harvest and over the years. A cluster analysis allowed us to define two clearly contrasted zones in terms of phenology and maturation in both fields, explained by the microclimate. The magnitude of difference between zones varied from 4 to 9 days depending on phenological stages and from 5 to 43 days for maturation. These differences are similar and comparable to that observed at larger scales or under scenarios of climate change. These results highlight the necessity to better take into account this variability to improve sampling and to base decisions of production activities (spraying, harvest, pruning, etc.) application on more relevant information. Further investigations should determine the environmental factors that determine the observed spatial variability.
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Yield maps are a key component of precision agriculture, due to their usefulness in both development and evaluation of precision management strategies. The value of these yield maps can be compromised by the fact that raw yield maps contain a variety of inherent errors. Researchers have reported that 10 to 50% of the observations in a given field contain significant errors and should be removed. Methods for removing these outliers from raw yield data have not been standardized, although many different filtering techniques have been suggested to address specific error types. We developed a software tool called Yield Editor to simplify the process of applying filtering techniques for yield data outlier detection and removal. Yield Editor includes a map view of the yield data, allowing the user to interactively set, assess the effects of, and refine a number of previously reported automated filtering methods. Additionally, Yield Editor allows manual selection of erroneous points, transects, or regions for investigation and possible deletion. This paper describes the filters implemented in Yield Editor, discusses input, output, and filtering options, and documents availability of the program. Example applications of Yield Editor on five test fields are used to show how the user interacts with the software and to analyze the relative importance of the various filters.
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Crop simulation is useful for characterizing and predicting the crop growth and yield due to abiotic stress and also climate change events at the national and international levels, under different agronomic management options. Crop simulation models will gain wider acceptance if they are robust, accessible and easy to use. To simulate the growth of wheat on a daily basis using inputs of weather, soil, variety and management practices, a web-based application of the crop simulation model ‘Web InfoCrop - Wheat’ was designed and developed at the Indian Agricultural Research Institute. The Web InfoCrop – Wheat model was developed using Visual Studio Express, SQL Server, NET framework 4.0 and hosted at http://InfoCrop.iari.res.in. This web-based model has separate modules for input variables, management conditions, and result outputs. Crop model users after registration without any payment, have the right to insert, edit or update and delete data within their private domains while the system manager alone has the administrative right to add data to the public domain, this will ensure authentic data to be available in the public domain. This is done mainly to ensure the availability of factual data to the public and also to maintain the privacy of the user data. The users can run the model to simulate the wheat crop growth and yield, at a single day interval or as defined. This web-based model performed well under different irrigation and nitrogen management practices for the observed and predicted yield and biomass with significant R2 (0.958 and 0.947 respectively) and RMSE (0.054 and 1.318, respectively). Thus, the ‘Web InfoCrop – Wheat’ crop model provides an innovative and efficient approach to the evolving crop simulation model to be used as a decision support tool in the agricultural production system.
Conference Paper
Yield maps provide important information for developing and evaluating precision management strategies. The high-quality yield maps needed for decision-making require screening raw yield monitor datasets for errors and removing them before maps are made. To facilitate this process, we developed the Yield Editor interactive software which has been widely used by producers, consultants and researchers. Some of the most difficult and time consuming issues involved in cleaning yield maps include determination of combine delay times, and the removal of “overlapped” data, especially near end rows. Our new Yield Editor 2.0 automates these and other tasks, significantly increasing the reliability and reducing the difficulty of creating accurate yield maps. This paper describes this new software, with emphasis on the Automated Yield Cleaning Expert (AYCE) module. Application of Yield Editor 2.0 is illustrated through comparison of automated AYCE cleaning to the interactive approach available in Yield Editor 1.x. On a test set of fifty grain yield maps, AYCE cleaning was not significantly different than interactive cleaning by an expert user when examining field mean yield, yield standard deviation, and number of yield observations remaining after cleaning. Yield Editor 2.0 provides greatly improved efficiency and equivalent accuracy compared to the interactive methods available in Yield Editor 1.x.