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Veris Technologies Inc

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Salina, KS, United States
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SAN FRANCISCO--(BUSINESS WIRE)--Today, The Climate Corporation, a subsidiary of Monsanto Company, announced the integration of imagery from innovative aerial imagery partners: Ceres Imaging, TerrAvion and Agribotix, that will deliver valuable, high-resolution imagery to farmers through The Climate Corporation’s industry-leading Climate FieldViewTM digital agriculture platform. The addition of these partners supports Climate’s commitment to deliver a true digital ag ecosystem where farmers can access a broad and interconnected set of tools, services and data in a single, easy-to-use interface to optimize all of their farm management decisions. “There have been tremendous advancements in innovative, aerial imagery technologies that are helping farmers and agriculture providers more precisely monitor crop health and performance at scale through increased image resolution and frequency,” said Mark Young, chief technology officer for The Climate Corporation. “As aerial imagery becomes of greater interest to farmers as a valuable tool to more efficiently manage their operations, we’re thrilled to collaborate with these companies to equip more farmers with the data-driven insights they need through one, connected digital ag platform.” Climate already offers advanced satellite imagery tools to help farmers protect their crops by identifying issues in the field before they impact yield. Innovative aerial imagery technologies can provide farmers imagery at a higher resolution and frequency than satellite imagery, delivering on-demand information that can be used in digital ag tools to help farmers make more informed, data-driven agronomic decisions. With the addition of these new partnerships, aerial imagery data will seamlessly flow into a farmer’s Climate FieldView account from their imagery provider account, giving them the ability to access and visualize all of their data in one place alongside other important field data layers, including planting and yield data, to unlock new insights about field performance. Farmers can also experience deeper analysis of how their crops are performing in-season, identify potential yield-limiting factors and take action early to protect yield, through a unified platform. Ceres Imaging, TerrAvion and Agribotix each provide unique offerings and capabilities: Ceres Imaging: Based in Oakland, Calif., Ceres is an aerial spectral imagery and analytics company delivering university-validated agronomic insights to farmers. Using proprietary cameras, analytics and ground-truthed crop models, Ceres delivers high-resolution spectral imagery as a service that helps farmers in the United States and Australia improve yields and reduce costs. Learn more at www.ceresimaging.net. TerrAvion: Based in San Leandro, Calif., TerrAvion is one of the largest providers of operational imagery to agriculture in the United States and Chile. TerrAvion differentiates itself on operational reliability, speed of delivery, price and working through retail agronomy organizations. TerrAvion serves a diverse farmer customer base in California and the Pacific Northwest, the Great Plains, and the Mississippi Delta and Florida. Learn more at www.terravion.com. Agribotix: Based in Boulder, Colo., Agribotix delivers agricultural intelligence to increase yields and profits using drone-enabled technologies. All Agribotix solutions include FarmLens™, a cloud-based data analysis and reporting solution for people using drones in agriculture. Results are available in a matter of hours. Agribotix serves a diverse customer base including farmers, coop’s, agronomy groups, equipment dealers and other agricultural retailers across the United States, Canada, Latin America and more than 50 different countries around the world. Learn more at www.agribotix.com/climate. To develop the Climate FieldView platform’s capability in this area, Climate is activating a commercial pilot with Ceres and TerrAvion in limited regions of the United States to provide high-resolution aerial imagery for the 2017 growing season. Customers interested in the Agribotix solution can visit their website to learn more. Current Climate customers will soon be able to request these new features right from within their Climate FieldView account. Additional regions and partners are expected to be added in the future. In 2016, Climate announced the extension of the Climate FieldView platform, citing Veris Technologies as the company’s first platform partner. Ultimately, this platform strategy unlocks a stronger and quicker path to market for third-party ag innovators, simplifying the complex digital ag landscape for farmers and making it easier for other innovators to bring valuable new technologies to farmers faster. Launched in 2015, the Climate FieldView platform is on more than 100 million acres across the United States, Canada and Brazil, with more than 100,000 U.S. farmers engaging in Climate's digital tools. Backed by the most powerful data science engine and most extensive field research network in the agriculture industry, the Climate FieldView platform delivers customized insights that help farmers make data-driven decisions with confidence to maximize yield potential, improve efficiency and manage risk. As innovation in the digital agriculture space continues to accelerate rapidly around the globe, Climate continues to explore partnership opportunities to provide farmers with the insights they need to improve their productivity. If you are interested in partnering with The Climate Corporation, please visit www.climate.com/partners. The Climate Corporation, a subsidiary of Monsanto Company, aims to help all the world’s farmers sustainably increase their productivity through the use of digital tools. The integrated Climate FieldView™ digital agriculture platform provides farmers with a comprehensive, connected suite of digital tools. Bringing together seamless field data collection, advanced agronomic modeling and local weather monitoring into simple mobile and web software solutions, the Climate FieldView platform gives farmers a deeper understanding of their fields so they can make more informed operating decisions to optimize yields, maximize efficiency and reduce risk. For more information, please visit www.climate.com or follow the company of Twitter @climatecorp. Monsanto is committed to bringing a broad range of solutions to help nourish our growing world. We produce seeds for fruits, vegetables and key crops – such as corn, soybeans, and cotton – that help farmers have better harvests while using water and other important resources more efficiently. We work to find sustainable solutions for soil health, help farmers use data to improve farming practices and conserve natural resources, and provide crop protection products to minimize damage from pests and disease. Through programs and partnerships, we collaborate with farmers, researchers, nonprofit organizations, universities and others to help tackle some of the world’s biggest challenges. To learn more about Monsanto, our commitments and our more than 20,000 dedicated employees, please visit: discover.monsanto.com and monsanto.com. Follow our business on Twitter® at twitter.com/MonsantoCo, on the company blog, Beyond the Rows® at monsantoblog.com or subscribe to our News Release RSS Feed.


A system for measuring soil properties on-the-go using a narrow profile sensor unit is provided on an implement for traversing a field. The sensor unit includes a front disk/coulter arranged to open a slot in the soil, a runner assembly arranged to follow behind the front disk/coulter for sliding contact with the soil in the slot, and a rotating disk/spoked wheel arranged to follow behind the runner assembly to close the slot. The front disk or coulter serves as a first electrode of an electrode array, the runner assembly has second and third electrodes attached thereto, and the rotating disk/spoked wheel serves as a fourth electrode. The electrode array can be used to measure soil electrical conductivity at multiple depths and to measure soil moisture. An optical window and pH sensor can also be incorporated into the runner assembly to measure soil reflectance and soil pH.


Kweon G.,Gyeongsang National University | Maxton C.,Veris Technologies Inc
Biosystems Engineering | Year: 2013

This research was conducted to develop an inexpensive on-the-go optical sensor for soil organic matter (OM) sensing. Diffuse reflectance for 86 soil samples from Kansas and Illinois was measured by a spectrometer in a laboratory. Stepwise multiple linear regression (SMLR) and B-matrix in partial least squares (PLS) were used to determine important wavelengths for soil OM measurement. The wavelengths of 660 and 940nm, identified by both SMLR and PLS, were used for an optical sensor. The developed optical sensor with dual wavelength was evaluated with dry and wet soils in the lab and the relationship between reflectance and OM showed a coefficient of determination (R2) as high as 0.91. Gaps between soil and the sensor window reduced the ability to estimate soil organic matter, thus the sensor window should press firmly against soil. In field tests, all fields gave good results, with RPD (ratio of prediction to deviation=standard deviation/root mean square error) of 2 or greater for OM estimation. In comparison with a NIR spectrophotometer shank unit, the optical sensor showed similar results for OM mapping pattern with coefficient of determination as 0.86. The level of agreement between the two maps was 0.56 for overall accuracy and 0.34 for kappa coefficient. Further field tests need to be implemented to evaluate the soil organic matter estimation with the sensor over different types of soils in a wider set of locations. © 2013 IAgrE.


Kweon G.,Korea University | Lund E.,Veris Technologies Inc | Maxton C.,Veris Technologies Inc
Geoderma | Year: 2013

An on-the-go optical soil sensor with 660nm red and 940nm near-infrared wavelengths with an electrical conductivity (EC) sensing unit were tested to estimate soil organic matter (SOM) and cation-exchange capacity (CEC) on 551ha on 15 fields in 6 U.S. states. For calibration between sensed data and lab-analyzed values, a multivariate linear regression (MLR) with leave-one-out cross validation was performed on fields with more than 10 lab samples and a single variable linear regression was performed on fields with less than 10 samples. From the SOM calibration results, 12 of 15 fields had good results with R2 of 0.80 or higher and RPD (Ratio of Prediction to Deviation=standard deviation / root mean square error of prediction) of 2.33 or greater. For CEC calibrations, six of nine fields had good results with R2 of 0.86 or higher and RPD of 2.78 or greater. The best calibration model was applied to each field and the estimated SOM and CEC maps exhibited strong spatial structure and high correlation to lab-analyzed SOM in all fields. EC and optical data in each field was normalized and combined together by state and tested with MLR. Combining fields in this manner showed good results with R2 of 0.80 or higher and RPD of 2.30 or greater for SOM in four of five states, and combined fields in two of three states showed good correlations to lab data with R2 of 0.86 or higher and RPD of 2.69 or greater for CEC. From these results, SOM and CEC mapping with soil EC and optical sensors seems to be a promising approach. Future research will be implemented to estimate SOM and CEC more precisely by developing a reliable universal calibration model using soil EC, optical data, soil moisture contents and topographic attributes for global areas. © 2012 Elsevier B.V..


PubMed | International Center for Tropical Agriculture, Swedish University of Agricultural Sciences, University of Embu, Ministry of Agriculture and Veris Technologies Inc
Type: Journal Article | Journal: Sensors (Basel, Switzerland) | Year: 2016

Four proximal soil sensors were tested at four smallholder farms in Embu County, Kenya: a portable X-ray fluorescence sensor (PXRF), a mobile phone application for soil color determination by photography, a dual-depth electromagnetic induction (EMI) sensor, and a LED-based soil optical reflectance sensor. Measurements were made at 32-43 locations at each site. Topsoil samples were analyzed for plant-available nutrients (N, P, K, Mg, Ca, S, B, Mn, Zn, Cu, and Fe), pH, total nitrogen (TN) and total carbon (TC), soil texture, cation exchange capacity (CEC), and exchangeable aluminum (Al). Multivariate prediction models of each of the lab-analyzed soil properties were parameterized for 576 sensor-variable combinations. Prediction models for K, N, Ca and S, B, Zn, Mn, Fe, TC, Al, and CEC met the setup criteria for functional, robust, and accurate models. The PXRF sensor was the sensor most often included in successful models. We concluded that the combination of a PXRF and a portable soil reflectance sensor is a promising combination of handheld soil sensors for the development of in situ soil assessments as a field-based alternative or complement to laboratory measurements.


Igne B.,Iowa State University | Reeves III J.B.,Hydrology and Remote Sensing Laboratory | McCarty G.,Hydrology and Remote Sensing Laboratory | Hively W.D.,Hydrology and Remote Sensing Laboratory | And 2 more authors.
Journal of Near Infrared Spectroscopy | Year: 2010

Soil testing requires the analysis of large numbers of samples in the laboratory that is often time consuming and expensive. Mid-infrared spectroscopy (mid-IRI and near infrared (NIR) spectroscopy are fast, non-destructive and inexpensive analytical methods that have been used for soil analysis, in the laboratory and in the field, to reduce the need for measurements using complex chemical/physical analyses. A comparison of the use of spectral pretreatment as well as the implementation of linear and non-linear regression methods was performed. This study presents an overview of the use of infrared spectroscopy for the prediction of five physical [sand, silt and clayl and chemical - (total carbon and total nitrogen) soil parameters with near and mid-infrared units in bench top and field set-ups. Even though no significant differences existed among pretreatment methods, models using second derivatives performed better. The implementation of partial least squares IPLS], least squares support vector machines (LS-SVM) and locally weighted regression ILWRI for the development of the calibration models showed that the LS-SVM did not out-perform linear methods for most components while LWR that creates simpler models performed well. The present results tend to show that soil models are quite sensitive to the complexity of the model. The ability of LWR to select only the appropriate samples did help in the development of robust models. Results also proved that field units performed as well as bench-top instruments. This was true for both near infrared and mid-infrared technology. Finally, analysis of field moist samples was not as satisfactory as using dried-ground samples regardless of the chemometrics methods applied. © 2010 IM Publications LLP All right reserved.


Kweon G.,Veris Technologies Inc
Agronomy Journal | Year: 2012

Many studies have found the range of spatial dependence is shorter than the distances used in most grid sampling, and government soil surveys have limited utility in many precision applications. A new multi-sensor platform was developed that records apparent soil electrical conductivity (ECa), optical reflectance readings with red and near-infrared light-emitting diodes and pH along with topography data. The objective of this study was to evaluate its performance for estimation of soil cation exchange capacity (CEC), organic matter (OM), and pH on eight fields in four states, comparing results with lab-analyzed samples, USDA soil surveys, and 1-ha grid maps. Proximal soil sensor measurements correlated well with lab-analyzed soil samples. The OM calibrations using ECa, optical, and/or topographic data showed good performance with R2 of 0.8 or higher and ratio of prediction to deviation (RPD) of 2.5 or greater in all fields. In CEC calibrations, five of six fields had higher than 0.86 for R2 and greater than 2.8 for RPD. The pH calibration results showed RPD of 2.1 or greater and R2 of 0.76 or higher in seven fields. The sensor maps showed small-scale variability not detected at conventional grid sample scales or with USDA soil surveys. Using the proximal soil sensors, the average root mean square error of prediction for OM was 2.78 g kg-1, CEC 1.20 cmolc kg-1, and pH 0.26 for the project fields. These values are significantly lower than the soil property ranges found in the soil surveys. This is a promising development for improving farm practices and management. © 2012 by the American Society of Agronomy.


A delineation procedure for site-specific productivity zones was developed with a fuzzy logic system using soil properties obtained from on-the-go electrical conductivity (EC) and organic matter (OM) sensors and topographic attributes. EC, OM, slope and curvature were used as input variables, and productivity was set as an output variable. The fuzzy rules were developed with grower's knowledge for typical central Kansas upland fields; areas within the field having high OM, low EC and low slope have the highest productivity potential, and areas within the field with low OM, high EC and high slope have the lowest productivity potential. The fuzzy logic system performed properly and generated productivity as designed by the fuzzy logic and inference scheme. To validate the system, an adjacent field with 5 years of wheat yield data was selected. The spatial agreement between productivity and yield showed as high as 0.57 and 0.35 for overall accuracy and kappa coefficient. The level of agreement is promising, considering there were many other yield-limiting factors such as precipitation, temperature and management effects. From comparison of the productivity map with the map generated by a fuzzy c-means clustering algorithm (FCM map), agreement between the productivity and yield exhibited generally higher in overall accuracy and Kappa coefficient than the agreement between FCM map and yield. Results of this study can benefit producers and consultants who utilise site-specific management by delineating productivity zones using EC, OM, slope and curvature from the on-the-go sensors. © 2012 IAgrE.


Grant
Agency: Department of Agriculture | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 89.69K | Year: 2010

Crop growers in the United States, and in developed countries worldwide, apply large amounts of phosphate fertilizers to their fields. In the US for example, growers annually apply over 4,000,000 tons of phosphate fertilizer. Fertilizer rates are typically applied in excess of the crop need, since the cost of fertilizer is low relative to the loss in crop yield if a given nutrient is yield-limiting. Over-application of fertilizers can be harmful in some situations. Phosphorus increases may cause algae blooms in fresh-water ecosystems. An excess of P in the soil also can increase the risk of P runoff and leaching. Soils that are P-deficient, due to poor management or inaccurate sampling and application methods, can experience yield reductions. The economic and environmental concerns of both extremes demonstrate the importance of keeping phosphorus levels within a proper range. Phosphorus levels within a field often range from soils that are P-deficient to those are significantly above a sufficiency threshold. In order to deal with spatial variability, a large number of soil measurements must be taken on each field. Using conventional lab analyses, sampling at the scale needed for accurate mapping of phosphorus levels is not feasible. In-field measurements represent an appealing alternative. This project will develop the test equipment required to evaluate sensor technologies in a side-by-side comparison. The sensors to be used in this project have shown initial feasibility to meet the criteria. However, they have not been widely tested using unprocessed soil cores, nor has a thorough side-by-side comparison been conducted. In order to perform this feasibility comparison test, equipment will be devised to collect and process the soil cores, and bring them into contact with the sensors. Results from in-field sensors will be compared with laboratory-analyzed soil tests. The results of this Phase I project will establish a clear direction for the development of an in-field sensing system during a Phase II.


Grant
Agency: Department of Agriculture | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 356.55K | Year: 2010

Nitrogen affects proteins, enzymes and metabolic processes and is essential for crop growth. While annual usage varies based on world economic conditions, approximately 80,000,000 metric tons of fertilizer N are applied annually in the world, of which more than 10,000,000 tons are applied annually in the United States. If crops don?t have an adequate supply of nitrogen, significant yield losses can occur. Consequently, growers typically apply an extra margin of fertilizer as insurance against possible yield reduction from under-application. Excessive nitrogen applications create a number of problems. First, excess nitrogen rates can contaminate water resources. Nitrogen lost from Midwest farm fields is a leading cause of the hypoxia zone in the Gulf of Mexico. Second, wasted nitrogen can cause a significant reduction in profitability, well beyond the cost of the wasted nitrogen. On crops like potatoes and sugar beets, excess nitrogen can cause a reduction in crop quality. Third, there is a negative impact to our atmosphere from applying nitrogen that is not consumed by the crop. Unused nitrogen enters the atmosphere as a potent greenhouse gas. As US farm policy becomes increasingly based on soil and water-quality initiatives, individual farm sustainability may be affected by a farmer?s ability to maintain production levels under closer scrutiny, and even mandates on the amount of fertilizer used. For each of these factors, improvements in managing nitrogen properly will increase the sustainability of their agro-economic production systems. These improvements include accurate assessments of available nitrogen already in the soil. Current approaches employ conventional soil sampling and lab analysis. Sampling depths required for nitrate are relatively deep. As a result, nitrate sampling is laborious, time-consuming, and expensive. Because the samples must be submitted to a testing lab for analysis, the delay in receiving results is a major problem for growers needing to apply fertilizer immediately. As a result of these and other obstacles, many fields that would benefit from improved nitrogen management are not sampled at all, or not sampled with the density required for accurate variable rate prescriptions. The Veris Technologies Automated Soil Measurement System will collect and analyze nitrate and other soil properties to a depth of 24? rapidly, accurately, and economically. The System will perform the measurements automatically, with no action required by the operator. Based on typical zone sampling for nitrate, this system will have a daily capacity of several hundred acres, and will be able to perform these measurements for a competitive price versus conventional sampling and lab analyses. This will offer growers major improvements in fertilizer management, reducing sampling cost, shortening turnaround time for soil test information, increasing precision of site-specific fertilizer applications.

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