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1. Maturity Grape Indicators Obtained By Means Of Earth Observation TechniquesWine producers often need to buy grapes from growers. A good selection of grapes allows obtaining the desired wine quality. This paper presents a procedure to obtain by means of earth observation techniques indices and parameters used in the Spanish vineyards to monitor the state of the grapes. In this way is possible to monitor the ripeness of the grapes or the best time to harvest in such a way that growers can get the highest quality grapes, while producers of wine can select the most appr... J. Sanz, A. Romo, J.L. Casanova, S. Fraile |
2. Beyond NDVI - Additional Benefits of RapidEye Image Products... U. Schulthess, K. Schelling |
3. Spectral Models for Estimation of Chlorophyll Content, Nitrogen, Moisture Stress and Growth of Wheat CropField experiments were conducted during 2009-10 and 2010-11 at research farm of the department of Farm Machinery and Power Engineering, Punjab Agricultural university, Ludhiana. Three w... B.S. Sekhon, J. Mukherjee, A. Sharma, S.K. Thind, R. Kaur, M.S. Makkar |
4. The Map - Supported by New NPK-Sensors - is Intelligent, Not the TractorDI Walter H. Mayer PROGIS Software GmbH Postgasse 6, A-9500 Villach www.progis.com office@progis.com +43 4242 26332 WinGIS®-AGROffice® and BING®-maps: Since years PROGIS has been developing an object oriented GIS (WinGIS®), agriculture and forestry applications for single enterprises, for advisors, for the chain management including logistics and communication implementation with mobile GIS (mobG... W. Mayer |
5. Exploiting the Dmc Satellite Constellation for Applications in Precision AgricultureThis paper presents the unique capabilities of the DMC constellation of optical sensors, and examples of how a number of organisations around the world are exploiting this powerful data source for applications in precision farming. The DMC consists of five satellites built in the UK by Surrey Satellite Technology Ltd, each carrying a wide swath (650km) optical sensor. It is an international programme of satellite ownership and groundstations, with joint campaigns being coordinated c... P. Stephens, S. Mackin, G. Holmes |
6. Microbial Contaminants in Cocoa Powder Samples in South – West NigeriaCocoa powder (CP), which is the major ingredient of cocoa-based beverages, is obtained from cocoa cake in a process involving hydraulic pressing of cocoa butter from fermented and roasted cocoa beans. Cocoa powder is presently being consumed as a health drink because of the presence of flavonoids in it. Evidences have shown that cocoa flavonoids exert powerful antioxidant properties by boosting immune responses and also the presence of procyanidins in cocoa protects the body against free-radi... A.A. Oluwadun, O.0. Olubamiwa, O.C. Jayeola |
7. Potential of Visible and Near Infrared Spectroscopy for Prediction of Paddy Soil Physical PropertiesA fast and convenient soil analytical technique is needed for soil quality assessment and precision soil management. The main objective of this study was to evaluate the ability of Visible (Vis) and Near-infrared Reflectance Spectroscopy (NIRS) to predict paddy soil physical properties in a typical Malaysian paddy field. To assess the utility of spectroscopy for soil physical characteristics prediction, we used 118 soil samples for laboratory analysis and optical measurement in the Vis-NIR re... A. Gholizadeh, M. Saberioon, M. Mohd soom |
8. Can Active Sensor Based NDVI Consistently Classify Wheat Genotypes?ABSTRACT ... M.A. Naser, R. khosla, S. Haley, R. Reich, L. Longchamps, M. Moragues, G.W. buchleiter, G.S. Mcmaster |
9. Variation in Nitrogen Use Efficiency for Multiple Wheat Genotypes across Dryland and Irrigated Cropping SystemsABSTRACT ... M.A. Naser, R. Khosla, R. Reich, S. Haley, L. longchamps, M. Moragues, G.W. buchleiter, G.S. Mcmaster |
10. Automatic Remote Image Processing For Agriculture Uses Through Specific SoftwareAbstract ... D. Gómez-candón, J.J. Caballero-novella, J.M. Peña-barragán, M. Jurado-expósito, F. López-granados, L. Garcia-torres, A.I. Decastro |
11. Position Error of Input Prescription Map Delineated From Remote ImagesThe spatial variability of biotic fact... D. Gómez-candón, J.J. Caballero-novella, J.M. Peña-barragán, M. Jurado-expósito, L. Garcia-torres, F. López-granados, A.I. Decastro |
12. Comparing Sensing Platforms for Crop Remote SensingRemote sensing offers the possibility to obtain a rapid and non-destructive diagnosis of crop health status. This gives the opportunity to apply variable rates of fertilizers to meet the actual crop needs at every locations of the field. However, the commonly used normalized difference vegetation index (ND... R. Khosla, L. Longchamps |
13. Estimation of Soil Moisture from RADARSAT-2 Multi-Polarized SAR Data over Wheat FieldsGuijun Yanga... G. Yang |
14. Precision Agriculture Initiative for Karnataka – A New Direction for Strengthening Farming CommunityStrengthening agriculture is crucial to meet the myriad challenges of rural poverty, food security, unemployment, and sustainability of natural resources and it also needs strengthening at technical, financial and management levels. In this c... U.K. Shanwad, M.B. Patil, V. H, M. B.g , P. R, R. N.l. , S. S, R. Khosla, V.C. Patil |
15. Bayesian Methods for Predicting LAI and Soil MoistureCrop models describe the growth and development of a crop interacting with soil, climate, and managemen... M. Majdi, D. Benjamin, D. Marie-france |
16. Estimation of Rice Yield from MODIS Data in West Java, IndonesiaChiharu Hongo1*, Takaaki Furukawa1, Gunardi Sigit2, Masayasu Maki3, Koki Honma3,... C. Hongo, T. Furukawa, G. Sigit, M. Maki, K. Honma, K. Yoshida, K. Oki, H. Shirakawa |
17. Ground Level Hyperspectral Imagery For Weeds Detection In Wheat FieldsWeeds are a severe pest in agriculture resulting in extensive yield loss. Applying precise weed control has economical as well as environmental benefits. Combining remote sensing tools and techniques with the concept of precision agriculture has the potential to automatically locate and identify weeds in order to allow precise control. The objective of the current work is to detect ... D.J. Bonfil, U. Shapira, A. Karnieli, I. Herrmann, S. Kinast |
18. Estimation of Leaf Nitrogen Concentration in Barley with In Situ Hyperspectral MeasurementsLeaf nitrogen concentration (LNC), a good indicator of nitrogen status in crop, is of special significance to diagnose nutrient stress and guide nitrogen fertilization in fields. Due to its non-destructive and quick advantages, hyperspectral remote sensing plays a unique r... J.M. Wang, C.M. Li, X.M. Yang, W.M. Huang, H.M. Yang, X.M. Xu |
19. Applications for Precision Agriculture: the Italian Experience of SIRIUS ProjectThis paper reports the results of the project SIRIUS (Sustainable Irrigation water management and River-ba... P. Nino, S. Vanino, F. Lupia, F. Altobelli, F. Vuolo, I. Namdarian, C. De michele |
20. Appropriate Wavelengths for Winter Wheat Growth Status Based On Multi-Spectral Crop Reflectance DataOne of the applications of remote sensing in agriculture is to obtain crop status for estimation and management of variable rate of inputs in the crop production. In order to select the appropriate wavelengths relat... I. Han-ya, K. Ishii, N. Noguchi, V. Rasooli sharabian |
21. Assessment of Land Use Changes in Dirab Region of Saudi Arabia Using Remotely Sensed ImageriesA thorough knowledge of land use changes is important for planning and management activities of land resources. Moreover, it is considered ... K.A. Al-gaadi |
22. Remote NIR-Sensor Fusion with Weather Data for Improved Prediction of Wheat Yield ModelsPrediction models for grain yield based on remote sensing data are commonly shown to perform reasonably well for one single cropping season. The model performances often drop, however, when data from more years is included. This may be caused by biased data, resulting from diverging growth conditions from year to year, which a... T. Isaksson, A. Korsaeth, S. Øvergaard |
23. Soil Resource Appraisal towards Land use Planning Using Satellite Remote Sensing and GIS – A Case Study in Medak Nala Watershed in Northern Karnataka, IndiaIn precision farming, knowledge of spatial variability in soil properties is important. The soil map shows soil series and phases like stoniness, gravelliness, salinity, sodicity... V.C. Patil, H.H. Gowda, K.A. Reddy, U.K. Shanwad |
24. Remote Sensing Imagery Based Agricultural Land Pattern Extraction around Miyajimanuma WetlandThis research aimed to extract agricultural land use pattern around the Miyajimanuma wetland, Hokkaido, Japan. By combining the image segmentation technology - watershed transform and image classification technology- particle swarm optimization (PSO)-k-means based minimum distance classifier, a new method for extracting the agricultural land use information ... R. Mochizuki, I. Han-ya, N. Noguchi, B. Su, K. Ishii |
25. Estimating Crop Leaf Area Index from Remotely Sensed Data: Scale Effects and Scaling MethodsLeaf area index (LAI) of crop canopies is significant for growth condition monitoring and crop yield estimation, and estimating LAI based on remote sensing observations is the normal way to assess regional crop growth. However, the scale effects of LAI make multi-scale observations harder to be fully and effectively utilized for LAI estimation. A systematical statistical str... Y. Dong , J. Wang , C. Li , G. Yang, X. Song, W. Huang |
26. Developing an Integrated Rice Management System for Improved Yield and Nitrogen Use Efficiency in Northeast China... G. Zhao, Y. Miao, F. Zhang, M. Fan |
27. Enhancing Farmers' Indigenous Knowledge Management in Cassava Varietal Trial Using Agro Ecosystem Analysis, Farmers' Drama Group and Animations in Eastern part of Nigeria.Researchers continue to come up with new varieties but farmer perspectives and preferences are very important factors for new varieties to spread in farmers’ communities. Researcher priorities alone are not enough. A variety may be ‘scientifically pe... C.C. Asiabaka, M.O. Adesope, C.C. Ifeanyi- obi, R.N. Nwakwasi, F. Nnadi, E.C. Matthews- njoku, J. Chikaire |
28. Monitoring Drought Stress Index in Wheat Field of Mongolia Using Remote SensingWater stress during crop growth, even during short periods of a couple of weeks, is a major cause of yield reduction. The complexity in defining the magnitude of such water stress is due to diversity of crops grown in a given location, variability in soil type and conditions, spatial variability of rainfall, delay in timely of agriculture, and diversity in crop management practices. The problem associated with drought ... B.M. Banzragch, N.M. Damdinpurev, E.M. Batzorig |
29. Hyperspectral Imagery for the Detection of Nitrogen Stress in Potato for In-season Management... T.J. Nigon, C. Rosen, D. Mulla, Y. Cohen, V. Alchanatis, R. Rud |
30. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial ImagesPotato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to ev... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud |
31. Comparison of Algorithms for Delineating Management Zones... A.M. Saraiva, R.T. Santos, J.P. Molin |
32. A Low Cost, Modular Robotics Tool Carrier for Precision Agriculture ResearchCurrent research within agricultural crop production focus on using autonomous robot technology to optimize the production efficiency, enhance sustainability and minimize tedious, monotonous and wearing tasks. But progress is slow pa... A. Bøgild, S.H. Nielsen, N.J. Jacobsen, C.L. Jaeger-hansen, R.N. Jørgensen, K. Jensen, O.J. Jørgensen |
33. An Approach to Selection of Soil Water Content Monitoring Locations within FieldsIncreased input efficiency is one of the main challenges for a modern agricultural enterprise. One way to optimize production cycles is to rationalize crop residue utilization. In conditions where there is limited use of mineral fertilizers and without applying manure, plant residues may be used as an organic fertilizer ... V.I. Adamchuk, L. Pan, R.B. Ferguson |
34. Understanding Spatial and Temporal Variability of Wheat Yield: An Integrated System ApproachSpatial variation in soil water and nitrogen are often the causes of crop yield spatial variability due to their influence on the uniformity of plant stand at emergence and for in-season stresses. Natural and acquired variability in production capacity or potential within a field causes uniform agronomic management practices for the field to be correct in some parts and inappropriate in others. To ... B. Basso, C. Fiorentino, D. Cammarano, A. D'errico |
35. The Use of Crop Sensors Beyond Nitrogen and Improving the Right to Farm... C. Mackenzie |
36. Spectral Characterization to Discriminate Grass Weeds from Wheat Crop Using Remote Sensing and GIS for Precision Agriculture and Environmental SustainabilityKaur, Ramanjit, Mahey RK, Mahal JS, Kingra PK and Kaur Pukhraj ... R. Randhawa |
37. Farmers Cooperatives in Georgia as Key Factor for Food Security... K. Nadiradze |
38. John Deere FarmSight™Agriculture has had several revolutions in the past century, and it currently faces what may be its greatest challenge to date – population growth and the increased need for food, fiber, and fuel in the future. To meet this challenge the agricultural industry will have to drive efficiencies to a level never seen before, within a context of several macro trends (e.g., farm sizes increasing, environmental sustainability requirements evolving). John Deere FarmSightTM... M. Stelford |
39. AMMO Ag: Agricultural Marketing & Merchandising OptimizerEHedger provides an integrated risk management solution for farm operations utilizing our proprietary AMMO platform combined with proven hedging strategies, first-hand market insight, effective trade execution and farming expertise. AMMO software enables real-time analysis of crop/livestock production. Farmers can set profit margins, evaluate variable profit scenarios, understand production costs and risks, and create sustainable marketing programs to maximize their... C. Krol, D. Dempsey |
40. Real-Time Fluorescence Sensors for Precision Agriculture... J. Ayral |
41. Raven Sponsor Presentation: Slingshot OverviewSlingshot, a suite of products and services centered around high-speed wireless connectivity in the cab ... D. Schwiesow |
42. Precision Agriculture and SpringerMaryse Walsh will be presenting Precision Agriculture, the Springer journal, but also the discipline and its place in the Springer publications overall. The community attending the ICPA has a major role in ensuring the positive development of these publications and the affiliation of the journal to the ISPA will only help. ... M. Walsh |
43. Raising Awareness of the Potential of Crop Sensing Technologies to Improve Environmental StewardshipExtensive research and on-farm work using active crop sensors for input management have been conducted in the Midwest and Great Plain USA with favorable results. Contrasting is the situation in the Southeast where the adoption by farmers is still limited and current on-going research is focused on the main southeastern crops. This presentation will provide an overview of the multiple extension activities related to crop sensing involving farmers, extension agents and crop consultants in ... B. Ortiz |
44. Making the Most of Precision Ag Data: Big Data in Farm Managementna ... T. Griffin |
45. Davco's Journey Into Precision Sugarcane FarmingDavco's Journey Into Precision Sugarcane Farming ... D. Cox |
46. Sensor Algorithms 101This presentation will break down the algorithms used for Optical Sensor Based Nitrogen rate recommendations. The group will walk through the mechanics and agronomics behind the most commonly used equations, in order to turn the black boxes into slightly muddied waters. ... B. Arnall |
47. Use of Zone or Grid Soil Nutrient Management as Part of an Integrated Site-specific Nutrient StrategyZone and grid sampling are used as a basis for fertilizing with nutrients site-specifically. Use of sensors to assist in-season management of nitrogen is also gaining momentum. The presentation will suggest when grid or zone sampling for preplant nutrients might be utilized and how these recommendations would be used in an integrated approach of preplant plus in-season nutrient management. ... D. Franzen |
48. Application of Drone Data to Assess Damage Intensity of Bacterial Leaf Blight Disease on Rice Crop in IndonesiaThe Government of Indonesia has launched agricultural insurance program since 2016. A key in agricultural insurance is damage assessment which is required to be as precise, quick, quantitative and inexpensive as possible. Current method is to inspect the damage by human eyes of specialist having experiences. This method, however, costs much and is difficult to estimate disease infected fields precisely in wide area. So, there is increasing need to develop effective, simplified and low cost me... C. Hongo, S. Isono, G. Sigit, B. Utoyo, E. Tamura |
49. Evaluation of Image Acquisition Parameters and Data Extraction Methods on Plant Height Estimation with UAS ImageryAerial imagery from unmanned aircraft systems (UASs) has been increasingly used for field phenotyping and precision agriculture. Plant height is one important crop growth parameter that has been estimated from 3D point clouds and digital surface models (DSMs) derived from UAS-based aerial imagery. However, many factors can affect the accuracy of aerial plant height estimation. This study examined the effects of image overlap, pixel resolution, and data extraction methods on estimati... C. Yang, C. Suh, W. Guo, H. Zhao, J. Zhang, R. Eyster |
50. Knowledge-based Approach for Weed Detection Using RGB ImageryA workflow was developed to explore the potential use of Phase One RGB for weed mapping in a herbicide efficacy trial in wheat. Images with spatial resolution of 0.8 mm were collected in July 2020 over an area of nearly 2000 square meters (66 plots). The study site was on a research farm at the University of Saskatchewan, Canada. Wheat was seeded on June 29, 2020, at a rate of 75 seeds per square meter with a row spacing of 30.5 cm. The weed species seeded in the trial were kochia, wild oat, ... T. Ha, K. Aldridge, E. Johnson, S.J. Shirtliffe, S. Ryu |
51. UAV-based Hyperspectral Monitoring of Peach Trees As Affected by Silicon Applications and Water Stress StatusPrevious research has shown that the application of reduced doses of Silicon (Si) improves crop tolerance to water stress, which is common in commercial young peach trees because irrigation is not usually applied during their first two years. In this study, aerial images were used to monitor the impact of different Si and water treatments on the hyperspectral response of peach trees. An experiment with 60 young (under 1 year old) peach trees located at the Musser Fruit Research Center (Seneca... J. Peña, J. Melgar, A. De castro, J. Maja, K. Nascimento-silva |
52. N-management Using Structural Data: UAV-derived Crop Height As an Estimator for Biomass, N Concentration, and N Uptake in Winter WheatIn the last 15 years, sensors mounted on Unmanned Aerial Vehicles (UAVs) have been intensively investigated for crop monitoring. Besides known remote sensing approaches based on multispectral and hyperspectral sensors, photogrammetric methods became very important. Structure for Motion (SfM) and Multiview Stereopsis (MVS) analysis approaches enable the quantitative determination of absolute crop height and crop growth. Since the first paper on UAV-derived crop height was published by Bendig e... G. Bareth, A. Jenal, H. Hüging |
53. Cotton Boll Detection and Yield Estimation Using UAS Lidar Data and RGB ImageCotton boll distribution is a critical phenotypic trait that represents the plant's response to its environment. Accurate quantification of boll distribution provides valuable information for breeding cultivars with high yield and fiber quality. Manual methods for boll mapping are time-consuming and labor-intensive. We evaluated the application of Lidar point cloud and RGB image data in boll detection and distribution and yield estimation. Lidar data was acquired at 15 m using a DJI Matri... Z. Lin, W. Guo, N. Gill |
54. Integration of Unmanned Aerial Systems Images and Yield Monitor in Improving Cotton Yield EstimationThe yield monitor is one of the most adopted precision agriculture technologies because it generates dense yield data to quantify the spatial variability of crop yield as a basis for site-specific management. However, yield monitor data has various errors that prevent proper interpretation and precise field management. The objective of this study was to evaluate the application of unmanned aerial systems (UAS) images in improving cotton yield monitor data. The study was conducted in a dryland... H. Gu, W. Guo |
55. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS ImageryDeep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high re... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal |
56. Evaluation of Unmanned Aerial Vehicle Images in Estimating Cotton Nitrogen ContentEstimating crop nitrogen content is a critical step for optimizing nitrogen fertilizer application. The objective of this study was to evaluate the application of UAV images in estimating cotton (Gossypium hirsutum L.) N content. This study was conducted in a dryland cotton field in Garza County, Texas, in 2020. The experiment was implemented as a randomized complete block design with three N rates of 0, 34, and 67 kg N ha-1. A RedEdge multispectral sensor was used to acqu... R. Karn, H. Gu, O. Adedeji, W. Guo |
57. Establishment of a Canola Emergence Assessment Methodology Using Image-based Plant Count and Ground Cover AnalysisManual assessment of emergence is a time-consuming practice that must occur within a short time-frame of the emergence stage in canola (Brassica napus). Unmanned aerial vehicles (UAV) may allow for a more thorough assessment of canola emergence by covering a wider scope of the field and in a more timely manner than in-person evaluations. This research aims to calibrate the relationship between emerging plant population count and the ground cover. The field trial took place at the Uni... K. Krys, S. Shirtliffe, H. Duddu, T. Ha, A. Attanayake, E. Johnson, E. Andvaag, I. Stavness |
58. Utilization of UASs to Predict Sugarcane Yields in Louisiana Prior to HarvestOne of the most difficult tasks that both sugarcane producers and processors face every year is estimating the yields of sugarcane fields prior to the start of harvest. This information is needed by processors to determine when the harvest season is to be initiated each year and by producers to decide when each field should be harvested. This is particularly important in Louisiana because the end of the harvest season is often affected by freeze events. These events can severely damage the cr... R.M. Johnson, B. Ramachandran |
59. Increasing the Accuracy of UAV-Based Remote Sensing Data for Strawberry Nitrogen and Water Stress DetectionThis paper presents the methods to increase the accuracy of unmanned aerial vehicles (UAV)-based remote sensing data for the determination of plant nitrogen and water stresses with increased accuracy. As the demand for agricultural products is significantly increasing to keep up with the growing population, it is important to investigate methods to reduce the use of water and chemicals for water conservation, reduction in the production cost, and reduction in environmental impact. UAV-based r... S. Bhandari, A. Raheja |
60. Estimation of Cotton Biomass Using Unmanned Aerial Systems and Satellite-based Remote SensingSatellite and unmanned aerial system (UAS) images are effective in monitoring crop growth at various spatial, temporal, and spectral scales. The objective of the study was to estimate cotton biomass at different growth stages using vegetation indices (VIs) derived from UAS and satellite images. This research was conducted in a cotton field in Hale County, Texas, in 2021. Data collected include 54 plant samples at different locations for three dates of the growing season. Multispectral images ... O.I. Adedeji, B.P. Ghimire, H. Gu, R. Karn, Z. Lin, W. Guo |
61. Enhancing Spatial Resolution of Maize Grain Yield DataGrain yield data is frequently used for precision agriculture management purposes and as a parameter for evaluating agronomy experiments, but unexpected challenges sometimes interfere with harvest plans or cause total losses. The spatial detail of modern grain yield monitoring data is also limited by combine header width, which could be nearly 14 m in some crops. Remote sensing data, such as multispectral imagery collected via satellite and unmanned aerial systems (UAS), could be used t... J. Siegfried, R. Khosla, D. Mandal, W. Yilma |
62. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US MidwestEffective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly acr... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan |
63. Assessment of Goss Wilt Disease Severity Using Machine Learning Techniques Coupled with UAV ImageryGoss Wilt has become a common disease in corn fields in North Dakota. It has been one of the most yield-limiting diseases, causing losses of up to 50%. The current method to identify the disease is through visual inspection of the field, which is inefficient, and can be subjective, with misleading results, due to evaluator fatigue. Therefore, developing a reliable, accurate, and automated tool for assessing the severity of Goss's Wilt disease has become a top priority. The use of un... A. Das, P. Flores, Z. Zhang , A. Friskop, J. Mathew |
64. Precision Nitrogen and Water Management for Optimized Sugar Beet Yield and Sugar ContentSugar beet (SB) production profitability is based on maximizing three parameters: beet yield, sucrose content, and sucrose recovery efficiency. Efficient nitrogen (N) and water management are key for successful SB production. Nitrogen deficits in the soil can reduce root and sugar yield. Overapplication of N can reduce sucrose content and increase nitrate impurities which lowers sucrose recovery. Application of N in excess of SB crop need leads to vigorous canopy growth, while compromising ro... O.S. Walsh, S. Shafian |
65. Potential of UAS Multispectral Imagery for Predicting Yield Determining Physiological Parameters of CottonThe use of unmanned aerial systems (UAS) in precision agriculture has increased rapidly due to the availability of reliable, low-cost, and high-resolution sensors as well as advanced image processing software. Lint yield in cotton is the product of three physiological parameters: photosynthetically active radiation intercepted by canopy (IPAR), the efficiency of converting intercepted active radiation to biomass (RUE), and the ratio of economic yield to total dry matter (HI). The relationship... A. Pokhrel, S. Virk, J.L. Snider, G. Vellidis, V. Parkash |
66. Multispectral Assessment of Chickpea in the Northern Great PlainsChickpea is an increasingly important crop in the Montana agricultural system. From 2017 to 2021 the U.S. has planted an average of about 492,000 acres per year with Montana chickpea production accounting for around 44% of the U.S. total (USDA/NASS QuickStats accessed on 2/11/2021). This has led to an increase in breeding efforts for elite varieties adapted to the unique conditions in the Northern Great Plains. Breeding of chickpea often relies on traditional phenotyping techniques that are l... J.M. Vetch |