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1. Applications for Precision Agriculture: the Italian Experience of SIRIUS ProjectThis paper reports the results of the project SIRIUS (Sustainable Irrigation water management and River-basin... P. Nino, S. Vanino, F. Lupia, F. Altobelli, F. Vuolo, I. Namdarian, C. De michele |
2. Study on Monitoring System of Wheat SowingIn order to real-time monitoring the sowing status of the multi-channel seeder, a distributed monitoring system is developed. The monitoring module of sowing and the monitoring terminal is designed with ... W. Fu, Z. Meng, G. Wu, J. Dong, H. Mei, C. Zhao |
3. Evaluating Spectral Measures Derived From Airborne Multispectral Imagery for Detecting Cotton Root RotCotton root rot, caused by the soilborne fungus Phymatotrichopsis omnivore, is one of the most destructive plant diseases occurring... C. Yang, G.N. Odvody, C.J. Fernandez, J.A. Landivar, R.L. Nichols |
4. Landscape Influences on Soil Nitrogen Supply and Water Holding Capacity for Irrigated Corn... T. Shaver, M. Schmer, S. Irmak, S. Van donk, B. Wienhold, V. Jin, A. Bereuter, D. Francis, D. Rudnick, N. Ward, L. Hendrickson, R. Ferguson, V.I. Adamchuk |
5. The Performance Of Mobile Devices' Inertial Measurement Unit For The Detection Of Cattle's Behaviors On PastureOver the past decade, the Precision Livestock Farming (PLF) concept has taken a considerable place in the development of accurate methods for a better management of farm animals. The recent technological improvements allow the raising of numerous motion sensors such as accelerometers and GPS tracking. Several studies have shown the relevancy of these sensors to distinguish the animals’ behavior using various classification techniques such as neuronal networks or multivariate... A. Andriamandroso, B. Dumont, F. Lebeau, J. Bindelle |
6. Suitability Of Fluorescence Sensors To Estimate The Susceptibility Degree Of Spring Barley To Powdery Mildew And Leaf RustThe overall role of precision agriculture is not restricted to those systems for in-field and in-season sensing of the impact of stresses. Much more, its contribution comprises the prevention of stresses, amongst others by supporting the selection of appropriate and stress-tolerant genotypes in breeding programs. In this context, the development, selection and use of cultivars which are tolerant to pathogens establish an essential tool for a more sustainable and environmental-friendly... G. Leufen, G. Noga, M. Hunsche |
7. Selection Of Fluorescence Indices For The Proximal Sensing Of Single And Multiple Stresses In Sugar BeetThe use of fluorescence indices for sensing the impact of abiotic and biotic stresses in agricultural crops is well documented in the literature. Pigment fluorescence gives a precise picture about the plant physiology and its changes following the occurrence of stresses. In general, alterations in such optical signals is caused either by the stress-induced accumulation of one or more fluorophores, or the degradation of specific molecules like chlorophyll. Unfortunately, many stresses... G. Leufen, G. Noga, M. Hunsche |
8. Exploiting The Variability In Pasture Production On New Zealand Hill Country.New Zealand has about four million hectares in medium to steep hill country pasture to which granular solid fertiliser is applied by airplane. On most New Zealand hill country properties where cultivation is not possible the only means of influencing pasture production yield is through the addition of fertilizers and paddock subdivision to control grazing and pasture growth rates. Pasture response to fertilizer varies in production zones within the farm which can be modelled... M.Q. Grafton, P.J. Mcveagh, R.R. Pullanagari, I.J. Yule |
9. Maize Seeding Rate Optimization in Iowa Using Soil and Topographic Characteristics.The ability to collect soil, topography, and productivity information at spatial scales has become more feasible and more reliable with many advancement in precision technologies. This ability, combined with precision services and the accessibility farmers have to equipment capable implementing precision practices, has led to continued interest in making site-specific crop management decisions. The objective of this research was to utilize soil and topographic parameters to optimize seeding rates... M.A. Licht, A. Lenssen, R. Elmore |
10. In-season Diagnosis of Rice Nitrogen Status Using Crop Circle Active Canopy Sensor and UAV Remote SensingActive crop canopy sensors have been used to non-destructively estimate nitrogen (N) nutrition index (NNI) for in-season site-specific N management. However, it is time-consuming and challenging to carry the hand-held active crop sensors and walk across large paddy fields. Unmanned aerial vehicle (UAV)-based remote sensing is a promising approach to overcoming the limitations of proximal sensing. The objective of this study was to combine unmanned aerial vehicle (UAV)-based remote sensing system... J. Lu, Y. Miao, Y. Huang, W. Shi |
11. Active and Passive Crop Canopy Sensors As Tools for Nitrogen Management in CornThe objectives of this research were to (i) assess the correlation between active and passive crop canopy sensors’ vegetation indices at different corn growth stages and (ii) assess sidedress variable rate nitrogen (N) recommendation accuracy of active and passive sensors compared to the agronomic optimum N rate (AONR). The experiment was conducted near Central City, Nebraska on a Novina sandy loam planted to corn on 15 April 2015. The experiment was a randomized complete-block design with... L. Bastos, R. Ferguson |
12. Comparing Predictive Performance of Near Infrared Spectroscopy at a Field, Regional, National and Continental Scales by Using Spiking and Data Mining TechniquesThe development of accurate visible and near infrared (vis-NIR) spectroscopy calibration models for selected soil properties is a crucial step for variable rate application in precision agriculture. The objective of the present study was to compare the prediction performance of vis-NIR spectroscopy at local, regional, national and continental scales using data mining techniques including spiking. Fresh soil samples collected from farms in the UK, Czech Republic, Germany, Denmark and the Netherlands... S.M. Nawar, A.M. Mouazen, D. George, A. Manfield |
13. Toward Geopolitical-Context-Enabled Interoperability in Precision Agriculture: AgGateway's SPADE, PAIL, WAVE, CART and ADAPTAgGateway is a nonprofit consortium of 240+ businesses working to promote, enable and expand eAgriculture. It provides a non-competitive collaborative environment, transparent funding and governance models, and anti-trust and intellectual property policies that guide and protect members’ contributions and implementations. AgGateway primarily focuses on implementing existing standards and collaborating with other organizations to extend them when necessary. In 2010 AgGateway identified... R. Ferreyra, D.B. Applegate, A.W. Berger, D.T. Berne, B.E. Craker, D.G. Daggett, A. Gowler, R.J. Bullock, S.C. Haringx, C. Hillyer, T. Howatt, B.K. Nef, S.T. Rhea, J.M. Russo, S.T. Nieman, P. Sanders, J.A. Wilson, J.W. Wilson, J.W. Tevis, M.W. Stelford, T.W. Shearouse, E.D. Schultz, L. Reddy |
14. Development of an Airborne Remote Sensing System for Aerial ApplicatorsAn airborne remote sensing system was developed and tested for recording aerial images of field crops, which were analyzed for variations of crop health or pest infestation. The multicomponent system consists of a multi-spectral camera system, a camera control system, and a radiometer for normalizing images. To overcome the difficulties currently associated with correlating imagery data with what is actually occurring on the ground (a process known as ground truthing); a hyperspectral reflectance... Y. Lan, Y. Huang, D.E. Martin, W.C. Hoffmann, B.K. Fritz, J.D. López |
15. An Automatic Control Method Research for 9YG-1.2 Large Round BalerWhen manual or semi-automatic round baler working, the tractor driver have to frequently manual the machine according to the bale process at the same time of driving. The driver easily feel fatigue in this operating mode for a long time, so the consistency of the bale’s density can not be guaranteed. And there may be wrong operation. In this article, we use the model 9YG-1.2 large round baler as a research prototype. We study the information collection and processing of the baler’s... J. Dong, Z. Meng, Y. Cong, A. Zhang, W. Fu, R. Pan, Q. Yang, Y. Shang |
16. Development of Farmland-Terrain Simulation System for Consistency of Seeding DepthA farmland-terrain simulation system suitable for rugged topography was designed to study the irregularities of farmland surface morphology led by both topographic fluctuation and terrain tilt. The system consists of terrain simulation mechanism, hydraulic system, control system, etc. The terrain simulation mechanism is connected to the rack through hydraulic cylinder to simulate farmland surface fluctuation. The hydraulic system controls the hydraulic cylinder to drive the terrain simulation... W. Fu, J. Dong, Y. Cong, N. Gao, Y. Li, Z. Meng |
17. Exploring Relationships Between Dairy Herd Improvement Metrics in Minas Gerais – Brazil Dairy HerdsThe objective of the present study was to apply principal component analysis (PCA) on Brazilian Dairy Herd Improvement (DHI) data to discover the subset of most meaningful variables to describe complete lactations. The Holstein Livestock Breeders Association of Minas Gerais provided data collected between 2005 and 2016 from 122 dairy farms located in the State of Minas Gerais – Brazil. Twelve numerical variables were selected from the original dataset and four additional variables were created.... G.M. Dallago, D. Figueiredo, R. Santos, P. Andrade, D. Santos |
18. Active and Passive Sensor Comparison for Variable Rate Nitrogen Determination and Accuracy in Irrigated CornThe objectives of this research were to (i) compare active and passive crop canopy sensors’ sidedress variable rate nitrogen (VRN) derived from different vegetation indices (VI) and (ii) assess VRN recommendation accuracy of active and passive sensors as compared to the agronomic optimum N rate (AONR) in irrigated corn. This study is comprised of six site-years (SY), conducted in 2015, 2016 and 2017 on different soil types (silt loam, loam and sandy loam) and with a range of preplant-applied... L. Bastos, R.B. Ferguson |
19. Relationships Between First Test Day Metrics of First Lactation Cows to Evaluate Transition PeriodThe objective of this study was to apply principal component analysis (PCA) and multiple correspondence analysis (MCA) on Dairy Herd Improvement (DHI) data of animals on their first lactation to discover the most meaningful set of variables that describe the outcome on the first test day. Data collected over 4 years were obtained from 13 dairy herds located in Québec – Canada. The data set was filtered to contain only information from first test day of animals on their first lactation,... G.M. Dallago, D. Figueiredo, R. Santos, P. Andrade, D.E. Santschi, R. Lacroix, D.M. Lefebvre |
20. Active Canopy Sensor-Based Precision Rice Management Strategy for Improving Grain Yield, Nitrogen and Water UseThe objective of this research was to develop an active crop sensor-based precision rice (Oryza sativa L.) management (PRM) strategy to improve rice yield, N and water use efficiencies and evaluate it against farmer’s rice management in Northeast China. Two field experiments were conducted from 2011 to 2013 in Jiansanjiang, Heilongjiang Province, China, involving four treatments and two varieties (Kongyu 131 and Longjing 21). The results indicated that PRM system significantly increased... J. Lu, H. Wang, Y. Miao |
21. Overview and Value of Digital Technologies for North American Soybean ProducersIn the current state of digital agriculture, many digital technologies and services are offered to assist North American soybean producers. Opportunities for capturing and analyzing information related to soybean production methods are made available through the adoption of these technologies. However, often it is difficult for producers to know which digital tools and services are available to them or understand the value they can provide. The objective of this... J. Lee, J. Fulton, K. Port, R. Colley iii |
22. Investigating Spatial Relationship of Apparent Electrical Conductivity with Turfgrass and Soil Characteristics in Sand-capped Golf Course FairwaysTurfgrass quality decreases when grown on fine textured soils that are irrigated with poor quality water. As a result, sand-capping (i.e., a sand layer above existing native soil) is now considered during golf course fairway renovation and construction. Mapping spatial variability of soil apparent electrical conductivity (ECa) has recently been suggested to have applications for precision turfgrass management (PTM) in native soil fairways, but sand-capped fairways have received less... C. Straw, B. Wyatt, A.P. Smith, K. Watkins, S. Hong, W. Floyd, D. Williams, C. Garza, T. Jansky |
23. 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 Matrice... Z. Lin, W. Guo, N. Gill |
24. 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 |
25. 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 acquire... R. Karn, H. Gu, O. Adedeji, W. Guo |
26. Modeling Spatial and Temporal Variability of Cotton Yield Using DSSAT for Decision Support in Precision AgricultureThe quantification of spatial and temporal variability of cotton yield provides critical information for optimizing resources, especially water. The Southern High Plains (SHP) of Texas is a major cotton (Gossypium hirsutum L.) production region with diminishing water supply. The objective of this study was to predict cotton yield variability using soil properties and topographic attributes. The DSSAT CROPGRO-Cotton model was used to simulate cotton growth, development and yield using... B.P. Ghimire, O. Adedeji, Z. Lin, W. Guo |
27. 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 from... O.I. Adedeji, B.P. Ghimire, H. Gu, R. Karn, Z. Lin, W. Guo |
28. Variability in Yield Response of Maize to N, P and K Fertilization Towards Site-specific Nutrient Recommendations in Two Maize Belts in TogoSavannah and central regions are the major maize production zones in Togo, but with maize grain yields at a threshold of only 1.5 Mg ha-1. We use a participatory approach to assess the importance of the major three macro elements (N, P and K) for maize cropping in the two regions in order to further allow for site-specific and scalable fertilizer recommendations. Thirty farmers’ fields served as pilot sites, allocated within the two regions to account for spatial variability in... J.M. Sogbedji, M. Lare, A.K. Lotsi, K.A. Amouzou, T. Agneroh |
29. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather DataNitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by combining... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia |
30. Within Field Cotton Yield Prediction Using Temporal Satellite Imagery Combined with Deep LearningCrop yield prediction at the field scale plays a pivotal role in enhancing agricultural management, a vital component in addressing global food security challenges. Regional or county-level data, while valuable for broader agricultural planning, often lacks the precision required by farmers for effective and timely field management. The primary obstacle in utilizing satellite imagery to forecast crop yields at the field level lies in its low temporal and spatial resolutions. This study aims to... R. Karn, O. Adedeji, B.P. Ghimire, A. Abdalla, V. Sheng, G. Ritchie, W. Guo |
31. Enhancing Nutrient-related Stress Detection: High Throughput Phenotyping and Image Analysis for Improved PrecisionIn the 21-century agriculture has the unique responsibility to provide food, fuel, fiber and feed for the growing population under the stress of climate change and diminishing natural resources. A feat that will take considerable change to the sustainability of such practices. One of which is the idea of assessing phenotypic expression of complex traits in response to environmental factors. This idea elevates the use of phenotyping to quantitatively monitor stress manifestation. Therefore,... K.J. Bathke, Y. Ge, S.D. Choudhury, J.D. Luck |
32. Estimating Water and Nitrogen Deficiency in Corn Using a Multi-parameter Proximal SensorThe Crop Circle Phenom (CCP) is an innovative integrated proximal sensor that can be potentially used to perform in-season diagnosis of nitrogen and water status. In addition to measuring spectral reflectance in several bands including the red, red edge, and near-infrared wavelengths, the CCP can also measure canopy and air temperatures and provides several parameters that can be associated with chlorophyll content, crop vigor, and water status. These capabilities differentiate the CCP from other... L. Lacerda, Y. Miao, V. Sharma, A. E. flores, A. Kechchour, J. Lu |
33. In-season Diagnosis of Corn Nitrogen and Water Status Using UAV Multispectral and Thermal Remote SensingFor irrigated corn fields, how to optimize nitrogen (N) and irrigation simultaneously is a great challenge. A promising strategy is to use remote sensing to diagnose corn N and water status during the growing season, which can then be used to guide in-season variable rate N application and irrigation management. The objective of this study was to evaluate the effectiveness of UAV multispectral and thermal remote sensing in simultaneous diagnosis of corn N and water status. Two field experiments... Y. Miao, A. Kechchour, V. Sharma, A. Flores, L. Lacerda, K. Mizuta, J. Lu, Y. Huang |
34. Fertigation Management Strategies Effect on Residual Nitrates in the Soil Profile and Ground WaterNitrogen is an input that is vital for growth and productivity within the corn belt states of the U.S. However, when nitrogen as an input into agricultural cropping systems is often over-applied and thus not optimally utilized by the cropping system. Therefore, it is at risk of loss within the environment through processes of leaching, denitrification, and volatilization. This is a major concern in Nebraska, as the reality is that much of the state’s groundwater has been contaminated with... K.J. Bathke, T. Cross, J.D. Luck |
35. Sensor Based Fertigation ManagementSensor-based fertigation management (SBFM) is a relatively new technology for directing nitrogen (N) decisions, specifically tailored for delivery of N via center pivot irrigation systems (fertigation). The development of SBFM began in 2018 at the University of Nebraska-Lincoln with the help of cooperating producers across the state. Over two dozen field sites provided testbeds for the development and evaluation of the technology. The key technique in this fertigation approach is the... J. Stansell, J.D. Luck, T. Cross, K.J. Bathke, T. Smith |
36. Evaluating Different Strategies to Analyze On-farm Precision Nitrogen Trial DataOn-farm trials are being conducted by more and more researchers and farmers. On-farm trials are very different to traditional small plot experiments due to the existence of significant within-field variability in soil-landscape conditions. Traditional statistical techniques like analysis of variance (ANOVA) are commonly adopted for on-farm trial analysis to evaluate overall performance of different treatments, assuming uniform environmental and management factors within a field. As a result, the... K. Mizuta, Y. Miao, J. Lu, R.P. Negrini |
37. Predicting Soil Cation Exchange Capacity from Satellite Imagery Using Random Forest ModelsCrop yield variability is often attributed to spatial variation in soil properties. Remote sensing offers a practical approach to capture soil surface properties over large areas, enabling the development of detailed soil maps. This study aimed to predict cation exchange capacity (CEC), a key indicator of soil quality, in the agricultural fields of the Lower Mississippi Alluvial Valley using digital soil mapping techniques. A total of 15,586 soil samples were collected from agricultural fields... I. Muller, J. Czarnecki, M. Li, B.K. Smith |
38. In-Season Nitrogen Management: Leveraging Data Visualization for Precision AgricultureThe agricultural sector nitrogen management-related research has been extensively high by experiencing a data revolution, with an increasing influx of information from diverse sources like sensors, satellites, and Unmanned Aerial Vehicles (UAVs) imaging technologies. In this context, effective in-season nitrogen data management has become a critical factor; however, the ability of farmers to visualize the impact of such technologies in field research settings has been limited. This project... C. Narayana, S. vanderplas, K.J. Bathke, J.D. Luck |
39. Assessing Precision Water Management in Cotton Using Unmanned Aerial Systems and Satellite Remote SensingThe goal of this study was to improve agricultural sustainability and water use efficiency by allocating the right amount of water at the right place and time within the field. The objectives were to assess the effect of variable rate irrigation (VRI) on cotton growth and yield and evaluate the application of satellites and Unmanned aerial systems (UAS) in capturing the spatial and temporal patterns of cotton growth response to irrigation. Irrigation treatments with six replications of three different... O. Adedeji, W. Guo, H. Alwaseela, B. Ghimire, E. Wieber, R. Karn |
40. Simulating Climate Change Impacts on Cotton Yield in the Texas High PlainsCrop yield prediction enables stakeholders to plan farming practices and marketing. Crop models can predict crop yield based on cropping system and practices, soil, and other environmental factors. These models are being used for decision support in agriculture in a variety of ways. Cultivar selection, water and nutrient input optimization, planting date selection, climate change analysis and yield prediction are some of the promising area of applications of the models in field level farm management.... B. Ghimire, R. Karn, O. Adedeji, G. Ritchie, W. Guo |
41. Predicting Within-field Cotton Yield Variability Using DSSAT for Decision Support in Precision AgricultureThe quantification of spatial and temporal variability of cotton (Gossypium hirsutum L.) yield provides critical information for optimizing resources, especially water, in the Southern High Plains (SHP), Texas, with a diminishing water supply. The within-field yield variation is mostly influenced by the properties of soil and their interaction with water and nutrients. The objective of this study was to predict within-field cotton yield variability using a crop growth model... B. Ghimire, R. Karn, O. Adedeji, W. Guo |
42. On-farm Evaluation of a Satellite Remote Sensing-based Precision Nitrogen Management StrategyImproper management of nitrogen (N) fertilizers in the cropping systems of the U.S. Midwest has resulted in significant N leaching into the Mississippi River Basin that flows to the Gulf of Mexico. The majority of the U.S. Midwest states need to develop a plan for a nutrient loss reduction strategy to decrease N and phosphorous loadings into waters and the Gulf of Mexico by 45% by 2050. In Minnesota, high nitrate concentration and loads have not been significantly reduced in surface and ground... J. Lu, Y. Miao, C.J. Ransom, F. Fernández |
43. Evaluating the Impact of Irrigation Rate, Timing, and Maturity-based Cotton Cultivars on Yield and Fiber Quality in West TexasIn West Texas, effective irrigation is crucial for sustainable cotton production given the water scarcity from the declining Ogallala aquifer and erratic rainfall patterns. A three-year study (2020-2022) investigated irrigation rate and timing effects on early to mid-season cotton maturity groups. Five treatments, including rainfed (W1 or LLL) and variations in irrigation rates at growth stages (P1 to P4), were applied. Evaluation involved six to seven cotton cultivars from four maturity groups,... O. Adedeji, R. Karn, B.P. Ghimire, W. Guo, E.N. Wieber |