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1. Evaluation of The Advantages of Using GPS-Based Auto-Guidance on Rolling Terrain Peanut Fields... B.V. Ortiz, G. Vellidis, K. Balkcom, H. Stone, J. Fulton, E. Vansanten |
2. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
3. EZZone - An Online Tool for Delineating Management ZonesManagement zones are a pillar of Precision Agriculture research. Spatial variability is apparent in all fields, and assessing this variability through measurement devices can lead to better management decisions. The use of Geographic Information Systems for agricultural management is common, especially with management zones. Although many algorithms have been produced in research settings, no online software for management zone delineation exists. This research used a common... G. Vellidis, C. Lowrance, S. Fountas, V. Liakos |
4. Precision Nutrient Management System Based on Ion and Crop Growth SensingAutomated sensing and variable-rate supply of nutrients in hydroponic solutions according to the status of crop growth would allow more efficient nutrient management for crop growth in closed systems. The Structure from Motion (SfM) method has risen as a new image sensing method to obtain 3D images of plants that can be used to estimate their growth, such as leaf cover area (LCA), plant height, and fresh weight. In this sense, sensor fusion technology combining ion-selective electrodes (ISEs)... W. Cho, D. Kim, C. Kang, H. Kim, J. Son, S. Chung, J. Jiang, H. Yun |
5. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
6. A Dynamic Variable Rate Irrigation Control SystemCurrently variable rate irrigation (VRI) prescription maps used to apply water differentially to irrigation management zones (IMZs) are static. They are developed once and used thereafter and thus do not respond to environmental variables which affect soil moisture conditions. Our approach for creating dynamic prescription maps is to use soil moisture sensors to estimate the amount of irrigation water needed to return each IMZ to an ideal soil moisture condition. The UGA Smart... G. Vellidis, V. Liakos, W. Porter, X. Liang, M.A. Tucker |
7. On Farm Studies to Determine Seeding Rate in CornSeeding rate (SDR) is one of the most critical production practices impacting productivity and economic return for corn (Zea mays L.) By changing SDRs in different zones within a field, herein termed as site-specific management, better economic results can be produced as the outcome of reducing SDRs in low productivity areas and increasing SDRs under high-yielding environments, relative to the uniform SDR management performed by the producer. The aim of this study was to analyze yield responses... G. Balboa, S. Varela, I. Ciampitti, S. Duncan, T. Maxwell, D. Shoups, A. Sharda |
8. Real-time Gauge Wheel Load Variability on Planter with Downforce Control During Field OperationDownforce control allows planters to maintain gauge wheel load across a range of soil resistance within a field. Downforce control is typically set for a target seed depth and either set to manually or automatically control the gauge wheel load. This technology uses load cells to actively regulate downforce on individual row units by monitoring target load on the gauge wheels. However, no studies have been conducted to evaluate the variability in gauge wheel load observed during planter operation... A. Sharda, S. Badua, D. Flippo, I. Ciampitti, T.W. Griffin |
9. Automated Support Tool for Variable Rate Irrigation PrescriptionsVariable rate irrigation (VRI) enables center pivot management to better meet non-uniform water and fertility needs. This is accomplished through correctly matching system water application with spatial and temporal variability within the field. A computer program was modified to accommodate GIS data layers of grid-based field soil texture properties and fertility needs in making management decisions. The program can automatically develop a variable rate application prescription along the lateral... A.T. Nguyen, A.L. Thompson, K.A. Sudduth, E.D. Vories, A.T. Nguyen |
10. Rationale for and Benefits of a Community for On-Farm Data SharingMost data sets for evaluating crop production practices have too few locations and years to create reliable probabilities from predictive analytical analyses for the success of the practices. Yield monitors on combines have the potential to enable networks of farmers in collaboration with scientists and farm advisors to collect sufficient data for calculation of more reliable guidelines for crop production showing the probabilities that new or existing practices will improve the efficiency of... T. Morris, N. Tremblay, P.M. Kyveryga, D.E. Clay, S. Murrell, I. Ciampitti, L. Thompson, D. Mueller, J. Seger |
11. Three Years of On-Farm Evaluation of Dynamic Variable Rate Irrigation: What Have We Learned?This paper will present a dynamic Variable Rate Irrigation System developed by the University of Georgia. The system consists of the EZZone management zone delineation tool, the UGA Smart Sensor Array (UGA SSA) and an irrigation scheduling decision support tool. An experiment was conducted in 2015, 2016 and 2017 in two different peanut fields to evaluate the performance of using the UGA SSA to dynamically schedule Variable Rate Irrigation (VRI). For comparison reasons strips were designed within... V. Liakos, W. Porter, X. Liang, M. Tucker, A. Mclendon, C. Perry, G. Vellidis |
12. Management Zone Delineation for Irrigation Based on Sentinel-2 Satellite Images and Field PropertiesThis paper presents a case study of the first application of the dynamic Variable Rate Irrigation (VRI) System developed by the University of Georgia to cotton. The system consists of the EZZone management zone software, the University of Georgia Smart Sensor Array (UGA SSA) and an irrigation scheduling decision support tool. An experiment was conducted in 2017 in a cotton field to evaluate the performance of the system in cotton. The field was divided into four parallel strips. All four strips... V. Liakos, G. Vellidis, L. Lacerda, W. Porter, M. Tucker, C. Cox |
13. Opportunities for Precision Agriculture in SerbiaThe aim of this paper is to analyze the factors leading to low adoption rate of precision farming in Serbia and to describe steps being taken by BioSense institute to increase it. The majority of the arable land in Serbia is grown by small family owned and operated farms most of which are in the range of 2 to 5 ha making them highly unsustainable. Only 16% of the arable land is managed by agricultural companies and cooperatives. We believe that the adoption of advanced technologies with the currently... A.C. Tagarakis, F. Van evert, D. Milic, V. Crnojevic, V. Crnojevic-bengin, C. Kempenaar, N. Ljubicic |
14. Influence of Planter Downforce Setting and Ground Speed on Seeding Depth and Plant Spacing Uniformity of CornUniform seed placement improves seed-to-soil contact and requires proper selection of downforce control across varying field conditions. At faster ground speeds, downforce changes and it becomes critical to select the level of planter downforce settings to achieve the desired consistency of seed placement during planting. The objective of this study was to assess the effect of ground speed and downforce setting on seeding depth and plant spacing and to evaluate the relationship of ground speed... A. Sharda, S. Badua, I. Ciampitti, R. Strasser, T.W. Griffin |
15. 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, wild... T. Ha, K. Aldridge, E. Johnson, S.J. Shirtliffe, S. Ryu |
16. 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 University... K. Krys, S. Shirtliffe, H. Duddu, T. Ha, A. Attanayake, E. Johnson, E. Andvaag, I. Stavness |
17. Establishing the First Soil Water Characteristics Curve for the Soils of Prince Edward Island, CanadaSoil water characteristics curve (SWCC), for Prince Edward Island (PEI), is much more needed currently for the sustainable production of agriculture yields. It will not only fulfil the requirements of the province’s farmers for irrigation scheduling but also help the government to decide about permitting the use of groundwater for supplemental irrigation on the island. A soil water characteristics curve in PEI does not exist to support precision agriculture practices. Precision irrigation... S.J. Cheema, A.A. Farooque, F. Abbas, T. Esau, K. Grewal |
18. Analytical and Technological Advancements for Soybean Quality Mapping and Economic DifferentiationIn the past, measuring soybean protein and oil content required the collection of soybean seed samples and laboratory analyses. Modern on-the-go near-infrared (NIR) sensing technologies during the harvest and proximal remote sensing (aerial and satellite imagery) before harvest time can be used to provide an early estimate of seed quality levels, benchmark in-season predictions with at-harvest final seed quality and enable seed differentiation for farmers leading to better marketing strategies. Recent... A. Prestholt, C. Hernandez, I. Ciampitti , P. Kyveryga |
19. 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 relationships... A. Pokhrel, S. Virk, J.L. Snider, G. Vellidis, V. Parkash |
20. Making Irrigator Pro an Adaptive Irrigation Decision Support SystemIrrigator Pro is a public domain irrigation scheduling model developed by the USDA-ARS National Peanut Research Laboratory. The latest version of the model uses either matric potential sensors to estimate the plant’s available soil water or manual data input. In this project, a new algorithm is developed, which will provide growers and consultants with much more flexibility in how they can feed data to the model. The new version will also run with Volumetric Water Content sensors, giving... I. Gallios, G. Vellidis, C. Butts |
21. Developing a neural-network model for detecting Aflatoxin hotspots in peanut fieldsAflatoxin is a carcinogenic toxin produced by a soilborne fungi, called Aspergillus flavus, causing a difficult struggle for the peanut industry in terms of produce quality, price and the range of selling market. This study aims to develop a successful U-Net CNN (Convolutional Neural Network) model, a reliable image segmentation method, that will help in distinguishing high probability zones of occurrence of Aflatoxin in peanut fields using remotely sensed hyperspectral imagery. The research was... S. Kukal, G. Vellidis |
22. Developing a Decision Support Model for Informing N Fertilization in CornAssessing crop nitrogen (N) status is crucial for optimizing the application of N fertilizers in corn. The Critical Nitrogen Dilution Curve (CNDC) stands as a fundamental model supporting diagnostic tool for identifying the corn nitrogen (N) status. However, there is a need for efficient, non-destructive methods to estimate the crop N status. The objective of this study was to evaluate the potential of three handheld sensors: SPAD, LI-600, and Green Seeker to diagnose corn N deficiencies at early... L. Lemes bosche, I. Ciampitti |
23. Algorithm to Estimate Sorghum Grain Number from Panicles Using Images Collected with a Smartphone at Field-scaleAn estimation of on-farm yield before harvest is important to assist farmers on deciding additional input use, time to harvest, and options for end uses of the harvestable product. However, obtaining a rapid assessment of on-farm yield can be challenging, even more for sorghum (Sorghum bicolor L.) crop due to the complexity for accounting for the grain number at field-scale. One alternative to reduce labor is to develop a rapid assessment method employing computer vision and artificial intelligence... G.N. Nocera santiago, P. Cisdeli magalhães, I. Ciampitti, L. Marziotte |
24. Combining Remote Sensing and Machine Learning to Estimate Peanut Photosynthetic ParametersThe environmental conditions in which plants are situated lead to changes in their photosynthetic rate. This alteration can be visualized by pigments (Chlorophyll and Carotenoids), causing changes in plant reflectance. The goal of this study was to evaluate the performance of different Machine Learning (ML) algorithms in estimating fluorescence and foliar pigments in irrigated and rainfed peanut production fields. The experiment was conducted in the southeast of Georgia in the United States in... C. Rossi, S.L. Almeida, M.N. Sysskind, L.A. Moreno, A. Felipe dos santos, L. Lacerda, G. Vellidis, C. Pilcon, T. Orlando costa barboza |
25. Decision Support Tools for Developing Aflatoxin Risk Maps in Peanut FieldsAspergillus flavus and Aspergillus parasiticus hereafter referred to jointly as A. flavus, are soil fungi that infect and contaminate preharvest and postharvest peanuts with the carcinogenic secondary metabolite aflatoxin. A. flavus can cause extensive economic losses to peanut growers and shellers by contaminating peanut kernels with aflatoxins. In the southeastern U.S., contamination from aflatoxin continues to be a major threat to the peanut industry and... G. Vellidis, M. Abney, T. Burlai, J. Fountain, R.C. Kemerait, S. Kukal, L. Lacerda, S. Maktabi, A. Peduzzi, C. Pilcon, M. Sysskind |
26. Assessing Soybean Water Stress Patterns and ENSO Occurrence in Southern Brazil: an in Silico ApproachWater stress (WS) is one of the most important abiotic stresses worldwide, responsible for crop yield penalties and impacting food supply. The frequency and intensity of weather stresses are relevant to delimitating agricultural regions. In addition, El Nino Southern Oscillation (ENSO) has been employed to forecast the occurrence of seasonal WS. Lastly, planting date and cultivar maturity selection are key management strategies for boosting soybean (Glycine max (L.) Merr.) yield... A. Carcedo, L.F. Antunes de almeida, T. Horbe, G. Corassa, L.P. Pott, I. Ciampitti, G.D. Hintz, T. Hefley, R.A. Schwalbert, V. Prasad |
27. Using Remote Sensing to Benchmark Crop Coefficient Curves of Sweet Corn Grown in the Southeastern United StatesIrrigation is responsible for over 75% of global freshwater use, making it the largest consumer of the world’s freshwater resources. With freshwater scarcity increasing worldwide, increased efficient irrigation water use is necessary. Smart irrigation is described as ‘the linking of technology and fundamental knowledge of crop physiology to significantly increase irrigation water use efficiency'. Irrigation scheduling tools such as smartphone applications have become... E. Bedwell, L. Lacerda, T. Mcavoy, B.V. Ortiz, J. Snider, G. Vellidis, Z. Yu |
28. Environmental Characterization for Rainfed Maize Production in the US Great Plains RegionIdentifying regions with similar productivity and yield-limiting climatic factors enables the design of tailored strategies for rainfed maize (Zea mays L.) production in vulnerable environments. Within the United States (US) Great Plains region, rainfed maize production in Kansas is susceptible to weather fluctuations. This study aims to delimit environmental regions with similar crop growth conditions and to identify the main climatic factors limiting rainfed maize yield, using the state... L.N. Lingua, A. Carcedo, V. Gimenez, G. Maddonni, I. Ciampitti |
29. An Open Database of Crop Yield Response to Fertilizer Application for SenegalFood security is one of the major global challenges today. Africa is one of the continents with the largest gaps in terms of challenges for food security. In Senegal, about 60% of the population resides in rural areas and the cropping systems are characterized as a low productivity system, low input and in reduced areas, smallholder subsistence systems. Increasing crop productivity would have a positive impact on food security in this country. One of the main factors limiting crop productivity... F. Gomez, A. Carcedo, A. Diatta, L. Nagarajan, V. Prasad, Z. Stewart, S. Zingore, I. Ciampitti, P. Djighaly |
30. Spatial Predictive Modeling to Quantify Soybean Seed Quality Using Remote Sensing and Machine LearningIn recent years, the advancement of artificial intelligence technologies combined with satellite technology is revolutionized agriculture through the development of algorithms that help producers become more sustainable. This could improve the conditions of farmers not only by maximizing their production and minimizing environmental impact but also due to better economic benefits by allowing them to access high-value-added markets. Furthermore, the use of predictive tools that could improve the... C. Hernandez, P. Kyveryga, A. Correndo, A. Prestholt, I. Ciampitti |
31. Spatio-temporal Variability of Intra-field Productivity Using Remote SensingUnderstanding the spatiotemporal variability in intra-farm productivity is crucial for management in making agronomic decisions. Furthermore, these decision-making processes can be enhanced using spatial data science and remote sensing. This study aims to develop a framework to asses the spatio-temporal variability of intra-farm productivity through historical satellite data and climate data. Historical satellite data and rainfall information from diverse fields across the United States (2016-2022)... E. Van versendaal, C. Hernandez, P. Kyveryga, I. Ciampitti |
32. Field Mapping for Aflatoxin Assessment in Peanut Crops Using Thermal ImageryAflatoxin is a toxic carcinogenic compound produced by certain species of Aspergillus fungi, which has a significant impact on peanut production. Aflatoxin levels above a certain threshold (20 ppb in the USA and 4 ppb in Europe) make peanuts unsuitable for export, resulting in significant financial losses for farmers and traders. Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular for remote sensing applications in agriculture. Leveraging this advancement, UAV-based thermal imaging... S. Shrestha, L. Lacerda, G. Vellidis, C. Pilcon, S. Maktabi, M. Sysskind |
33. A Digital Interactive Decision Dashboard to Analyze, Store and Share Year-to-year Crop Genotype YieldThe lag time between data collection and sharing is a critical bottleneck in order to make impactful decision at farmer field-scale. Following this line, there is a need for developing a digital interactive decision dashboard for sharing results of crop trials, in parallel to establish a database for storing data. These crop trials, invaluable for farmers seeking to determine the optimal genotype for their crops, are at risk of becoming obsolete due to the current format and the lack of more near... P. Magalhaes cisdeli, G.N. Nocera santiago, I. Ciampitti, C. Hernandez |
34. Integrated Data-driven Decision Support SystemsSite-specific and data-driven decision support systems in agriculture are evolving fast with the rapid advancements in cutting-edge technologies such as Agricultural Artificial Intelligence (AgAI) and big data integration. Data driven decision support systems have the potential to revolutionize various aspects of farming, from crop monitoring and precision management decisions to the way growers interact with complex technologies. The AgAI decision support-based systems excel at analyzing... L.A. Puntel, P. Pellegrini, S. Joalland , J. Rattalino, L. Vitantonio |
35. Use of Crop and Drought Spectral Indices to Support Harvest Decisions of Peanut Fields in AlabamaHarvest efficiency expressed in quantity and quality of peanut fields could increase if farmers are provided with tools to support harvest decisions. Peanut farmers still rely on a visual and empiric method to assess the right time of peanut maturity but this method does not account for within-field variability of crop growth and maturity. The integration of spectral vegetation indices to assess drought, soil moisture, and crop growth to predict peanut maturity can help farmers strengthen decisions... M.F. Oliveira, B.V. Ortiz, E. Hanyabui, J.B. Costa souza, A. Sanz-saez, S. Luns hatum de almeida , C. Pilcon, G. Vellidis |
36. Exploring Crop Suitability in Senegal Across Global Warming Scenarios: an In-silico ApproachFood production systems in Africa are fragile and vulnerable to climate change. In this context, rising temperatures are the primary cause of the anticipated negative climate change impacts on crop yields. Future yield reductions poses a challenging setting for smallholders to attain self-sufficiency, but new opportunities for managing the risk via implementationof decisions towards mitigate these negative effects from an economic, nutritional, and productive standpoint. Therefore, the objectives... A. Carcedo, I. Ciampitti |
37. Using Remote Sensing to Quantify Biomass in AlfalfaSatellite images are a useful decision support tool to optimize management practices at on-farm scale. Based on this, the development of predictive tools to estimate pasture biomass can be a promising framework to determine the best cutting time, maximizing biomass without compromising yield parameters. Therefore, the main objective of this study was to develop a regression model that allows estimating a value of biomass to give as a recommendation to farmers. To collaborate in their decision... M.F. Lucero, A. Zajdband, C. Hernandez, I. Ciampitti, A. Carcedo |
38. From Scientific Literature to the End User: Democratizing Access to Data Products Through Interactive ApplicationsIn recent years, the sustained advance in the creation of powerful programming libraries is allowing not only the creation of complex models with predictive capabilities but also revolutionizing visualization processes and the deployment of interactive applications. Some of these tools, such as Streamlit or Shiny frameworks in languages such as Python or R, allow us to create from simple applications with friendly interfaces to complex tools. These interactive digital decision dashboards allow... C. Hernandez, A. Correndo, J. Lacasa, P. Magalhaes cisdeli, G.N. Nocera santiago, I. Ciampitti |
39. Predicting the Spatial Distribution of Aflatoxin Hotspots in Peanut Fields Using DSSAT CSM-CROPGRO-PEANUT-AFLATOXINAflatoxin contamination in peanuts (Arachis hypogaea L.) is a persistent concern due to its detrimental effects on both profitability and public health. Several plant stress-inducing factors, including high soil temperatures and low soil moisture, have been associated with aflatoxin contamination levels. Understanding the correlation between stress-inducing factors and contamination levels is essential for implementing effective management strategies. This study uses the DSSAT CSM-CROPGRO-Peanut-Aflatoxin... S. Maktabi, G. Vellidis, G. Hoogenboom, K. Boote, C. Pilcon, J. Fountain, M. Sysskind, S. Kukal |
40. Trends in Agricultural Technology Advancements: Insights from US Patent AnalysisMeeting the demand for food, fiber, and fuel production while addressing environmental concerns and enhancing societal benefits underscores the need to transition to conservation approaches and sustainable intensification pathways in current agricultural cropping systems. Technological advances in agriculture offer promising opportunities to facilitate this transition. Following this rationale, this study aims to analyze prevailing trends in agricultural technology advancements. Active patents... P.B. Cano, A. Carcedo, F. Gomez, C. Hernandez, V. Gimenez, I. Ciampitti |