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1. Mapping The Effect Of Food Prices, Productivity And Poverty In The Development Domains Of NigeriaPoverty remains the major obstacle to economic emancipation and achievement of development agenda in Nigeria. Worse still, rising food prices pose a major threat to feeding the teeming population in Nigeria. Declining food production, high population growth, and negative food trade balance combine to worsen the food and poverty situations in Nigeria. We stand on the premise that surging and volatile food prices could have a hardest hit on those who could not afford it –... O.E. Olayide, A.E. Ikpi, V.O. Okoruwa, , T. Alabi, T. Omodele |
2. Comparison of Active and Passive Spectral Sensors in Discriminating Biomass Parameters and Nitrogen Status in Wheat CultivarsSeveral sensor systems are available for ground-based remote sensing in crops. Vegetation indices of multiple active and passive sensors have seldom been compared in determining plant health. This study was aimed to compare active and passive sensing systems in terms of their ability to recognize agronomic parameters. One bi-directional passive radiometer (BDR) and three active sensors (Crop Circle, GreenSeeker, and an active flash sensor (AFS)) were tested for their ability to assess six destructively... B. Mistele, U. Schmidhalter, K. Erdle |
3. Ontology for Data Representation in the Production of Cotton Fiber in Brazil... C.S. Junior, A.R. Hirakawa |
4. Spectral High-Throughput Assessments Of Phenotypic Differences In Spike Development, Biomass And Nitrogen Partitioning During Grain Filling Of Wheat Under High Yielding Western European ConditionsSingle plant traits such as green biomass, spike dry weight, biomass and nitrogen (N) transfer to grains are important traits for final grain yield. However, methods to assess these traits are laborious and expensive. Spectral reflectance measurements allow researchers to assess cultivar differences of yield-related plant traits and translocation parameters that are affected by different genetic material and varying amounts of available N. In a field experiment, six high-yielding wheat cultivars... U. Schmidhalter, K. Erdle |
5. Use Of Active Radiometers To Estimate Biomass, Leaf Area Index, And Plant Height In CottonActive radiometers have been tested extensively as tools to assess in-season nitrogen (N) status of crops like wheat (Triticum aestivum), corn (Zea mays), and cotton (Gossypium hirsutum). Fewer studies target in-season plant growth parameters such as biomass, plant height or leaf area index (LAI). Uses of this plant data include simulation modeling, total N uptake measurements, evapotranspiration (ET) estimates and irrigation... K.R. Thorp, J.W. White, M.M. Conley, J. Mon, K.F. Bronson |
6. A Tree Planting Site-Specific Fumigant Applicator for Orchard CropsThe goal of this research was to use recent advances in the global positioning system and computer technology to apply just the right amount of fumigant where it is most needed (i.e., in the neighborhood of each tree planting site or tree- planting-site-specific application) to decrease the incidence of replant disease, and achieve the environmental and economical benefits of reducing the application of these toxic chemicals. In the first year of this study we retrofitted a chemical applicator... S.K. Upadhayaya, V. Udompetaikul, M.S. Shafii, G.T. Browne |
7. Barriers to Adoption of Smart Farming Technologies in GermanyThe number of smart farming technologies available on the market is growing rapidly. Recent surveys show that despite extensive research efforts and media coverage, adoption of smart farming technologies is still lower than expected in Germany. Media analysis, a multi stakeholder workshop, and the Adoption and Diffusion Outcome Prediction Tool (ADOPT) (Kuehne et al. 2017) were applied to analyze the underlying adoption barriers that explain the low to moderate adoption levels of smart farming... M. Gandorfer, S. Schleicher, K. Erdle |
8. 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 estimation... C. Yang, C. Suh, W. Guo, H. Zhao, J. Zhang, R. Eyster |
9. Spatially Explicit Prediction of Soil Nutrients and Characteristics in Corn Fields Using Soil Electrical Conductivity Data and Terrain AttributesSite specific nutrient management (SSNM) in corn production environments can increase nutrient use efficiency and reduce gaseous and leaching losses. To implement SSNM plans, farmers need methods to monitor and map the spatial and temporal trends of soil nutrients. High resolution electrical conductivity (EC) mapping is becoming more available and affordable. The hypothesis for this study is that EC of the soil, in conjunction with detailed terrain attributes, can be used to map soil nutrients... S. Sela, N. Graff, K. Mizuta, Y. Miao |
10. Use of MLP Neural Networks for Sucrose Yield Prediction in SugarbeetINTRODUCTION Sugar beet is one of the more technified agro industries in Spain. In the last years, it has leaded as well the digital transformation with the objective of maintaining sugar beet competitivity both national and internationally. Among other lines, very high potential has been identified in determining the sucrose content using a combination of Artificial Intelligence and Remote Sensing. This work presents the conclusions of an extensive data acquisition task, creation of... M. Cabrera dengra, C. Ferraz pueyo, V. Pajuelo madrigal, L. Moreno heras, G. Inunciaga leston, R. Fortes |
11. You Can Not Manage What You Dont MeasureThe problem of variability in soil nutrient analysis has been studied for years by a number of industry experts; unable to decipher and commercialize hyperspectral soil sensing. Many studies have taken years of testing to account for variability thathas a dramatic impacts on precision of recommendations. The main tradeoff we have identified is between accuracy and precision. Large quantities of raw data are required... K. Fleming, N. Schottle, P. Nagel, G. Koch |
12. 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 across... 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 |
13. Evaluation of a Single Transect Method for Collecting Grape Samples Based on Sentinel-2 Imagery for the Characterization of Overall Vineyard PerformanceCommercial vineyards are streamed into different wine programs based on analysis of grape or juice samples collected from the field, but spatial and temporal variability can lead to sub-optimal tiering of grapes. This is a particularly difficult problem to overcome in the typically large vineyards of California’s Central Valley. Due to economic and laboratory constraints on sample collection, processing, and analysis, a single sample is often expected to represent the overall fruit quality... B. Sams, M. Aboutalebi, L. Sanchez, N. Dokoozlian, R. Bramley |
14. Influence of Ground Control Points and Processing Parameters on UAS Image Mosaicking for Plant Height EstimationDigital surface models (DSMs) and 3D point clouds, generated using overlapping images from unmanned aircraft systems (UASs), are often used for plant height estimation in phenotyping and precision agriculture. This study examined the effects of the quantity and placement of ground control points (GCPs) and image processing parameters on the creation of DSMs and 3D point clouds for plant height estimation. A 2-ha field containing multiple experimental plots with four crops (corn, cotton, sorghum,... C. Yang, H. Zhao, W. Guo, J. Zhang, C. Suh, B.K. Fritz |
15. Multi-sensor Remote Sensing: an AI-driven Framework for Predicting Sugarcane FeedstockPredicting saccharine and bioenergy feedstocks in sugarcane enables stakeholders to determine the precise time and location for harvesting a better product in the field. Consequently, it can streamline workflows while enhancing the cost-effectiveness of full-scale production. On one hand, Brix, Purity, and total reducing sugars (TRS) can provide meaningful and reliable indicators of high-quality raw materials for industrial food and fuel processing. On the other hand, Cellulose, Hemicellulose,... M. Barbosa, D. Duron, F. Rontani, G. Bortolon, B. Moreira, L. Oliveira, T. Setiyono, L. Shiratsuchi, R.P. Silva, K.H. Holland |
16. UAV Multispectral Data As a Suitable Tool for Predicting Sweetness, Size, and Yield of Vidalia OnionsVidalia onions is a specialty crop cultivated solely within the southeastern region of Georgia. The key distinguishing characteristic of Vidalia onions is its high sugar content, making them highly prized and widely consumed. Ten thousand acres are grown with Vidalia Onions each year approximately, and the market value (~$150Mi/year) makes the crop very important for the State of Georgia. Traditionally, the planting, weeding, spraying, harvesting, and post-harvesting operations are usually done... M. Barbosa, L. Oliveira, C. Tyson, A. Shirley, R. Santos, L. Sales, R. Vargas |
17. Automated Sow Estrus Detection Using Machine Vision TechnologySuccessful artificial insemination for gilts and sows relies on accurate timing that is determined by estrus check. Estrus checks in current farms are manually conducted by skilled breeding technicians using the back pressure test (BPT) method that is labor-intensive and inefficient due to the large animal-to-staff ratio. This study aimed to develop a robotic imaging system powered by artificial intelligence technology to automatically detect estrus status for gilts and sows in a stall-housing... J. Zhou, Z. Xu, T.J. Safranski, C. Bromfield |
18. 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 |
19. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal DataField scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locations... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi |
20. Deep Learning to Estimate Sorghum Yield with Uncrewed Aerial System ImageryIn the face of growing demand for food, feed, and fuel, plant breeders are challenged to accelerate yield potential through quick and efficient cultivar development. Plant breeders often conduct large-scale trials in multiple locations and years to address these goals. Sorghum breeding, integral to these efforts, requires early, accurate, and scalable harvestable yield predictions, traditionally possible only after harvest, which is time-consuming and laborious. This research harnesses high-throughput... M.A. Bari, A. Bakshi, T. Witt, D. Caragea, K. Jagadish, T. Felderhoff |
21. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and VisionAdvancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi |
22. Advancements in Agricultural Robots for Specialty Crops: a Comprehensive Review of Innovations, Challenges, and ProspectsThe emergence of robot technology presents a timely opportunity to revolutionize specialty crop production, offering crucial support across various activities such as planting, supporting general traits, and harvesting. These robots play a pivotal role in keeping stakeholders up-to-date of developments in their production fields, while providing them the capability to automate laborious tasks. Then, to elucidate the advancements in this domain, we present the results of a comprehensive review... M. Barbosa, R. Santos, L. Sales, L. Oliveira |