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1. Fluorescence Imaging Spectroscopy Applied To Citrus DiseasesDiseases are one of the most serious threats for citrus production worldwide. Sao Paulo, Brazil and Florida, USA, are the most important citrus producers and, both, are making efforts for citrus diseases control. Citrus canker is one of the serious diseases, caused by the Xanthomonas citri subsp. citri bacteria, that infects citrus trees and relatives, causing a large economic loss in the citrus juice production. Another important disease affecting the citrus production worldwide is the Huang... C. Wetterich, J. Belasque jr., L. Marcassa |
2. HLB Detection Using Hyperspectral RadiometryThe need for sustainable agriculture requires the adoption of low input, long-term and cost-effective strategies to overcome the adverse impact of disease and nutritional deficiencies on citrus groves. In this context, early detection of diseased trees has become an important topic in the citrus industry. Multiple factors make field assessment of disease conditions a challenging task: the non-specific nature of many symptoms, the possibility of having localized affections in only certain area... J. Gonzalez-mora, C. Vallespi gonzalez, R. Ehsani, C.S. Dima, G. Duhachek |
3. Development Of Ground-based Sensor System For Automated Agricultural Vehicle To Detect Diseases In Citrus PlantationsAn integrated USDA-funded project involving Carnegie Mellon University, University of Florida, Cornell University and John Deere is ongoing, to develop an autonomous tractors for sustainable specialty crop farming. The research teams have come together to develop an automated system for detecting plant stress, estimating yields, and reducing chemical usage through precision spraying for specialty crops. The goals of the automation process are to reduce the tractor-related labor costs, r... S. Sankaran, R. Ehsani, A. Mishra, C. Dima |
4. Normalized Difference Vegetative Index For Evaluating Turfgrass Color: A Comparison Of Two Handheld DevicesThe normalized difference vegetative index (NDVI) is a commonly used light reflectance index in agriculture. For turfgrass research, color and herbicide phytotoxicity have historically been subjectively rated by human evaluators. Prior research has related NDVI to creeping bentgrass (Agrostis stolonifera L.) (R2 = 0.50) and tall fescue (Festuca arundinacea Schreb) (R2 = 0.80) color, and bermudagrass [Cynodon ... J.Q. Moss, X. Pan, Y. Tian, A. Hutchinson |
5. Design And Experiment On Target Spraying Robot For GreenhouseIn greenhouse, the robot sprayers give rise to concern as they reduce the labor intensity and improve the accuracy of the spraying. This paper details the progress to date in the development of a precision robot sprayer. The precision robot sprayer is able to adjust both liquid and air volume to match, the branches contour and location of the greenhouse crops with two ultrasonic sensors which ensures the position of the plants in the greenhouse. The spraying robot with ... W. Ma, C. Zhao, Q.U. Zaman, D. Zach |
6. Adoption Of N-application Rates In Different Broccoli Cultivars By Reflectance MeasurementsTo date many sensors have been solely developed and tested for arable crops. This project aims to develop the means to rapidly map N-demand in broccoli plants on a site-specific, plant-by-plant basis using reflectance measurements. The aim of this specific study was to monitor nitrogen status in six different broccoli cultivars using reflectance measurements and to derive suitable N-fertilization strategies based on the sensor measurements.... S. Graeff, J. Pfenning, W. Claupein |
7. Indirect Measurement Of Creeping Bentgrass N, Chlorophyll, And Color For Precision Golf Green ManagementIndirect measurement of turfgrass tissue through optical sensing may provide golf course managers with non-destructive and relatively simple real-time measurements of golf green N requirements. The objective of this study was to determine the effect of N rate on ‘Crenshaw’ creeping bentgrass (Agrostis stolonifera L.) tissue N, chlorophyll concentration, and color using the GreenSeeker (NTech Industries, Ukiah, CA) handheld sensor... J.Q. Moss, G.E. Bell |
8. Citrus Greening Disease Detection Using Airborne Multispectral And Hyperspectral ImagingCitrus greening disease (Huanglongbing or HLB) has become a major catastrophic disease in Florida’s $9 billion citrus industry since 2005, and continued to be spread to other parts of the U.S. There is no known cure for this disease. As of October 2009, citrus trees in 2,702 different sections (square mile) in 34 counties were infected in Florida. A set of hyperspectral imageries were used to develop disease detection algorithms using image-derived spectral library, the mixture tu... W. Lee, A. Kumar, R. Ehsani, C. Yang, L.G. Albrigo, |
9. Development Of A Precision Sensing Sprayer For The Application Of Nitrogen Fertilizer To TurfgrassNormalized difference vegetation index (NDVI) may be very useful for turfgrass managers to measure turf quality and obtain an indirect measurement of turf N status. The objective of this research was to develop a Nitrogen Fertilization Optimization Algorithm (NFOA) for use in a turfgrass variable rate N applicator on bermudagrass [Cynodon dactylon (L.) Pers] fairways and creeping bentgrass (Agrostis stolonifera L.) greens in Oklahoma. Plots (0.9 X 1.5 ... J.Q. Moss, G.E. Bell, J.B. Solie, M.L. Stone, D.L. Martin, M.E. Payton |
10. Research On Nutrition Detection Technology Of Soil And Leaf Of Citrus Based On Spectroscopic TechniquesThe diagnosis technique of real-time lossless crop nutrition is the foundation and conditions for the precise and effective fertilization. Currently, the diagnosis of crop nutrition mainly relies on the routine chemical analysis of laboratory. Due to the complicated procedure, time-consuming, high cost and high professional technique requirement, it can hardly meet the need of precise variable fertilization technology. Spectrum technology is the technology of real-time and non-destructive tes... S. Yi, L. Deng |
11. Fruit Fly Electronic Monitoring SystemInsects are a constant threat to agriculture, especially the cultivation of various types of fruits such as apples, pears, guava, etc. In this sense, it is worth mentioning the Anastrepha genus flies (known as fruit fly), responsible for billionaire losses in the fruit growing sector around the world, due to the severity of their attack on orchards. In Brazil, this type of pests has been controlled in most product areas by spraying insecticides, which due to the need for prior knowledge regar... C.L. Bazzi, F.V. Silva, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, R.S. Dos santos, A.M. Hachisuca, F. Franz |
12. Yield Mapping in Fruit FarmingDue to the importance of increasing the quantity and quality of world agricultural production, the use of technologies to assist in production processes is essential. Despite this, a timid adoption by precision agriculture (PA) technologies is verified by the Brazilian fruit producers, even though it is one of the segments that had been stood out in recent years in the country's economy. In the PA context, yield maps are rich sources of information, especially by species harvested through... C.L. Bazzi, M.R. Martins, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, A. . Hachisuca, F. Franz |
13. AgDataBox: Web Platform of Data Integration, Software, and Methodologies for Digital AgricultureAgriculture is challenging to produce more profitably, with the world population expected to reach some 10 billion people by 2050. Such a challenge can be achieved by adopting precision agriculture and digital agriculture (Agriculture 4.0). Digital agriculture has become a reality with the availability of cheaper and more powerful sensors, actuators and microprocessors, high-bandwidth cellular communication, cloud communication, and Big Data. Digital agriculture enables the flow of informatio... E.G. Souza, C. Bazzi, A. Hachisuca, R. Sobjak, A. Gavioli, N. Betzek, K. Schenatto, E. Mercante, M. Rodrigues, W. Moreira |
14. Economic Potential of IPMwise – a Generic Decision Support System for Integrated Weed Management in 4 CountriesReducing use and dependency on pesticides in Denmark has been driven by political action plans since the 1980ies, and a series of nationally funded accompanying R&D programs were completed in the period 1989-2006. One result of these programs was a decision support system (DSS) for integrated weed management. The 4th generation (2016) of the agro-biological models and IT-tools in this DSS, named IPMwise. The concept of IPMwise is to systematically exploit that: ... P. Rydahl, O. Boejer, K. Torresen, J.M. Montull, A. Taberner, H. Bückmann, A. Verschwele |
15. Web Application for Automatic Creation of Thematic Maps and Management Zones - AgDataBox-Fast TrackAgriculture is challenging to produce more profitably, with the world population expected to reach some 10 billion people by 2050. Such a challenge can be achieved by adopting precision agriculture and digital agriculture (Agriculture 4.0). Digital agriculture (DA) has become a reality with the availability of cheaper and more powerful sensors, actuators and microprocessors, high-bandwidth cellular communication, cloud communication, and Big Data. DA enables information to flow from used agri... J. Aikes junior, E.G. Souza, C. Bazzi, R. Sobjak, A. Hachisuca, A. Gavioli, N. Betzek, K. Schenatto, W. Moreira, E. Mercante, M. Rodrigues |
16. Delineation of Site-specific Management Zones with Proximal Data and Multi-spectral ImageryMany findings suggested that it’s possible to improve the accuracy of delineating site-specific management zones (SSMZs) through a combination of proximal data with remote sensing imagery. The objective of this study is to assess the feasibility of delineating SSMZs with a wide range of ancillary data (proximal survey and multi-spectral data). The study area is a 22.1acre located 10 miles north of Fort Collins, CO and is known for having a high spatial and temporal variability of soil p... W.A. Yilma, J. Siegfried, R. Khosla |
17. AgDataBox-IoT Application Development for Agrometeorogical Stations in Smart FarmCurrently, Brazil is one of the world’s largest grain producers and exporters. Brazil produced 125 million tons of soybean in the 2019/2020 growing season, becoming the world’s largest soybean producer in 2020. Brazil’s economic dependence on agribusiness makes investments and research necessary to increase yield and profitability. Agriculture has already entered its 4.0 version, also known as digital agriculture, when the industry has entered the 4.0 era. This new paradigm ... A. Hachisuca, E.G. Souza, E. Mercante, R. Sobjak, D. Ganascini, M. Abdala, I. Mendes, C. Bazzi, M. Rodrigues |
18. Integration of High Resolution Multitemporal Satellite Imagery for Improving Agricultural Crop Classification: a Case StudyTimely and accurate agriculture information is vital for ensuring global food security. Satellite imagery has already been proved as a reliable tool for remote crop mapping. Planet satellite imagery provides high cadence, global satellite coverage with higher temporal and spatial resolution than the Landsat-8 and Sentinel-2. This study examined the potential of utilizing high-resolution multitemporal imagery along with and normalized difference vegetation index (NDVI) to map the agricultural ... U. Ali, T. Esau, A. Farooque, Q. Zaman |
19. Data Sources and Risk Management in Precision AgricultureThe digitalisation of the agricultural economy provides more data about the biological processes and technological solutions used for producing agricultural products than ever before. Paralell to the data collection – aiming to provide information for agricultural decision-making and operations – the data informs the farmers, public administration officers and other players in agriculture about the state of the environment. The strategic planning on operation of farms and data han... G. Milics, P.M. Varga, F. Magyar, I. Balla |
20. 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 ... B.P. Ghimire, O. Adedeji, Z. Lin, W. Guo |
21. Decision Support from On-field Precision ExperimentsEmpirically driven adaptive management in large-scale commodity crop production has become possible with spatially controlled application and sub-field scale crop monitoring technology. Site-specific experimentation is fundamental to an agroecosystem adaptive management (AAM) framework that results in information for growers to make informed decisions about their practices. Crop production and quality response data from combine harvester mounted sensors and internet available remote sensing d... B.D. Maxwell, P.D. Hegedus, S.D. Loewen, H.D. Duff, J.W. Sheppard, A.D. Peerlinck, G.L. Morales, A. Bekkerman |
22. Optimizing Nitrogen Application to Maximize Yield and Reduce Environmental Impact in Winter Wheat ProductionField-specific fertilizer rate optimization is known to be beneficial for improving farming profit, and profits can be further improved by dividing the field into smaller plots and applying site-specific rates across the field. Finding optimal rates for these plots is often based on data gathered from said plots, which is used to determine a yield response curve, telling us how much fertilizer needs to be applied to maximize yield. In related work, we use a Convolutional Neural Network, known... A. Peerlinck, J. Sheppard, G.L. Morales luna, P. Hegedus, B. Maxwell |
23. Evaluating APSIM Model for Site-Specific N Management in NebraskaMany approaches have been developed to estimate the optimal N application rates and increase nitrogen use efficiency (NUE). In particular, in-season and variable-rate fertilizer applications have the potential to apply N during the time of rapid plant N uptake and at the rate needed, thereby reducing the potential for nitrogen fertilizer losses. However, there remains great challenges in determining the optimal N rate to apply in site-specific locations within a field in a given year.&nb... L. Thompson, L. Puntel, S. Archontoulis |
24. Impacts of Interpolating Methods on Soil Agri-environmental Phosphorus Maps Under Corn ProductionPhosphorus (P) is an essential nutrient for crops production including corn. However, the excessive P application, tends to P accumulation at the soil surface under crops systems. This may contribute to increase water and groundwater pollution by surface runoff. To prevent this, an agri-environmental P index, (P/Al)M3, was developed in Eastern Canada and USA. This index aims to estimate soil P saturation for accurate P fertilizer recommendations, while integrating agronomical aspec... J. Nze memiaghe, A.N. Cambouris, N. Ziadi, M. Duchemin, A. Karam |
25. Soybean Variable Rate Planting Simulator Using Economic ScenariosSoybean seed costs have increased considerably over the past 15 years, causing a growing interest in variable rate planting (VRP) to optimize seeding rates within soybean fields. We developed a publicly available online Soybean Variable Rate Planting Simulator (http://analytics.iasoybeans.com/cool-apps/SoybeanVRPsimulator/) tool to help farmers, agronomists, and other agriculturalists to understand the essential prerequisite agronomic or economic conditions necessary for profitable VRP implem... B. Mcarthor , A. Prestholt, P. Kyveryga |
26. Stem Characteristics and Local Environmental Variables for Assessment of Alfalfa Winter SurvivalAlfalfa (Medicago sativa L.) is considered the queen of forage due to its high yield, nutritional qualities, and capacity to sequester carbon. However, there are issues with its relatively low persistency and winter survival as compared to grass. Winter survival in alfalfa is affected by diverse factors, including the environment (e.g., snow cover, hardiness period, etc.) and management (e.g., cutting timing, manure application, etc.). Alfalfa's poor winter survival reduces the number of ... M. Saifuzzaman, V. Adamchuk, M. Leduc |
27. Evaluation of Crop Model Based Tools for Corn Site-specific N Management in NebraskaThere is a critical need to reduce the nitrogen (N) footprint from corn-based cropping systems while maintaining or increasing yields and profits. Digital agriculture technologies for site-specific N management have been demonstrated to improve nitrogen use efficiency (NUE). However, adoption of these technologies remains low. Factors such as cost, complexity, unknown impact and large data inputs are associated with low adoption. Grower’s hands-on experience coupled with targeted resear... L. Puntel, L. Thompson , T. Mieno, S. Norquest |
28. 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, givin... I. Gallios, G. Vellidis, C. Butts |
29. An IoT-based Smart Real Time Sensing and Control of Heavy Metals to Ensure Optimal Growth of Plants in an Aquaponic Set-upThe concentration of heavy metals that needs to be maintained in aquaponic environments for habitable growth of plants has been a cause of concern for many decades now as it is not possible to eliminate them completely in a commercial set-up. Our goal is to design a cost-effective real-time smart sensing and actuation system in order to control the concentration of heavy metals in aquaponic solutions. Our solution consists of sensing the nutrient concentrations in the aquaponic solution, name... S. Dhal, J. Louis, N. O'sullivan, J. Gumero, M. Soetan, S. Kalafatis, J. Lusher, S. Mahanta |
30. 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 ... S. Kukal, G. Vellidis |