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1. Implementation of ECU For Agricultural Machines Based On IsoAgLib Open SourceIn this paper work, we consider implementation of electronic control unit (ECU) for agricultural machineries. Software implementation is based on IsoAgLib library developed by OSB&IT Engineering Company. We modify IsoAgLib and upgrade it for our target system. The IsoAgLib is an object oriented C++ library that has the communication services and management systems according to the ISO 11783 standard. This library allows building ISOBUS compatible equipment without the protocols implementa... E. Tumenjargal, L. Badarch, W. Ham, H. Kwon |
2. An RFID-Based Variable Rate Technology Fertilizer Applicator for Plantation Tree CropsCurrently, in the Malaysian tree crop plantation, fertilizer is applied manually or mechanically at uniform rate without due consideration to nutrient variability. Potential wastage and excessive application of this fertilizer contaminates ground water and raises its mineral contents above the World Health Organization (WHO) limit for safe drinking water. However, Variable Rate Technology (VRT) fertilizer application promotes Green Engineering practice by reducing excessive fertilizer ap... A. Yahya |
3. Computer Aided Engineering Analysis and Design Optimization for Precision Manufacturing of Tillage Tool: Sweep CultivatorThe process optimization in advance tillage tool system conceptually designed and fabricated by computer aided engineering analysis techniques. The Software testing a field performance is taken in the soil bed preparation as well as in the various crop patterns. It was found most use full in obtaining high weed removal efficiency. The precision geometry, optimum energy utilization, multi-operational design, easy transport and flexible attachments are some of the features which results in achi... G.U. Shinde, D.M. Salokhe, P.D. Badgujar, D.B. Sharma |
4. Adaptive Sensor Fusion Method for Agricultural and Environmental MonitoringEnvironmental and agricultural monitoring involves continuous observation in areas such as grains crop, in order to evaluate changes in the environment. Wireless Sensor Networks may be employed in th... C.E. Cugnasca, M.A. Dota |
5. Optimization of Forage Harvesting By Automatic Speed Control and Additive ApplicationEfficient use of machines is especially important in forage harvesting due to the short harvesting period and expensive machinery. To achieve the best efficiency, a harvesting machine, such as a loader wagon, should be used with optimal loading. Whereas overloading the machine can cause blockages in the cut-and-feed unit, underloading consumes more time and reduces the quality of the resulting silage. In addition, the quality can be improved by optimizing the dosage of the additive. Since the... A. Suokannas, J. Backman, A. Visala, A. Kunnas |
6. Study on Water Distribution Measurement in Sand Using Sound Vibration... T. Sugimoto, T. Shirakawa, M. Sano, M. Ohaba, S. Shibusawa, Y. Nakagawa |
7. Measuring Error on Working Depth of Real-time Soil SensorThis paper described about the measuring error on working depth of the Real-time soil sensor (RTSS). It is necessary for accurately evaluating to observe the variation on the working depth, because the RTSS run in various real field conditions, such as soft or hard and even or uneven, and the RTSS has various using objective. In this paper, the RTSS run on asphalt with steps while the three-point hitch was free and position-controlled. In position-controlled, the measuring depth that is ... R. Kanda, M. Kodaira, S. Shibusawa |
8. 3D Acquisition System Applied to Agronomic ScenesTo enable a better decision making by the farmer in order to optimize the crop management, it is essential to provide a set of information on basic parameters of the crops. These information are numerous and the image processing is increasingly used for disease detection, weed detection or yield estimation. We will focus initially on assessing the yield of a wheat crop in automatic way. This yield is directly related to the number of ears per square meter for which the counting is curren... F. Cointault, P. Gouton, B. Billiot |
9. Water Distribution Response in a Soil-Root System for Subsurface Precision IrrigationA subsurface capillary irrigation system with a water source buried in a soil has been developed for precision irrigation. This system has advantages in the efficient irrigation to save much water and the real time measurement of evapotranspiration of plants. Creating this new subsurface capilla... S. Shibusawa, M. Ohaba, M.B. Zainal abidin, M. Kodaira, Q. Li |
10. Probabilistic Relational Model-based Scheduling Approach for Farmland Soil Sensor NetworkEnergy efficiency is one of the core issues of farmland soil sensor network (FSSN). For battery powered FSSN, the energy constraint restricts lifetime of WSN, which poses great challenged to its large scale application. Prior work has suggested approaches to optimize the RF module and communication protocols to reduce power consumption of FSSN. Although shown to be ef... L. Chen, R. Zhang, G. Xu |
11. Design Of A Data Acquisition System For Weighing LysimetersThe weighing lysimeter is an important tool for scientists to con... C. Zhang, X. Xue, L. Chen, W. Huang |
12. 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 |
13. Spray Pattern and Droplet Spectra Characteristics from an Actively Controlled Variable-Orifice Nozzle... M.P. Sama, S.A. Shearer, J.D. Luck |
14. Spot- Application of Pre-Emergence Herbicide Using a Variable Rate Sprayer in Wild BlueberryWild blueberry producers apply herbicides uniformly to control grasses and weeds without considering the significant weed density variability and bare spots within fields. The repeated and excessive use of ... Q. Zaman, Y. Chang, A. Farooque, A. Schumann, D. Percival, M. Cheema, T. Esau |
15. Development of Sensing System Using Digital Photography Technique for Spot-Application of Herbicide in Wild Blueberry FieldsAn automated sensing system, hardware and software, was developed for spot-application of herbicide with 6.1 m boom automated prototype spraye... Q. Zaman, T.J. Esau, A.A. Farooque, A.W. Schumann, D.C. Percival, Y.K. Chang |
16. Implementation of a Controller Unit Based on the ISO 11783 Standard for Automatic Measurement of the Electrical Conductivity of the Soil... L. M. rabello, R. R. d. pereira, W. C. lopes, R. Y. inamasu, R. V. de sousa |
17. Adaptive Control of Capillary Water Flow Under Modified Subsurface Irrigation Based on a SPAC ModelSoil moisture in a rhizosphere of a tomato is controlled adaptively based on a simple soil-plant-atmosphere continuum (SPAC) model. The water flow from a soil through a plant to the atmosphere is governed by the analogous rule of the SPAC model. In our experiment, we assume that plant transpiration is only affected by the water-potential of air when the soil m... M. Ohaba, M.B. zainal abidin, Q. Li, S. Shibusawa, M. Kodaira, K. Osato |
18. Farmer Uptake of Variable Rate Irrigation Technologies in New ZealandCost effective technological advances in recent years have allowed the uptake of variable rate irrigation (VRI) systems in New Zealand. Typically an existing sprinkler irrigator is modified for variable rate irrigation, irrigation management zones are defined using EM (ele... C. Hedley, I. Yule |
19. An Approach to Making Non-Smell Composting System : Case Study in FuchuThe project to form ... R. Fusamura, S. Shibusawa, M. Kodaira |
20. Development of Variable Rate Applicators Using Real-Time Machine Vision Sensing and Control System for Spot-Application of AgrochemicalsThe variable rate applicators comprised of a real-time sensing and control system were developed and tested for spot-application of agrochemicals (fertilizer and pesticides). ... Q. Zaman |
21. Hyperspectral Imaging Of Sugar Beet Symptoms Caused By Soil-borne OrganismsThe soil-borne pathogen Rhizoctonia solani and the plant parasitic nematode Heterodera schachtii are the most important constraints in sugar beet production worldwide. Symptoms caused by fungal infection are yellowing of leaves and rotting of the beet tuber late in the cropping season. Nematode afflicted plants show stunted growth early in the cropping season and also leaf wilting late in the season when water stress often sets in. Due to the low mobility of soil-borne organisms, they are ide... C. Hillnhuetter, A. Mahlein, R.A. Sikora, E. Oerke |
22. Using An Active Crop Sensor To Detect Variability Of Nitrogen Supply On Sugar Cane FieldsNitrogen management has been intensively studied on several crops and recently associated with variable rate application on-the-go based on crop sensors. On sugar cane those studies are yet scarce and as a biofuel crop the input of energy matters, looking for a high positive balance of biofuel production and low carbon emission on the whole production system. This paper shows the first results obtained using a nitrogen and biomass sensor (N-SensorTM ALS, Yara International ASA) aiming to indi... J. Molin, G. Portz, J. Jasper |
23. Primary Framework Of Diagnosis And Management For Wheat Production Based On The Online Telemonitoring NetworksPRIMARY FRAMEWORK OF DIAGNOSIS AND MANAGEMENT FOR WHEAT PRODUCTION BASED ON THE ONLINE TELEMONITORING NETWORKS Sun Zhong-fu, Du Ke-ming, Zhang Yan, Liang Ju-bao Inst. of Environ. & Sustainable Develop. in Agriculture£¨IEDA£© Chinese... Z. Sun, , |
24. Developing An Active Crop Sensor-based In-season Nitrogen Management Strategy For Rice In Northeast ChinaCrop sensor-based in-season N management strategies have been successfully developed and evaluated for winter wheat around the world, but little has been reported for rice. The objective of this study was to develop an active crop sensor-based in-season N management strategy for upland rice in ... Y. Yao, Y. Miao, S. Huang, M.L. Gnyp, R. Jiang, X. Chen, G. Bareth |
25. Canopy Reflectance Sensing As Impacted By Corn Hybrid GrowthDetection of physical and chemical properties within the growing season could help predict the overall health and yield of a corn crop. Little research has been done to show differences of corn hybrids on canopy reflectance sensing. This study was conducted to examine these potential differences during the early- to mid-vegetative growth stages of corn on three different soil types in Missouri. Canopy sensing (Crop Circle) and SPAD chlorophyll met... A. Sheridan, K.A. Sudduth, N.R. Kitchen |
26. Is A Nitrogen-rich Reference Needed For Canopy Sensor-based Corn Nitrogen Applications?The nitrogen (N) supplying capacity of the soil available to support corn (Zea mays L.) production can be highly variable both among and within fields. In recent years, canopy reflectance sensing has been investigated for in-season assessment of crop N health and fertilization. Typically the procedure followed compares the crop in an area known to be non-limiting in N (called a N-rich area) to the crop in areas inadequately fertilized. Measurements from the two areas are used to ... N.R. Kitchen, K.S. Suddth, S.T. Drummond |
27. Innovative Optical Sensors For Diagnosis, Mapping And Real-time Management Of Row Crops: The Use Of Polyphenolics And FluorescenceForce-A’s Dualex® leaf-clips and Multiplex® proximal optical sensors give rapid and quantitative estimations of chlorophyll and polyphenolics of crops by measuring the fluorescence and absorption properties of these molecules. The in vivo and real-time assessments of these plant compounds allow us to define new indicators of crop nitrogen status, health and quality. The measurements of these indicators allow consultants and farmers to monitor the nitrogen status of row crop... V. Martinon, , C. Duval, J. Fumery |
28. Ultra Low Level Aircraft (ULLA) As A Platform For Active Optical Sensing Of Crop BiomassCrop producers requiring crop biomass maps to support timely application of in-season fertilisers, pesticides or growth regulators rely on either on-ground active sensors or airborne/satellite imagery. Active crop sensing (for example using Yara N-SensorTM, GreenseekerTM or CropcircleTM) can only be used when the crop is accessible by person or vehicle, and extensive, high-resolution coverage is time consuming. On the other hand, airborne or satellite imaging ... D.W. Lamb, M.G. Trotter, D. Schneider |
29. Investigation Of Crop Varieties At Different Growth Stages Using Optical Sensor DataCotton, soybean and sorghum are economically important crops in Texas. Knowing the growing status of crops at different stages of growth is crucial to apply site-specific management and increase crop yield for farmers. Field experiments were initiated to measure cotton, soybean and sorghum plants growth status and spatial variability through the whole growing cycle. A ground-based active optical sensor, Greenseeker®, was used to collect the Normalized Difference Vegetation Index (NDVI) da... H. Zhang, Y. Lan, J. Westbrook, C. Suh, C. Hoffmann, R. Lacey |
30. Performance Evaluation Of Off-shelf Range Sensors For In-field Crop Height MeasurementAbstract: In-season plant height is a good predictor of yield potential, which needs to be measured with techniques of high spatial resolution and accuracy. In this study, systematic performance evaluations were conducted on three types of commercial range sensors, an ultrasonic sensor, a laser range finder and a range camera on plant height measurement, under laboratory and field conditions. Results showed that the average errors between the measured heigh... N. Wang, Y. Shi, R.K. Taylor |
31. A Model For Wheat Yield Prediction Based On Real-time Monitoring Of Environmental Factors... B. Dumont, F. Vancutsem, J. Destain, B. Bodson, F. Lebeau, M. Destain |
32. Real-time Calibration Of Active Crop Sensor System For Making In-season N Applications... K.H. Holland, J.S. Schepers |
33. Comparison Of Three Canopy Reflectance Sensors For Variable-rate Nitrogen Application In CornIn recent years, canopy reflectance sensing has been investigated for in-season assessment of crop nitrogen (N) health and subsequent control of N fertilization. The several sensor systems that are now commercially available have design and operational differences. One difference is the sensed wavelengths, although these typically include wavelengths in both the visible and near-infrared ranges. Another difference is orientation – the sensors most commonly used in the US are designed to... K.A. Sudduth, N.R. Kitchen, S.T. Drummond |
34. Changes Of Data Sampling Procedure To Avoid Energy And Data Losses During Microclimates Monitoring With Wireless Sensor Networks... J.C. Benavente, C.E. Cugnasca, M.F. Barros, H.P. Santos, G. Http://icons.paqinteractive.com/16x16/ac |
35. Development Of A Nitrogen Requirement Algorithm Using Ground-based Active Remote Sensors In Irrigated MaizeStudies have shown that normalized difference vegetation index (NDVI) from ground-based active remote sensors is highly related with leaf N content in maize (Zea mays). Remotely sensed NDVI imagery can provide valuable information about in-field N variability in maize and significant linear relationships between sensor NDVI and maize grain yield have been found suggesting that an N recommendation algorithm based on NDVI could optimize N application. Therefore, a study was conducted using the ... T. Shaver, R. Khosla, D. Westfall |
36. Comparison Of Spectral Indices Derived From Active Crop Canopy Sensors For Assessing Nitrogen And Water Status... L. Shiratsuchi, R.B. Ferguson, J.F. Shanahan, V.I. Adamchuk, G. Slater |
37. Embedded Sensing System To Control Variable Rate Agricultural InputsThis paper presents an embedded sensing system for agricultural machines to collect information about plants and also to control the application of fertilizer with variable rate in corn crop. The Crop Circle reflectance sensor was used with the aim to explore the spe... G.T. Tangerino, R.V. Sousa, A.J. Porto, R. . Inamasu, P. Pinkston |
38. Development Of Batch Type Yield Monitor For Small FieldsAbstract The yield monitor is intended to give the user an accurate assessment of yield variations y within a field. A yield monitor can assist grain producers in many aspects of crop management. A yield monitor by itself can provide useful information and enhance on-farm research. Yield data c... M. Singh, A. Sharma, G. Singh, P. Fixen |
39. Assessment Of Physiological Effects Of Fungicides In WheatThe use of fungicides is one of the most widespread methods implemented in intensive crop production focused in solving phytosanitary problems. The use of fungicides belonging to groups such as strobilurins has been associated with positive physiological effects such as increased tolerance against abiotic stresses, changes in plant growth regulator activities and delayed leaf senescence. The use of thermography is a non- destructive method which permits to distinguish physiological changes ca... C. Berdugo, U. Steiner, E. Oerke, H. Dehne |
40. Development Of A Sensor Suite To Determine Plant Water PotentialThe goal of this research was to develop a mobile sensor suite to determine plant water status in almonds and walnuts. The sensor suite consisted of an infrared thermometer to measure leaf temperature and additional sensors to measure relevant ambient conditions such as light intensity, air temperature, air humidity, and wind speed. In the Summer of 2009, the system was used to study the relationship between leaf temperature, plant water status, and relevant microclimatic information in an al... V. Udompetaikul, S. Upadhyaya, B. Lampinen, D. Slaughter |
41. Sensor And System Technology For Individual Plant Crop ScoutingSensor and system technologies are key components for automatic treatment of individual plants as well as for plant phenotyping in field trials. Based on experiences in research and application of sensors in agriculture the authors have developed phenotyping platforms for field applications including sensors, system and software development and application-specific mountings. Sensor and data fusion have a high potential by compensating varying s... A. Ruckelshausen, K.V. Alheit, L. Busemeyer, R. Klose, A. Linz, K. Moeller, F. Rahe, M. Thiel, D. Trautz, U. Weiss |
42. Vlite Node – New Sensor Technology For Precision Farming... K. Charvat, J. Jezek, M. Musil, Z. Krivanek, P. Gnip |
43. Cognitive Radio In Precision AgricultureThis is an attempt to design a precision agriculture (PA) model, to control the required parameters in greenhouse with wireless sensor network (WSN). This proto type model of wireless sensor and actuators network is designed as per required parameters of available crops in a greenhouse. The design of the sensor node consists of sensors, a micro-controller and a low-powered radio module. Real-time data, enable the operators to characterise the operating parameters of the greenhouse and a... S.P. Nayse, D.D. Choudhari, V.M. Wadhai |
44. Optimizing Vineyard Irrigation Through The Automatic Resistivity Profiling (arp) Technology. The Proposal Of A Methodological ApproachIn Tuscany, central Italy, grape cultivation and wine production (i.e., Chianti DOCG, Brunello di Montalcino) are farming activities appreciated worldwide. Differently from the past, irrigation is allowed to meet the intense physiological stress that may occur during seasons affected by the increasing climate variability, in order to guarantee quality product and hence high market profitability in many vines areas. Most ... P. Pagni, G.P. Ghinassi, M.P. Vieri |
45. Canopy Reflectance-based Nitrogen Management Strategies For Subsurface Drip Irrigated CottonNitrogen (N) fertilizer management in subsurface drip irrigation (SDI) systems for cotton (Gossypium hirsutum L.) can be very efficient when N is fertigated on a near daily time step. Determining the amounts and timing of the N fertigation, however are questions that weekly canopy reflectance measurements may answer. The main objective of this 3-yr. study was to test two canopy reflectance strategies for adjusting urea ammonium nitrate (UAN) fertilizer in-season injections... K. Bronson |
46. Edxrfs-based Sensing Of Phosphorus And Other Mineral Macronutrient Distribution In Field SoilsPhosphorus (P) requirements for major agronomic crops have been currently based on a pre-plant mass balance method. Fertilizer needs are estimated from crop needs, available soil P and other external nutrient inputs that include animal manure, crop residues, etc... Thus, this approach uses f... T.H. Dao |
47. NDVI 'Depression' In Pastures Following GrazingPasture biomass estimation from normalized difference vegetation index (NDVI) using ground, air or space borne sensors is becoming more widely used in precision agriculture. Proximal active optical sensors (AOS) have the potential to eliminate the confounding effects of path radiance and target illumination conditions typically encountered using passive sensors. Any algorithm that infers the green fraction of pasture from NDVI must factor in plant morphology and live/dead plant ratio, irrespe... J.S. Stanley, D.W. Lamb, M.G. Trotter, M.M. Rahman |
48. Multitemporal Satellite Imaging To Support Near Real-Time Precision FarmingThis paper presents a 2014 update on the DMC constellation of optical satellite sensors and how they are exploited for various types of agricultural monitoring. Thousands of farmers around the world are exploiting this powerful data source for the management of crops, enabled by specialist service providers which convert the imagery into meaningful biophysical measurements and spatially variable nitrogen/irrigation recommendations. The paper also looks ahead to future ... G. Holmes |
49. Detection Of Fruit Tree Water Status In Orchards From Remote Sensing Thermal ImageryIn deciduous fruit trees there is a growing need of using water status indicators for scheduling irrigation and adopt regulated deficit irrigation (RDI) strategies taking into account spatial variability of orchards. RDI strategies have been successfully adopted for many fruit trees as a means for reducing water use and because yield and quality at harvest are not sensitive to water stress at some developmental stages. Although water status is generally monitored by measuring tr... P.J. Zarco-tejada, V. Gonzalez-dugo, J. Girona, E. Fereres, J. Bellvert |
50. An Evaluation Of HJ-CCD Broadband Vegtation Indices For Leaf Chlorophyll Content EstimationLeaf chlorophyll content is one of the most important biochemical variables for crop physiological status assessment, crop biomass estimation and crop yield prediction in precision agriculture. Vegetation indices were considered effective for chlorophyll content estimation. Although hyperspectral reflectance is proven to be better than multispectral reflectance for leaf chlorophyll content retrieval, the scarcity of available data from satellite hyperspectra... T. Dong, J. Shang, J. Meng, J. Liu |
51. Near-Real-Time Remote Sensing And Yield Monitoring Of Biomass CropsThe demand for bioenergy crops production has increased tremendously by the biofuel industry for substitution of traditional fuels due to the economic availability and environmental benefits. Pre-Harvest monitoring of biomass production is necessary to develop optimized instrumentation and data processing systems for crop growth, health and stress monitoring; and to develop algorithms for field operation scheduling. To cope with the problems of missing criti... Y. Zhao, L. Li, K.C. Ting, L.F. Tian, T. Ahamed |
52. Evaluating Soil Nutrition Status With Remote Sensing Derived Land ProductivityAvailable nitrogen is the amount of this nutrient available to plants in the soil and the amount of nitrogen provided by fertilizers. Compared to total nitrogen, nitrogen availability is a more useful tool for determining how much fertilizer you need and when to apply it. Determining the level of nitrogen available in field soil is also a useful method to increase the efficiency of fertilizer. Most soil properties are time-consuming and costly to measure, and also change over ti... Z. Chen, J. Meng, X. You |
53. Design, Development And Application Of A Satellite-Based Field Monitoring System To Support Precision FarmingThe factual base of precision agriculture (PA) - the spatial and temporal variability of soil and crop factors within or between different fields has been recognized for centuries. Field information on seeding suitability, soil & crop nutrition status and crop mature date is needed to optimize field management. How to acquire the spatially and temporally varied field parameters accurately, efficiently and at affordable cost has always been the focus of the researches in the ... Z. Li, B. Wu, J. Meng |
54. Creation Of Prescription For Optimal Nitrogen Fertilization Through Evaluation Of Soil Carbon Amount Using Remotely Sensed DataIn these years, drastic increase of agricultural production costs has been induced, which was triggered by the sharp rise of costs relating to agricultural production materials such as fertilizers and oil. In Japan, the substantial negative influence is anticipated to spread over to management of the farmers particularly in Hokkaido, the northern part of Japan. As one of the measures against this influence, a plan of effective fertilizer application and ... E. Tamura, K. Aijima, K. Niwa, O. Nagata, K. Wakabayashi, C. Hongo |
55. Study On Plant Health Condition Monitoring Using Acoustic Radiation ForceIn recent years, irrigation method using the negative pressure difference attracts attention from the point of view of water saving. In addition, it is proved that this technique is effective in upbringing of the plant as well as saving of water. By measuring water distribution of soil, active irrigation control will be performed In our previous study, we confirmed that the resonance frequency of a leaf is influenced by the water stress to the plant. Thus the vibration measureme... Y. Nakagawa, M. Sano, T. Shirakawa, K. Yamagishi, T. Sugihara, M. Ohaba, S. Shibusawa, T. Sugimoto |
56. 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 c... U. Schmidhalter, K. Erdle |
57. Monitoring Ratio Of Leaf Carbon To Nitrogen In Winter Wheat Based On Hyperspectral MeasurementsThe metabolic status of carbon (C) and nitrogen (N) as two essential elements of crop plants has significant influence on the ultimate formation of yield and quality in crop production. Leaf is the major organ of plant photosynthesis and physiological activity, and in leaf tissues the ratio of carbon to nitrogen (C/N), defined as the ratio of LCC (leaf carbon concentration) to LNC (leaf nitrogen concentration), can... X. Xu |
58. An Inexpensive Aerial Platform For Precise Remote Sensing Of Almond And Walnut Canopy TemperatureCurrent irrigation practices depend largely on imprecise applications of water over fields with varying degrees of heterogeneity. In most cases, the amount of water applied over a given field is determined by the amount the most water-stressed part of the field needs. This equates to over-watering most of the field in order to satisfy the needs of one part of the field. This approach not only wastes resources, but can have a detrimental effect on the value of that crop. A system t... K. Crawford, S. Upadhyaya, R. Dhillon, F. Rojo, J. Roach |
59. Automatic Detection And Mapping Of Irrigation System Failures Using Remotely Sensed Canopy Temperature And Image ProcessingToday there is no systematic way to identify and locate failures of irrigation systems mainly because of the labor costs associated with locating the failures. The general aim of this study was to develop an airborne thermal imaging system for semi - automatic monitoring and mapping of irrigation system failures, specifically, of leaks and clogs. Initially, leaks and clogs were simulated by setting controlled trials in table grapes vineyards and olive groves. Airborne ther... V. Alchanatis, Y. Cohen, M. Sprinstin, A. Cohen, I. Zipori, A. Dag, A. Naor |
60. Are Thermal Images Adequate For Irrigation Management?Thermal crop sensing technologies have potential as tools for monitoring and mapping crop water status, improving water use efficiency and precisely managing irrigation. As thermal sensors and imagers became more affordable, various platforms were examined to allow for canopy- and field-scale acquisitions of canopy temperature and to extract maps of water status variability. Various canopy temperature statistics and crop water stress index (CWSI) were used to estimate water stat... O. Rosenberg, V. Alchanatis, Y. Saranga, A. Bosak, Y. Cohen |
61. Estimation of Vegetative Biomass Using On-the-Go Mobile SensorsNon-destructive methods for estimation of vegetative biomass have been developed using several remote sensing strategies as well as physical measurement techniques. An effective method for estimating biomass must be at least as accurate as the accepted standard for destructive removal measurement techniques such as a forage harvester or quad harvest strategies. In large part vegetative biomass is considered a function of canopy or plant height. Subsequently, a method o... J. Pittman |
62. Use Of Quality And Quantity Information Towards Evaluating The Importance Of Independent Variables In Yield PredictionYield predictions based on remotely sensed data are not always accurate. Adding meteorological and other data can help, but may also result in over-fitting. Working with American Crystal Sugar, we were able to demonstrate that the relevance of independent variables can be tested much more reliably when not only yield but also quality attributes are known, such as the sugar content and the s... E. Momsen, J. Xu, D.W. Franzen, J.F. Nowatzki, K. Farahmand, A.M. Denton |
63. Use of Farmer’s Experience for Management Zones DelineationIn the management of spatial variability of the fields, the management zone approach (MZs) divides the area into sub-regions of minimal soil and plant variability, which have maximum homogeneity of topography and soil conditions, so that these MZs must lead to the same potential yield. Farmers have experience of which areas of a field have high and low yields, and the use of this knowledge base can allow the identification of MZs in a field based on production history. The objective of this s... K. Schenatto, E.G. Souza, C.L. Bazzi, A. Gavioli, N.M. Betzek, P.S. Magalhães |
64. Supporting and Analysing On-Farm Nitrogen Tramline Trials So Farmers, Industry, Agronomists and Scientists Can LearN TogetherNitrogen fertilizer decisions are considered important for the agronomic, economic and environmental performance of cereal crop production. Despite good recommendation systems large unpredicted variation exists in measured N requirements. There may be fields and farms that are consistently receiving too much or too little N fertilizer, therefore losing substantial profit from wasted fertilizer or lost yield. Precision farming technologies can enable farmers (& researchers) to test appropr... D. Kindred, R. Sylvester-bradley, S. Clarke, S. Roques, D. Hatley, B. Marchant |
65. An On-farm Experimental Philosophy for Farmer-centric Digital InnovationIn this paper, we review learnings gained from early On-Farm Experiments (OFE) conducted in the broadacre Australian grain industry from the 1990s to the present day. Although the initiative was originally centered around the possibilities of new data and analytics in precision agriculture, we discovered that OFEs could represent a platform for engaging farmers around digital technologies and innovation. Insight from interacting closely with farmers and advisors leads us to argue for a change... S. Cook, M. Lacoste, F. Evans, M. Ridout, M. Gibberd, T. Oberthur |
66. Evaluation of Strip Tillage Systems in Maize Production in HungaryStrip tillage is a form of conservation tillage system. It combines the benefits of conventional tillage systems with the soil-protecting advantages of no-tillage. The tillage zone is typically 0.25 to 0.3 m wide and 0.25 to 0.30 m deep. The soil surface between these strips is left undisturbed and the residue from the previous crop remain on the soil surface. The residue-covered area reaches 60-70%. Keeping residue on the surface helps prevent soil structure and reduce water loss from the so... T. Rátonyi, P. Ragán, D. Sulyok, J. Nagy, E. Harsányi, A. Vántus, N. Csatári |
67. Delineation of 'Management Classes' Within Non-Irrigated Maize Fields Using Readily Available Reflectance Data and Their Correspondence to Spatial Yield VariationMaize is grown predominantly for silage or gain in North Island, New Zealand. Precision agriculture allows management of spatially variable paddocks by variably applying crop inputs tailored to distinctive potential-yield limiting areas of the paddock, known as management zones. However, uptake of precision agriculture among in New Zealand maize growers is slow and limited, largely due to lack of data, technical expertise and evidence of financial benefits. Reflectance data of satellite and a... D.C. Ekanayake, J. Owens, A. Werner, A. Holmes |
68. Improving Yield Prediction Accuracy Using Energy Balance Trial, On-the-Go and Remote Sensing ProcedureOur long term experience in the ~23.5 ha research field since 2001 shows that decision support requires complex databases from each management zone within that field (eg. soil physical and chemical parameters, technological, phenological and meteorological data). In the absence of PA sustainable biomass production cannot be achieved. The size of management zones will be ever smaller. Consequently, the on the go and remote sensing data collection should be preferred. ... A. Nyéki , G. Milics, A.J. Kovács, M. Neményi, I. Kulmány, S. Zsebő |
69. Variety Effects on Cotton Yield Monitor CalibrationWhile modern grain yield monitors are able to harvest variety and hybrid trials without imposing bias, cotton yield monitors are affected by varietal properties. With planters capable of site-specific planting of multiple varieties, it is essential to better understand cotton yield monitor calibration. Large-plot field experiments were conducted with two southeast Missouri cotton producers to compare yield monitor-estimated weights and observed weights in replicated variety trials. Two replic... E. Vories, A. Jones, G. Stevens, C. Meeks |
70. Can Optimization Associated with On-Farm Experimentation Using Site-Specific Technologies Improve Producer Management Decisions?Crop production input decisions have become increasingly difficult due to uncertainty in global markets, input costs, commodity prices, and price premiums. We hypothesize that if producers had better knowledge of market prices, spatial variability in crop response, and weather conditions that drive crop response to inputs, they could more cost-effectively make profit-maximizing input decisions. Understanding the drivers of variability in crop response and designing accompanying management str... B.D. Maxwell, A. Bekkerman, N. Silverman, R. Payn, J. Sheppard, C. Izurieta, P. Davis, P.B. Hegedus |
71. Draft Privacy Guidelines and Proposal Outline to Create a Field-Scale Trial Data Repository for Data Collected by On-Farm NetworksImplementing better management practices in corn and soybeans that increase profitability and reduce pollution caused by the practices requires large numbers of field-scale, replicated trials. Numerous complex and often unmeasurable interactions among the environment, genetics and management at the field scale require large numbers of trials completed at the field scale in a systematic and uniform manner to enable calculation of probabilities that a practice will be an improvement compared wi... T. Morris, N. Tremblay |
72. An Economic-Theory-Based Approach to Management Zone DelineationIn both the academic and popular literatures on precision agriculture technology, a management zoneis generally defined as an area in a field within which the optimal input application strategy is spatially uniform. The characteristics commonly chosen to delineate management zones, both in the literature and in commercial practice, are yield and variables associated with yield. But microeconomic theory makes clear that economically optimal input application strategi... B. Edge |
73. 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 spee... A. Sharda, S. Badua, I. Ciampitti, R. Strasser, T.W. Griffin |
74. Investigate the Optimal Plot Length in On-Farm TrialsAgronomic researchers have recently begun running large-scale, on-farm field trials that employ new technologies that enable us to conduct hundreds of farm trials all over the world and, by extension, rigorous quantitative and data-centered analysis. The large-scale, on-farm trials follow traditional small-plot trials where the fields are divided into plots, and different treatments are randomly assigned to each plot. Over the past two years, researchers have been designing trials with ... A. Gong |
75. Using Deep Learning in Yield and Protein Prediction of Winter Wheat Based on Fertilization Prescriptions in Precision AgriculturePrecision Agriculture has been gaining interest due to the significant growth in the fields of engineering and computer science, hence leading to more sophisticated methods and tools to improve agricultural techniques. One approach to Precision Agriculture involves the application of mathematical models and machine learning to fertilization optimization and yield prediction, which is what this research focuses on. Specifically, in this work we report the results of predicting yield and protei... J. Sheppard, A. Peerlinck, B. Maxwell |
76. Can Unreplicated Strip Trials Be Used in Precision On-Farm Experiments?On-farm experiments are used to evaluate a wide variety of products ranging from pesticide and fertilizer rates to the installation of tile drainage. The experimental design for these experiments is usually replicated strip trials. Replication of strip trials is used to estimate experimental error, which is the basis for judging statistical significance of treatment effects. Another consideration for using strip trials is greater within-field variability than smaller fields us... G. Hatfield, G. Reicks, E. Carter |
77. eFields – An On-Farm Research Network to Inform Farm RecommendationsOn-farm research has been traditionally used to provide local, field-scale information about agronomic practices. Farmers tend to have more confidence in on-farm research results because they are perceived to be more relevant to their farm operations compared to small plot research results. In recent years, more farmers have been conducting on-farm studies to help evaluate practices and input decisions. Recent advances in precision agriculture technologies have stream-lined the on-... J.P. Fulton, E. Hawkins, R. Colley iii, K. Port, S. Shearer, A. Klopfenstein |
78. AI-based Precision Weed Detection and EliminationWeeds are a significant challenge in agriculture, competing with crops for resources and reducing yields. Addressing this issue requires efficient and sustainable weed elimination systems. This paper presents a comprehensive overview of recent advancements in weed elimination system development, focusing on innovative technologies and methodologies. Specifically, it details the development and integration of a weed detection and elimination system based on the CoreXY architecture, implemented... H. Kulhandjian, M. Kulhandjian, D. Rocha, B. Bennett |
79. AgDataBox-IA – Web Application with Artificial Intelligence for Agricultural Data Analysis in Precision AgricultureAgriculture has been continually evolving, incorporating hardware, software, sensors, aerial surveys, soil sampling for chemical, physical, and granulometric analysis (based on sample grids), and microclimatic data, leading to a substantial volume of data. This requires platforms to store, manage, and transform these data into actionable information for decision-making in the field. In this regard, Artificial Intelligence (AI) is the most widely used tool globally to mine and transform vast d... R. Sobjak, C.L. Bazzi, K. Schenatto, W.K. Oliveira, A.E. Menegasso |
80. 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, Hemicell... M. Barbosa, D. Duron, F. Rontani, G. Bortolon, B. Moreira, L. Oliveira, T. Setiyono, L. Shiratsuchi, R.P. Silva, K.H. Holland |
81. Drought Tolerance Assessment with Statistical and Deep Learning Models on Hyperspectral Images for High-throughput Plant PhenotypingDrought is an important factor that severely restricts blueberry growth, output and adversely impacts the desirable physiologic quality. Considering the challenges posed by climate change and erratic weather patterns, evaluating the drought tolerance of blueberry plants is not only vital for the agricultural industry but also for ensuring a consistent supply of these nutritious berries to consumers. Blueberry plants have a relatively ineffective water regulation mechanism due to their shallow... M. Rahman, S. Busby, A. Sanz-saez, S. Ru, T. Rehman |
82. A High-throughput Phenotyping System Evaluating Salt Stress Tolerance in Kale Plants Cultivated in Aquaponics EnvironmentsMonitoring plant growth in a controlled environment is crucial to make informed decisions for various management practices such as fertilization, weed control, and harvesting. Agronomic, physiological, and architectural traits in kale plants (Brassica oleracea) are important to producers, breeders, and researchers for assessing the performance of the plants under biotic and abiotic stresses. Traditionally, architectural, and morphological traits have been used to monitor plant growth. H... T. Rehman, M. Rahman, E. Ayipio, D. Lukwesa, J. Zheng, D. Wells, H.H. Syed |
83. 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 intell... G.N. Nocera santiago, P. Cisdeli magalhães, I. Ciampitti, L. Marziotte |
84. Towards a Digital Peanut Profile Board: a Deep Learning ApproachArtificial intelligence techniques, particularly deep learning, offer promising avenues for revolutionizing object detection and counting algorithms in the context of digital agriculture. The challenges faced by peanut farmers, particularly the precise determination of optimal maturity for digging, have prompted innovative solutions. Traditionally, peanut maturity assessment has relied on the Peanut Maturity Index (PMI), employing a manual classification process with the aid of a peanut profi... M.F. Freire de oliveira, B.V. Ortiz, J.B. Souza, Y. Bao, E. Hanyabui |
85. Utilizing Hyperspectral Field Imagery for Accurate Southern Leaf Blight Severity Grading in CornCrop disease detection using traditional scouting and visual inspection approaches can be laborious and time-consuming. Timely detection of disease and its severity over large spatial regions is critical for minimizing significant yield losses. Hyperspectral imagery has been demonstrated as a useful tool for a broad assessment of crop health. The use of spectral bands from hyperspectral data to predict disease severity and progression has been shown to have the capability of enhancing e... G. Vincent, M. Kudenov, P. Balint-kurti, R. Dean, C.M. Williams |
86. HOPSY: Harvesting Optimization for Production of Strawberry Using Real-time Detection with YOLOv8Optimizing the harvesting process presents a continuous challenge within the strawberry industry, especially during peak seasons when precise labor allocation becomes critical for efficiency and cost-effectiveness. The conventional method for addressing this issue has been hindered by an absence of real-time data regarding yield distribution, resulting in less-than-ideal worker assignments and unnecessary expenditures on labor. In response, a novel, portable, real-time strawberry detection sy... Z. Huang, W. Lee, N. Takkellapati |
87. Optimal Placement of Soil Moisture Sensors in an Irrigated Corn FieldPrecision agricultural practices rely on characterization of spatially and temporally variable soil and crop properties to precisely synchronize inputs (water, fertilizer, etc.) to crop needs; thereby enhancing input use efficiency and farm profitability. Generally, the spatial dependency range for soil water content is shorter near the soil surface compared to deeper depths, suggesting a need for more sampling locations to accurately characterize near-surface soil water content. However, det... D. Mandal, L. Longchamps, R. Khosla |
88. Real-time Seed Mapping Using Direct MethodsSeed distance estimations are critical for planter evaluation and the prediction of planting parameter performance. However, these estimations are typically not conducted in real-time. In this study, we propose a real-time seed mapping approach using cameras and computer vision networks, augmented by a Kalman filter for vehicle state estimation. This process involves the transformation of pixel coordinates into real-world coordinates. We conduct a comparative analysis between these estimates ... A. Sharda, R. Harsha chepally |
89. Incorporating Return on Investment for Profit-driven Management ZonesAdopting site-specific management practices such as profitability zones can help to stabilize long-term profit while also favoring the environment. Profitability maps are used to standardize data by converting variables into economic values ($/ha) for different cropping systems within a field. Thus, profitability maps can be used to define management zones from several years of data and show the regions within a field which are more profitable to invest in for production, or those that can be... A.A. Boatswain jacques, A.B. Diallo, A. Cambouris, E. Lord, E. Fallon |
90. Leveraging UAV-based Hyperspectral Data and Machine Learning Techniques for the Detection of Powderly Mildew in VineyardsThis paper presents the development and validation of machine learning models for the detection of powdery mildew in vineyards. The models are trained and validated using custom datasets obtained from unmanned aerial vehicles (UAVs) equipped with a hyperspectral sensor that can collect images in visible/near-infrared (VNIR) and shortwave infrared (SWIR) wavelengths. The dataset consists of the images of vineyards with marked regions for powdery mildew, meticulously annotated using LabelImg.&n... S. Bhandari, M. Acosta, C. Cordova gonzalez, A. Raheja, A. Sherafat |
91. 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 i... 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 |
92. Using Machine Vision to Build Field Maps of Forage Quality and the Need for Agriculture-specific Machine Vision NetworksMachine vision systems have truly come of age over the past decade. These networks are relatively simple to implement with systems such as YOLOv5 or the more recent YOLOv8. They are also relatively easy and computationally cheap to retrain to a custom data set, allowing for customization of these networks to new object detection and classification tasks. With this ease, it is no surprise that we are seeing an explosion of these networks and their application through all aspects of a... P. Nugent, J. Neupane |
93. Detection of Sorghum Aphids with Advanced Machine VisionSorghum aphid, Melanaphis sorghi (Theobald), became a significant pest concern due to the significant yield losses caused in the sorghum production region. Different management practices, including monitoring and applying insecticides, have been used to manage this invasive pest in sorghum. The most common management strategy consists of visual assessments of aphids on sorghum leaves to determine an economic threshold level to spray. However, because of their rapid reproduction,... I.A. Grijalva teran, B. Spiesman, N. Clark, B. Mccornack |
94. Sparse Coding for Classification Via a Locality Regularizer: with Applications to AgricultureHigh-dimensional data is commonly encountered in various applications, including genomics, as well as image and video processing. Analyzing, computing, and visualizing such data pose significant challenges. Feature extraction methods become crucial in addressing these challenges by obtaining compressed representations that are suitable for analysis and downstream tasks. One effective technique along these lines is sparse coding, which involves representing data as a sparse linear combination ... A. Tasissa, L. Li, J.M. Murphy |
95. 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 t... C. Hernandez, P. Kyveryga, A. Correndo, A. Prestholt, I. Ciampitti |
96. Obstacle-aware UAV Flight Planning for Agricultural ApplicationsThe use of unmanned aerial vehicles (UAVs) has emerged as one of the most important transformational tools in modern agriculture, offering unprecedented opportunities for crop monitoring, management, and optimization. To ensure effective and safe navigation in agricultural environments, robust obstacle avoidance capabilities are required to mitigate collision risks and to ensure efficient operations. Mission planners for UAVs are typically responsible for verifying that the vehicle is followi... K. Joseph, S. Pitla, V. Muvva |
97. Supervised Hyperspectral Band Selection Using Texture Features for Classification of Citrus Leaf Diseases with YOLOv8Citrus greening disease (HLB), a disease caused by bacteria of the Candidatus Liberibacter group, is characterized by blotchy leaves and smaller fruits. Causing both premature fruit drop and eventual tree death, HLB is a novel and significant threat to the Florida citrus industry. Citrus canker is another serious disease caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri) and causes economic losses for growers from fruit drops and blemishes. Citrus cank... Q. Frederick, T. Burks, P.K. Yadav, M. Dewdney, J. Qin, M. Kim |
98. 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 locati... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi |
99. RMAPs: an Integrated Tool to Delimitate Homogeneous Management ZonesManagement zones are one of the most studied methods in precision agriculture to optimize crop yield from the soil, plant, management, and climate input parameters. We present Rmaps, an R package that integrates soil and crop yield spatial variability using geostatistical methods and one-hidden-layer perceptron (OHLP) to identify how input parameters influence crop yield and delimitate homogenous zones. From georeferenced data of soil, plant, management, climate, and crop yield parameters, Rm... E. Erazo, C. Mosquera, O. Ochoa |
100. AI Enabled Targeted Robotic Weed ManagementIn contemporary agriculture, effective weed management presents a considerable challenge necessitating innovative solutions. Traditional weed control methods often rely on the indiscriminate application of broad-spectrum herbicides, giving rise to environmental concerns and unintended crop damage. Our research addresses this challenge by introducing an innovative AI-enabled robotic system designed to identify and selectively target weeds in real-time. Utilizing the advanced Machine Learning t... A. Balabantaray, S. Pitla |
101. AI-enabled 3D Vision System for Rapid and Accurate Tree Trunk Detection and Diameter EstimationHuanglongbing (HLB) is the major threat to citrus production in Florida. Imidacloprid and oxytetracycline injections were proven to be effective in controlling HLB. The total amount of imidacloprid and oxytetracycline needs to be injected for the tree depending on the trunk diameter. Therefore, precisely measuring trunk diameter is important to effectively control the HLB. However, manually injecting imidacloprid or oxytetracycline and measuring the trunk diameter is time-consuming and labor-... C. Zhou, Y. Ampatzidis |
102. Active Learning-based Measurements Prediction in Sparsely Observed Agricultural FieldsThe sustainability of farming methods relies on the quality of soil health. Rich soil supplies vital nutrients to plants. The soil structure and aggregation possess crucial physical attributes that facilitate the infiltration of water and air, as well as enable roots to explore. Long-term and extensive monitoring of soil data is crucial for obtaining important information into the water dynamics of the land surface. Soil moisture dynamics play a critical role in the hydrothermal process that ... D. Agarwal, A. Tharzeen, B. Natarajan |
103. 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 collec... 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 |
104. In-Field and Loading Crop: A Machine Learning Approach to Classify Machine Harvesting Operating ModeThis paper addresses the complex issue of classifying mode of operation (active, idle, stationary unloading, on-the-go unloading, turning) and coordinating agricultural machinery. Agricultural machinery operators must operate within a limited time window to optimize operational efficiency and reduce costs. Existing algorithms for classifying machinery operating modes often rely on heuristic methods. Examples include rules conditioned on machine speed, bearing angle and operational t... D. Buckmaster, J. Krogmeier, J. Evans, Y. Zhang, M. Glavin, D. Byrne, S.J. Harkin |
105. Estimating Real-time Soil Water Content (SWC) in Corn and Soybean Fields Using Machine Learning Models, Proximal Remote Sensing, and Weather DataSoil Water Content (SWC) is crucial for precise irrigation management, especially in center-pivot systems. Real-time estimation of SWC is vital for scheduling irrigation to prevent overwatering or underwatering. Proper irrigation yields benefits such as improved water efficiency, enhanced crop yield and quality, minimized environmental impact, optimized labor and energy costs, and improved soil health. Various in-situ techniques, such as Time-domain reflectometry (TDR), frequency-do... N. Chamara, Y. Ge, F. Bai |
106. Enhancing Seeding Efficiency: Evaluating Row Cleaners with Computer Vision in Precision AgricultureIn precision agriculture, the effective sowing of seeds is crucial but often hindered by challenges like hair pinning, low soil temperatures, and heavy residue on the soil surface. To address these issues, row cleaners are employed to clear the path for seeder opener discs, ensuring a clean, uniform trench for seed placement. This study examines the performance of various row cleaner models and introduces a novel method for their automatic, quantitative evaluation using computer vision techno... F. Sidharth, A. Sharda, B.G. Berretta |
107. Cotton Yield Estimation Using High-resolution Satellite Imagery Obtained from Planet SkySatSatellite images have been used to monitor and estimate crop yield. Over the years, significant improvements on spatial resolution have been made where ortho images can be generated at 30-centimeter resolution. In this study, we wanted to explore the potential use of Planet SKYSAT satellite system for cotton yield predictions. This system provided imagery data at 50 centimeters resolution, and we collected data 14 times during the season. The data were collected from two different cotton... M. Bhandari |
108. AIR-N: AI-Enabled Robotic Precision Nitrogen Management PlatformThe AI-Enabled Robotic Nitrogen Management (AIR-N) system is a versatile, cloud-based platform designed for precision nitrogen management in agriculture, targeting the reduction of nitrous oxide emissions as emphasized by the EPA. This end-to-end integrated system is adaptable to various cloud services, enhancing its applicability across different farming environments. AIR-N's framework consists of three primary components: a sensing layer for gathering data, a cloud layer where AI and ma... A. Kalra, S. Pitla, J.D. Luck |
109. Bird Welfare and Comfort in Poultry Coops Through Computations and AIBird welfare and comfort is very important inside poultry coops during transportation, especially during summer and winter months. The microenvironment inside a poultry coop resulting from hot/cold temperatures, relative humidity and heat production leads to complex scenarios affecting the bird welfare. The enthalpy comfort index (ECI) that relates to temperature, relative humidity was calculated to evaluate the poultry coop welfare that corresponds to bird welfare conditions (comfort; ... R. Pidaparti, A. Moghadham, H. Thippareddi |
110. Determining Desirable Swine Traits that Correlate to High Carcass Grades for Artificial Intelligence PredictionsWith the global population continuing to grow, there has been an increased stress applied to the agriculture industry to improve efficiency and yield. To achieve this goal within the cattle industry, selection and reproductive decisions have been lucrative aspects, both genetically and fiscally. Breeding animal selection impacts farms through passing on favorable market, reproductive, and temperament traits. The cattle industry has experienced genetic advancement due to the flexibility of art... A.N. Spina, J.P. Fulton, S.A. Shearer, T. Berger-wolf, D. Drewry |
111. Optimizing Soybean Management with UAV RGB and Multispectral Imagery: a Neural Network Method and Image ProcessingPrecision agriculture (PA) has emerged as a fundamental approach in contemporary agricultural management, aimed at maximizing efficiency in the use of resources and improving crop productivity. The transition to so-called "agriculture 4.0" represents a revolution in the way technology is applied in the field, with an emphasis on digital and automated solutions such as UAVs (Unmanned Aerial Vehicles). These devices offer new capabilities for capturing high-resolution images, enabling... F. Pereira de souza, L. Shiratsuchi, H. Tao, M. Acconcia dias, M. Barbosa, T. deri setiyono, S. Campos |
112. Premier Strategy Consulting - Sponsor Presentation... C. Zhu |
113. Advanced Classification of Beetle Doppelgängers Using Siamese Neural Networks and Imaging TechniquesThe precise identification of beetle species, especially those that have similar macrostructure and physical characteristics, is a challenging task in the field of entomology. The term "Beetle Doppelgängers" refers to species that exhibit almost indistinguishable macrostructural characteristics, which can complicate tasks in ecological studies, conservation efforts, and pest management. The core issue resides in their striking similarity, frequently confusing both experts and a... P.R. Armstrong, L.O. Pordesimo, K. Siliveru, A.R. Gerken, R.O. Serfa juan |
114. Spectral Imaging Deep Learning Mapper for Precision AgricultureWith the growing variety of RGB cameras, spectral sensors, and platforms like field robots or unmanned aerial vehicles (UAV) in precision agriculture, there is a demand for straightforward utilization of collected field data. In recent years, deep learning has gained significant attention and delivered impressive results in the realm of computer vision tasks, such as semantic segmentation. These models have also found extensive applications in research related to precision agriculture and spe... L. Thomas, B. Jakimow, A. Janz, P. Hostert, A. Lajunen |
115. Predicting Soybean Yield Using Remote Sensing and a Machine Learning ModelSoybean (Glycine max L.), a nutrient-rich legume crop, is an important resource for both livestock feed and human dietary needs. Accurate preharvest yield prediction of soybeans can help optimize harvesting strategies, enhance profitability, and improve sustainability. Soybean yield estimation is inherently complex because yield is influenced by many factors including growth patterns, varying crop physiological traits, soil properties, within-field variability, and weather conditions. The obj... M. Gardezi, O. Walsh, D. Joshi, S. Kumari, D.E. Clay, J. Rathore |