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1. A Step Towards Precision Irrigation: Plant Water Status Detection With Infrared ThermographyThe increasing demand for water all over the world calls for precision agriculture which accounts globally about 70 percent of all water withdrawal. Therefore, there is a need to optimizing water use efficiency and making the best use of available water for irrigation. Plant water status detection for advanced irrigation scheduling is frequently done by predawn leaf water potential (ΨPD) or leaf stomata conductance (gL) measurements. However, these measurements are time and labour consumi... S. Zia |
2. Timely, Objective, And Accurate Crop Area Estimations And Mapping Using Remote Sensing And Statistical Methods For The Province Of Prince Edward Island, CanadaThe provincial government of Prince Edward Island, Canada, required timely, objective, and accurate annual crop area statistics and mapping for 2006 to 2008. Consequently, Statistics Canada conducted a survey incorporating medium- resolution satellite imagery (10 to 30 m) and statistical survey methods. The objective was to produce crop area estimates with a coefficient of variation (CV) as a measure of accuracy, and to produce maps showing the distribution and location of different crops and... F. Bedard, G. Reichert, R. Dobbins, M. Pantel, J. Smith |
3. Estimation Of Sugar Beet Yield Brfore Harvesting Using Meteorological Data And Spot Satellite DataIn Japan, sugar beet is only cultivated in Hokkaido, the northernmost island. The area of sugar beet cultivation in Tokachi District is 30,000ha, which is equal to about 45% of the total national production area. Because sugar beet is suited to cool weather conditions, it is an important rotation crop in Hokkaido. The production of beet sugar in Hokkaido is about 640,000 tons, which is 75... C. Hongo, K. Niwa |
4. Low Cost High-resolution Aerial Photogrammetric Techniques For Precision Agriculture In Latin American CountriesOne of the first steps in precision agriculture is to obtain aerial images of an area of interest to determine soil units and management zones. Aerial and remote sensing information, digital elevation models and other spatial data are often inexistent in planning offices in Latin American countries and, up to now, enhancement and modifications have not been integrated into smaller scaled planning operation such as farming. High resolution remote sensing images from scanning satellites like Qu... J.S. Perret, O.E. arriaza, M.E. D, J. Aguilar |
5. Near Real-time Meter-resolution Airborne Imagery For Precision Agriculture: AerocamPrecision agriculture often relies on high resolution imagery to delineate the variability within a field. Airborne Environmental Research Observational Camera (AEROCam) was designed to meet the needs of agriculture producers, ranchers, and researchers, who require meter-solution imagery in a near real-time environment for rapid decision support. AEROCam was developed and operated through a unique collabor... X. Zhang, C.R. Streeter, H. Kim, D.R. Olsen |
6. Determination Of Crop Injury From Aerial Application Of Glyphosate Using Vegetation Indices And GeostatisticsInjury to crops caused by off-target drift of glyphosate can seriously reduce growth and yield, and is of great concern to farmers and aerial applicators. Determining an indirect method for assessing the levels and extent of crop injury could support management decisions. The objectives of this study were to evaluate multiple vegetation indices (VIs) as surrogate variables for glyphosate injury identification and to evaluate the combined use of Geostatistical methods and the VIs to asse... B. Ortiz, S.J. Thomson, Y. Huang, K. Reddy |
7. Sectioning And Assessment Remote Images For Precision Agriculture: The Case Of Orobanche Crenate In Pea CropThe software SARI® has been developed to implement precision agriculture strategies through remote sensing imagery. It is written in IDL® and works as an add-on of ENVI®. It has been designed to divide remotely sensed imagery into “micro-images”, each corresponding to a small area (“micro-plot”), and to determine the quantitative agronomic and/or environmental biotic (i.e. weeds, pathogens) and/or non-biotic (i.e. nutrient levels) indicator... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, M. Gomez-casero, J.M. Pe, M. Jurado-exp, F. Lopez-granados, I. Castillejo-gonz, A. Garc |
8. Multi, Super Or Hyper Spectral Data, The Right Way From Research Toward Application In AgricultureRemote sensing provides opportunities for diverse applications in agriculture. One consideration of maximizing the utility of these applications, is the need to choose the most efficient spectral resolution. Picking the optimal spectral resolutions (multi, super or hyper) for a specific application is also influenced by other factors (e.g., spatial and temporal resolutions) of the utilized device. This work focuses mainly ... D.J. Bonfil, I. Herrmann, A. Pimstein, A. Karnieli |
9. Weeds Detection By Ground-level Hyperspectral ImagingWeeds are a severe pest in agriculture, causing extensive yield loss. Weed control of grass and broadleaf weeds is commonly performed by applying selective herbicides homogeneously all over the field. As presented in several studies, applying the herbicide only where needed has economical as well as environmental benefits. Combining remote sensing tools and techniques with the concept of precision agriculture has the potential to auto... U. Shapira , I. Herrmann, A. Karnieli, D.J. Bonfil |
10. Assessment Of Field Crops Leaf Area Index By The Red-edge Inflection Point Derived From Venus BandsThe red-edge region of leaves spectrum (700-800 nm) corresponds to the spectral region that connects the chlorophyll absorption in the red and the amplified reflectance caused by the leaf structure in the near infrared (NIR) parts of the spectrum. At the canopy level, the inflection point of the red-edge slope is influenced by the plant’s condition that is related to several properties, including Leaf Area Index (LAI) and plant nutritional ... I. Herrmann, A. Pimstein, A. Karnieli, Y. Cohen, V. Alchanatis , D.J. Bonfil |
11. Site-specific Management For Biomass Feedstock Production: Development Of Remote Sensing Data Acquisition SystemsEfficient biomass feedstock production supply chain spans from site-specific management of crops on field to the gate of biorefinery. Remote sensing data acquisition systems have been introduced for site-specific management, which is a part of the engineering solutions for biomass feedstock production. A stand alone tower remote sensing platform was developed to monitor energy crops using multispectral imagery. The sensing system was capable of collecting RGB and CIR images during the crop gr... T. Ahamed, L. Tian, Y. Zhang, Y. Xiong, B. Zhao, Y. Jiang, K. Ting |
12. Inversion Of Vertical Distribution Of Chlorophyll Concentration By Canopy Reflectance Spectrum In Winter WheatThe objective of this study was to investigate the inversion of foliage chlorophyll concentration(Chl) vertical-layer distribution by bidirectional reflectance difference function (BRDF) data, so as to provide guidance on the application of fertilizer. The ratio of transformed chlorophyll absorption reflectance index (TCARI) to optimized soil adjusted vegetation index (OSAVI) was named as canopy chlorophyll inversion index (CCII) ... W. Huang, C. Zhao |
13. Remote Estimation Of Gross Primary Production In MaizeThere is a growing interest in the estimation of gross primary productivity (GPP) in crops due to its importance in regional and global studies of carbon balance. We have found that crop GPP was closely related to its total chlorophyll content, and thus chlorophyll can be used as a proxy of GPP in crops. In this study, we tested the performance of various vegetation indices for estimating GPP. The indices were derived from spectral data collected remotely but at close-range over a period of e... A.A. Gitelson |
14. Artificial Neural Network Techniques To Predict Orange Spotting Disease In Oil PalmLarge-Scale oil palm plantations require timely detection of disease symptoms to enable effective intervention. Orange spotting is an emerging disease that significantly reduces oil palm productivity. Remote sensing technology offers the means to detect crop biophysical properties, including crop stress, in a cost effective and non destructive manner. In this study, different portable sensors were used to measure spectral reflectance and chlorop... S. Liaghat, S.K. Balasundram |
15. Comparison Of Different Vegetation Indices And Their Suitability To Describe N-uptake In Winter Wheat For Precision FarmingTo avoid environment pollution and to minimize the costs of using mineral fertilizers an efficient fertilization system, tailored to the plant needs becomes more and more important. For that, the essential information can be determined by detecting certain crop parameters, like dry matter of the plant biomass above ground, N-content and N-uptake. By using fluorescence and reflectance measurements of the canopy and the mathematical analysis these parameters are appreciable. In three ... M. Strenner, F. Maidl |
16. Use Of Spectral Distance, Spectral Angle, And Plant Abundance Derived From Hyperspectral Imagery To Characterize Crop Growth VariationVegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band or three-band VIs based on selected waveleng... C. Yang |
17. Soybean Canopy Response To Charcoal Rot In Arkansas: Observations Using Crop Circletm (ACS-470).Charcoal Rot caused by Macrophomina phaseolina is a problem to soybean production, especially in hot and dry areas of southern US. As an approach to develop a fast assessment method of this soil-borne disease, soybean canopy reflectance was recorded with an active optical sensor, the Crop CircleTM ACS-470 in 2009 from a microplot field in Fayetteville, Arkansas. The microplot experiment was designed as a completely randomized factorial experiment with four cultivars, two ino... S.S. Kulkarni, M. Doubledee, S.G. Bajwa, J.C. Rupe |
18. The Use Of A Ground Based Remote Sensor For Winter Wheat Grain Yield Prediction In Northern PolandThe aim of the research was to investigate if algorithms developed for winter wheat, cv. Trend, yield predictions, based on ground measured GNDVI, differ significantly between 2 sequent years. The research was conducted in Pomerania, northern Poland (54° 31' N 17° 18' E) on sandy loam soils. The strip-trial design was used to compare the effect of 6 N treatments: 0, 50, 100, 150, 200 and 250 kg ha-1, applied as one dose at the b... S.M. Samborski, D. Gozdowski, S.E. Dobers |
19. Assessment Of Pod Ceal Dc™ Effect On Grain Yield In Beans Using Multi-spectral Satellite Imagery And Yield DataPod Ceal DC™ from BrettYoung creates an elastic membrane over pods in canola, beans etc., which results in controlling shatter before combining. To carry out this on-farm experiment, an irrigated field was divided in two parts according to the yielding potential and topographical characteristics to ensure equal conditions for both variants of the experiment. Grain beans were grown in the field using conventional technology. Pod Ceal DC™ was applied three weeks before harvesting on... A. Melnitchouck |
20. Active Sensor For Real-time Determination Of Soil Organic MatterSoil organic matter influences chemical and physical properties in the root zone as well as soil biological activity and plant vigor. As such, it is reasonable to assume that there are probably opportunities for producers to incorporate soil organic matter concentration information into their management decisions. However, soil organic matter is usually notoriously variable within fields. An active sensor based on in-soil reflectance was developed to provide apparent real-tim... J. Schepers, K.H. Holland |
21. Management Of Remote Imagery For Precision AgricultureSatellite and airborne remotely sensed images cover large areas, which normally include dozens of agricultural plots. Agricultural operations such as sowing, fertilization, and pesticide applications are designed for the whole plot area, i.e. 5 to 20 ha, or through precision agriculture. This takes into account the spatial variability of biotic and of abiotic factors and uses diverse technologies to apply inputs at variable rates, fitted to the needs of each small defined area, i.e. 25 to 200... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, J.M. Pe, M. Jurado-exp, I. Castillejo-gonz, A. Garc, F. Lopez-granados, L. Prassack |
22. Multisensor Data Fusion Of Remotely Sensed Imagery For Crop Field MappingA wide variety of remote sensing data from airborne hyperspectral and multispectral images is available for site-specific management in agricultural application and production. Aerial imaging system may offer less expensive and high spatial resolution imagery with Near Infra-Red, Red, Green and Blue spectral wavebands. Hyperspectral sensor provides hundreds of spectral bands. Multisensor data fusion provides an effective paradigm for remote sensing applications by sy... Y. Lan, H. Zhang, C. Yang, D. Martin, R. Lacey, Y. Huang, W.C. Hoffmann, P. Moulton |
23. Apparent Electrical Conductivity Calibration In Semiarid Soils: Ion-pair CorrectionThe electromagnetic induction sensor (EM38DD) is a field proven portable sensor for rapid measurement of the apparent electrical conductivity (ECa) of soils. Calibration with the electrical conductivity of saturation paste extracts is the most widely used method to correlate ECa with the effective electrical conductivity (ECe). A drawback of this method is the formation of ion pairs in the high ionic strength saturated paste extracts, which effectively decreases the measured ECe, leading to t... X. Amakor, A.R. Jacobson, G.E. Cardon, A. Hawks, W. Barnes |
24. Nitrogen And Water Stress Impacts Hard Red Spring Wheat (Triticum Aestivum) Canopy ReflectanceRemote sensing-based in-season N recommendations have been proposed as a technique to improve N fertilizer use efficiency. Remote sensing estimation of South Dakota hard red spring wheat N requirements needs assessment. Research objectives were: (1) determine the effect of an in-season N application on grain yield, yield loss to nitrogen stress (YLNS), and grain protein; and (2) assess if remote sensing collected at different growth stages may be used to predict yie... C.L. Reese, D.E. Clay, D.L. Beck, S.A. Clay, D.S. Long, M. Shahinian |
25. Using A Surface Energy Model (reset) To Determine The Spatial Variability Of ET Within And Between Agricultural FieldsRemote sensing algorithms are currently being used to estimate regional surface fluxes (e.g. evapotranspiration (ET)). Many of these surface energy balance models use information derived from satellite imagery such as aircraft, Landsat, AVHRR, ASTER, and MODIS to estimate ET. The remote sensing approach to estimating ET provides advantages over traditional methods. One of the most important advantages is that it can provide estimates of actual ET for each pixel in the image. Most conventional... L. Garcia, A. Elhaddad |
26. Development And Evaluation Of A Leaf Monitoring System For Continuous Measurement Of Plant Water Status In Almond And Walnut CropsAbstract: Leaf temperature measurements using handheld infrared thermometers have been used to predict plant water stress by calculating crop water stress index (CWSI). However, for CWSI calculations it is recommended to measure canopy temperature of trees under saturated, stressed and current conditions simultaneously, which is not very practical while using handheld units. An inexpensive, easy to use sensing system was developed to predict plant water status for tree crops by ... F. Rojo, J. Roach, R. Coates, S. Upadhyaya, M. Delwiche, C. Han, R. Dhillon |
27. Soil Mapping And Modeling On Twenty-Five Ingredients Using A Real-Time Soil SensorVisible and near-infrared spectroscopy is an effective measurement method for estimating many soil ingredients at once. In precision agriculture, rapid, non-destructive, cost-effective and convenient soil analysis techniques are needed for soil management, crop quality control using fertilizer, manure and compost, and variable-rate input for soil variability in a field. We obtained Twenty-five calibration models based on Vis-NIR (305 - 1700 nm) underground soil ... M. Kodaira, S. Shibusawa |
28. Suitability Of Crop Canopy Sensors For Determining Irrigation Differences In MaizeWater is the most limiting factor for agricultural production in the semiarid environment of the western Great Plains of the United States. Dry climate conditions combined with a large availability of ground water has led to crop systems that are dependent on irrigation for maximum yields. An increased emphasis on water is forcing users to find new ways to increase the efficiency of water used for agriculture. Crop canopy sensors may have the potential to deter... G. Kruger, S. Van donk, T.M. Shaver |
29. Visible And Near-Infrared Spectroscopy For Monitoring Potentially Toxic Elements In Reclaimed Dumpsite Soils Of The Czech RepublicDue to rapid economic development, high levels of potentially harmful elements and heavy metals are continuously being released into the brown coal mining dumpsites of the Czech Republic. Elevated metal contents in soils not only dramatically impact the soil quality, but also due to their persistent nature and long biological half-lives, contaminant elements can accumulate in the food chain and can eventually endanger human health. Conventional methods for investigating potentia... L. Borùvka, M. Saberioon, R. Vašát, A. Gholizadeh |
30. Evaluation Of The Temporal And Operational Stability Of Apparent Soil Electrical Conductivity MeasurementsMeasuring apparent soil electrical conductivity (ECa), using galvanic contact resistivity (GCR) and electromagnetic induction (EMI) techniques is frequently used to implement site-specific crop management. Various research projects have demonstrated the possibilities for significant changes in the measured quantities over time with relatively stable spatial structure representations. The objective of this study was to quantify the effects of temporal drift and operational noise for three... V.I. Adamchuk, A. Mat su |
31. Development Of An On-The-Spot Analyzer For Measuring Soil Chemical PropertiesProximal soil sensing (PSS) is a growing area of research and development focusing on the use of sensors to obtain information on the physical, chemical and biological attributes of soil when they are placed in contact with, or at a distance of less than 2 m, from the target. These sensor systems have been used to 1) make measurements at specific locations, 2) produce a set of measurements related to soil depth profiles, or 3) monitor changes in soil properties over time. In eac... V.I. Adamchuk, N. Dhawale, F. Rene-laforest |
32. Measuring And Mapping Sugarcane GapsSugarcane is an important crop in tropical regions of the world and especially for Brazil, the largest sugar supplier in the market, also running a domestic fleet of flex-fuel driven vehicles based on ethanol. Site specific production management can impact sugarcane production by increasing yield and reducing cost. Sugarcane fields are planted each five years, in average, and an important parameter that is measured after the planting operation is the gaps caused by problems during planti... J.P. Veiga, D.S. Cavalcante, J.P. Molin |
33. Development Of Online Soil Profile Sensor For Variable Depth TillageIntroduction First introduced in the early 1990s, precision agriculture technologies, or site-specific management, were considered by many to be perhaps the most significant development in production agriculture focused on improving farm profitability. The initial focus was on fertility, and treating the variability that we all knew existed from our experiences with soil sampling. However, to a large extent this application stil... A.B. Tekin, H. Yalcin |
34. 3-Dimension Reconstruction Of Cactus Using Multispectral ImagesUsing 3D reconstruction result to investigate plant morphology has been a focus of virtual plant. And multispectral imaging has proved to carried biological information in quite a lot work. This paper present a idea to investigate chlorophyll spatial variability of cactus using a bunch of multispectral images. 46 multispectral images are taken at equally distributed angles surrounding the tree and have over 80% overlap. Structure from motion approach has been u... F. Liu, Y. He, Y. Zhang, L. Tan, Y. Zhang, L. Jiang |
35. A Method For Sampling Scab Spots On Apple Leaves In The Orchard Using Machine VisionIntroduction One of the largest threats in apple orchards is scab. Current procedures involve models based on weather data that predict the likelihood of scab attacks. In case of alarm the orchard is sprayed with preventive pesticides and this typically happens 25-30 times per season. The scab attacks the leaves and stays on fallen leaves that reinfect the trees with rainwater, making it an advantage to include a-priori knowledge on previous... M.G. Bertelsen, K. Nielsen, M.R. Nielsen |
36. Using A Potable Spectroradiometer For In-Situ Measurement Of Soil Properties In A Slope Citrus FieldIn precision agriculture, rapid, non-destructive, cost-effective and convenient soil analysis techniques are needed for crop and soil management. However, the spatial variability of soil properties is consider to be high cost and time consuming to characterize using traditional soil analysis method. To achieve cost and time reduction, the potential benefits of in-situ measurement of soil spectra have been recognized. ... S. Shibusawa, H. Umeda, K. Usui, M. Kodaira, Q. Li |
37. Rapid Sensing For Water Stress Detection In Foxtail Millet (Setaria Italica)In recent years, the drought conditions due to changing climate patterns have adversely affected the U.S. agriculture. The 2012 drought that damaged major crops in Midwest was one of the most severe in last 25 years. It has resulted in losses of production, revenue, livestock and jobs, and has increased food prices. Under these circumstances, farmers are focused to use the water resources carefully. The researchers are working together to develop new crop varieties resistant to ... S. Sankaran, M. Wang, P. Ellsworth, A. Cousins |
38. Field-Based High-Throughput Phenotyping Approach For Soybean Plant ImprovementThe continued development of new, high yielding cultivars needed to meet the world’s growing food demands will be aided by improving the technology to rapidly phenotype potential cultivars. High-throughput phenotyping (HTP) is essential to maximize the greatest value of genetics analysis and to better understand the plant biology and physiology in view of a “Feed the World in 2050” theme. Field-based high-throughput&nb... L. Li, D. Jiang, R.P. Campos, Z. Lu, L.F. Tian |
39. Multivariate Geostatistics As A Tool To Estimate Physical And Chemical Soil Properties With Reduced Sampling In Area Planted With SugarcanePrecision Agriculture (PA) can be described as a set of tools and techniques applied to agriculture in order to enable localized production management, considering the spatial and temporal variability of crop fields. Among the numerous existing tools, one of the most important ones is the use of geostatistics, whose main objective is the description of spatial patterns and estimation data in non-sampled places. Nowadays, one of the most limiting factors to t... G.M. Sanches, P.S. Graziano magalhaes, H.C. Franco, A.Z. Remacre |
40. Evaluating Leaf Fluorescence Sensor Dualex 4 For Estimating Rice Nitrogen Status In Northeast ChinaReal-time non-destructive diagnosis of crop nitrogen (N) status is crucially important for the success of in-season site-specific N management. Chlorophyll meter (CM) has been commonly used to non-destructively estimate crop leaf chlorophyll concentration, and indirectly estimate crop N status. Dualex 4 is a newly developed leaf fluorescence sensor that can estimate both leaf chlorophyll concentration and polyphenolics, especially flavonoids. When N is deficient, N stress can in... W. Yu, Y. Miao, S. Hu, J. Shen, H. Wang |
41. Selection Of Fluorescence Indices For The Proximal Sensing Of Single And Multiple Stresses In Sugar BeetThe use of fluorescence indices for sensing the impact of abiotic and biotic stresses in agricultural crops is well documented in the literature. Pigment fluorescence gives a precise picture about the plant physiology and its changes following the occurrence of stresses. In general, alterations in such optical signals is caused either by the stress-induced accumulation of one or more fluorophores, or the degradation of specific molecules like chlorophyll. Unfortunately, many str... G. Leufen, G. Noga, M. Hunsche |
42. 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 irrigati... K.R. Thorp, J.W. White, M.M. Conley, J. Mon, K.F. Bronson |
43. Prediction Of Cation Exchange Capacity Using Visible And Near Infrared SpectroscopyCation exchange capacity (CEC) of the soil is a measure of the soil ability to hold positively charged ions and is an important indicator of soil physicochemical characteristic. It is an important property for site specific management of soil nutrients in precision agriculture. The conventional analytical methods used for the determination of CEC are expensive, difficult and time consuming, because different cations must be extracted and determined. Visible and near infrared (vis-NIR) sp... Y. Ulusoy, Z. Tümsavas, A.M. Mouazen, Y. Tekin |
44. Hand-Held Sensor For Measuring Crop Reflectance And Assessing Crop Biophysical CharacteristicsCrop vigor is difficult enough to define, let alone characterize and conveniently quantify. The human eye is particularly sensitive to green light, but quantifying subtle differences in plant greenness is subjective and therefore problematic in terms of making definitive management decisions. Plant greenness is one component of crop vigor and leaf area index or the relative ability o... J.S. Schepers, K.H. Holland |
45. Airborne Active Optical Sensors (AOS) For Photosynthetically-Active Biomass Sensing: Current Status And Future OpportunitiesThe first published deployment of an active optical reflectance sensor (AOS) in a low-flying aircraft in 2009 catalyzed numerous developments in both sensor development and sensor platform integration. Integral to these sensors is a modulated light source composed of high power LED technology that emits high radiance polychromatic light. The sensor easily mounts to agricultural aircraft and can sense agricultural landscapes at altitudes from a few meters to altitudes exceeding 40 meters ... K.H. Holland, D.W. Lamb |
46. Seeding and Planting Plots for Crop Performance Evaluation Using Gps-rtk Auto SteeringCrop performance evaluation plots are seeded both on and off the University of Nebraska West Central Research and Extension Center. Plots off the Center must match the producer’s rows for pesticide application, cultivation, ditching, irrigation, fertilization and any other operations performed in the fields. With row crops the producer blank-plants the plot area before we can follow up with planting the plots. This means that we have to wait for the producer to plant in the field. Blank... R.N. Klein, J.A. Golus, A.S. Cox |
47. Spotweeds: a Multiclass UASs Acquired Weed Image Dataset to Facilitate Site-specific Aerial Spraying Application Using Deep LearningUnmanned aerial systems (UASs)-based spot spraying application is considered a boon in Precision Agriculture (PA). Because of spot spraying, the amount of herbicide usage has reduced significantly resulting in less water contamination or crop plant injury. In the last demi-decade, Deep Learning (DL) has displayed tremendous potential to accomplish the task of identifying weeds for spot spraying application. Also, most of the ground-based weed management technologies have relied on DL techniqu... N. Rai, Y. Zhang, J. Quanbeck, A. Christensen, X. Sun |
48. A Generative Adversarial Network-based Method for High Fidelity Synthetic Data AugmentationDigital Agriculture has led to new phenotyping methods that use artificial intelligence and machine learning solutions on image and video data collected from lab, greenhouse, and field environments. The availability of accurately annotated image and video data remains a bottleneck for developing most machine learning and deep learning models. Typically, deep learning models require thousands of unique samples to accurately learn a given task. However, manual annotation of a large dataset will... S. Sridharan, S. Sornapudi, Q. Hu, S. Kumpatla, J. Bier |
49. Meta Deep Learning Using Minimal Training Images for Weed Classification in Wild BlueberryDeep learning convolutional neural networks (CNNs) have gained popularity in recent years for their ability to classify images with high levels of accuracy. In agriculture, they have been applied for disease identification, crop growth monitoring, animal behaviour tracking, and weed classification. Datasets traditionally consisting of thousands of images of each desired target are required to train CNNs. A recent survey of Nova Scotia wild blueberry (Vaccinium angustifolium Ait.) fie... P.J. Hennessy, T.J. Esau, A.W. Schumann, A.A. Farooque, Q.U. Zaman, S.N. White |
50. Generation of Site-specific Nitrogen Response Curves for Winter Wheat Using Deep LearningNitrogen response (N-response) curves are tools used to support farm management decisions. Conventionally, the N-response curve is modeled as an exponential function that aims to identify an important threshold for a given field: the economic optimum point. This is useful to determine the nitrogen rate beyond which there is no actual profit for the farmers. In this work, we show that N-response curves are not only field-specific but also site-specific and, as such, economic optimum points sho... G. Morales, J.W. Sheppard, A. Peerlinck, P. Hegedus, B. Maxwell |
51. Real-time Detection of Picking Region of Ridge Planted Strawberries Based on YOLOv5s with a Modified NeckRobotic strawberry harvesting requires machine vision system to have the ability to detect the presence, maturity, and location of strawberries. Strawberries, however, can easily be bruised, injured, and even damaged during robotic harvest if not picked properly because of their soft surfaces. Therefore, it is important to cut or pick the strawberry stems instead of picking the fruit directly. Additionally, real-time detection is critical for robotic strawberry harvesting to adapt to the chan... Z. He, K. Manoj, Q. Zhang, S. Kshetri |
52. Predicting Below and Above Ground Peanut Biomass and Maturity Using Multi-target RegressionPeanut growth and maturity prediction can help farmers and breeding programs improving crop management. Remote sensing images collected by satellites and drones make possible and accurate crop monitoring. Today, empirical relations between crop biomass and spectral reflectance could be used for prediction of single variables such as aboveground crop biomass, pod weight (PW), or peanut maturity. Robust algorithms such as multioutput regression (MTR) implemented through multioutput random fores... M.F. Oliveira, F.M. Carneiro, M. Thurmond, M.D. Del val, L.P. Oliveira, B. Ortiz, A. Sanz-saez, D. Tedesco |
53. From Fragmented Data to Unified Insights: Leveraging Data Standardization Tools for Better Collaboration and Agronomic Big Data AnalysisThe quantity and scope of agronomic data available for researchers in both industry and academia is increasing rapidly. Data sources include a myriad of different streams, such as field experiments, sensors, climatic data, socioeconomic data or remote sensing. The lack of standards and workflows frequently leads agronomic data to be fragmented and siloed, hampering collaboration efforts within research labs, university departments, or research institutes. Researchers and businesses therefore ... S. Sela |
54. Coupling Machine Learning Algorithms and GIS for Crop Yield Predictions Based on Remote Sensing Imagery and Topographic IndicesIn-season yield prediction can support crop management decisions helping farmers achieve their yield goals. The use of remote sensing to predict yield it is an alternative for non-destructive yield assessment but coupling auxiliary data such as topography features could help increase the accuracy of yield estimation. Predictive algorithms that can effectively identify, process and predict yield at field scale base on remote sensing and topography still needed. Machine learning could be an alt... M.F. Oliveira, G.T. Morata, B. Ortiz, R.P. Silva, A. Jimenez |
55. A Framework for Imputation of Missing Parts in UAV Orthomosaics Using Planetscope and Sentinel-2 DataIn recent years, the emergence of Unmanned Aerial Vehicles (UAV), also known as drones, with high spatial resolution, has broadened the application of remote sensing in agriculture. However, UAV images commonly have specific problems with missing areas due to drone flight restrictions. Data mining techniques for imputing missing data is an activity often demanded in several fields of science. In this context, this research used the same approach to predict missing parts on orthomosaics obtain... F.R. Pereira, A.A. Dos reis, R.G. Freitas, S.R. Oliveira, L.R. Amaral, G.K. Figueiredo, J.F. Antunes, R.A. Lamparelli, E. Moro, N.D. Pereira, P.S. Magalhães |
56. Identifying Key Factors Influencing Yield Spatial Pattern and Temporal Stability for Management Zone DelineationManagement zone delineation is a practical strategy for site-specific management. Numerous approaches have been used to identify these homogenous areas in the field, including approaches using multiple years of historical yield maps. However, there are still knowledge gaps in identifying variables influencing spatial and temporal variability of crop yield that should be used for management zone delineation. The objective of this study is to identify key soil and landscape properties affecting... L.N. Lacerda, Y. Miao, K. Mizuta, K. Stueve |
57. Strawberry Pest Detection Using Deep Learning and Automatic Imaging SystemStrawberry growers need to monitor pests to determine the options for pest management to reduce damage to yield and quality. However, manually counting strawberry pests using a hand lens is time-consuming and biased by the observer. Therefore, an automated rapid pest scouting method in the strawberry field can save time and improve counting consistency. This study utilized six cameras to take images of the strawberry leaf. Due to the relatively small size of the strawberry pest, six cam... C. Zhou, W. Lee, A. Pourreza, J.K. Schueller, O.E. Liburd, Y. Ampatzidis, G. Zuniga-ramirez |
58. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical DataBayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri |
59. Automated Lag Phase Detection in Wine GrapesCrop yield estimation, an important managerial tool for vineyard managers, plays a crucial role in planning pre/post-harvest operations to achieve desired yield and improve efficiency of various field operations. Although various technological approaches have been developed in the past for automated yield estimation in wine grapes, challenges such as cost and complexity of the technology, need of higher technical expertise for their operation and insufficient accuracy have caused major concer... P. Upadhyaya, M. Karkee, X. Zhang, S. Kashetri |
60. Supervised Feature Selection and Clustering for Equine Activity RecognitionIn this paper we introduce a novel supervised algorithm for equine activity recognition based on accelerometer data. By combining an approach of calculating a wide variety of time-series features with a supervised feature significance test we can obtain the best suited features using just 5 labeled samples per class and without requiring any expert domain knowledge. By using a simple cluster assignment algorithm with these obtained features, we get a classification algorithm that achieves a m... T. De waele, D. Peralta, A. Shahid, E. De poorter |
61. Increasing Precision Irrigation Efficacy for Row Crop Agriculture Through the Use of Artificial IntelligenceThe agricultural sector is the largest consumer of the world’s available fresh water resources. With fresh water scarcity increasing worldwide, more efficient use for irrigation water is necessary. Precision irrigation is described as the application of water to meet crop needs of a specific area, at the right amount and at the time that is optimum for crop health and management objectives. Irrigation becomes increasingly efficient through the use of precision irrigation tools. Howe... E. Bedwell |
62. In-season Nitrogen Prediction Evaluation Using Airborne Imagery with AI Techniques in Commercial Potato ProductionIn modern agriculture, timely and precise nitrogen (N) monitoring is essential to optimize resource management and improve trade benefits. Potato (Solanum tuberosum L.) is a staple food in many regions of the world, and improving its production is inevitable to ensure food security and promote related industries. Traditional methods of assessing nitrogen are labour-intensive, time-consuming, and require subjective observations. To address these limitations, a combination of multispec... B. Javed, A. Cambouris, M. Duchemin, L. Longchamps, P.S. Basran, S. Arnold, A. Fenech, A. Karam |
63. Securing Agricultural Imaging Data in Smart Agriculture: a Blockchain-based Approach to Mitigate Cybersecurity Threats and Future InnovationsSmart agriculture (SA) is a new technology that combines the Internet of Things (IoT) with a variety of smart devices, such as drones, unmanned ground vehicles (UGVs), and computer systems. The integration of technology improvements in SA has led to an increase in cybersecurity concerns, specifically pertaining to the protection of sensitive agricultural image data. It’s necessary to better understand SA network systems; establish stronger network structures; identify different types an... M. Alahe, S. Gummi, J.O. Kemeshi, Y. Chang |
64. X-ray Imaging in Breeding and Harvesting ProcessesThe application of X-ray technology has a long tradition in different medical and technical fields. Compared to other sensor systems, its advantages lie in the capability to reveal structures within objects non-destructively. The analysis of X-ray images with image processing methods is applied for quality control, the detection of foreign objects or damages and other anomalies (e.g. in organs or bones). Until recently, the application of X-ray was mainly constrained to stationary application... M. Weule, E. Hufnagel, J. Claussen, A. Berghaus, S. Burkhart, P. Noack, S. Gerth |
65. Emerging Megatrends of Sustainable Nutrient Management Research in Sub-saharan AfricaAfrica has the 12th highest population growth rates in the world, which may double by 2050; and have bio-physical constraints which impinge on development, that need to be addressed. This ever-increasing human population demands corresponding increase in food production, where low nutrient use and management is a critical challenge. Most research conducted by African scientists are rarely used in decision-making, because they are not properly aligned with the needs of decision-makers due to w... V. Aduramigba-modupe, K. Frimpong |
66. AgGateway Traceability API – The Foundation to Track Raw Agricultural CommoditiesThere is increasing demand for food traceability, ranging from consumers wanting to know where their food comes from (GMO, organic, climate-smart commodities), to manufacturers of agricultural inputs wanting to know the effectiveness of their products as used by farmers. Existing traceability requirements focus on the supply chain of goods packaged from their origin to retail grocery stores, with regulations provided by the Food Safety Modernization Act (FSMA) from the US Food and Drug Admini... S.T. Nieman, J. Tevis, B.E. Craker |
67. Prediction of Field-scale Evapotranspiration Using Process Based Modeling and Geostatistical Time-series InterpolationIrrigation scheduling depends on the combination of evaporative demand from the atmosphere, spatial and temporal heterogeneity in soil properties and changes in crop canopy during a growing season. This on-farm trial is based on data collected in 72-acre processing tomato field in Central Valley of California. The Multiband Spectrometric Arable Mark 2 sensors at three different locations in the field. Multispectral and thermal imagery provided by Ceres Imaging were collected eight times durin... G. Jha, F. Nazrul, M. Nocco, M. Pagé fortin, B. Whitaker, D. Diaz, A. Gal, R. Schmidt |
68. Within Field Cotton Yield Prediction Using Temporal Satellite Imagery Combined with Deep LearningCrop yield prediction at the field scale plays a pivotal role in enhancing agricultural management, a vital component in addressing global food security challenges. Regional or county-level data, while valuable for broader agricultural planning, often lacks the precision required by farmers for effective and timely field management. The primary obstacle in utilizing satellite imagery to forecast crop yields at the field level lies in its low temporal and spatial resolutions. This study aims t... R. Karn, O. Adedeji, B.P. Ghimire, A. Abdalla, V. Sheng, G. Ritchie, W. Guo |
69. Assessing Soybean Water Stress Patterns and ENSO Occurrence in Southern Brazil: an in Silico ApproachWater stress (WS) is one of the most important abiotic stresses worldwide, responsible for crop yield penalties and impacting food supply. The frequency and intensity of weather stresses are relevant to delimitating agricultural regions. In addition, El Nino Southern Oscillation (ENSO) has been employed to forecast the occurrence of seasonal WS. Lastly, planting date and cultivar maturity selection are key management strategies for boosting soybean (Glycine max (L.) Merr.) y... A. Carcedo, L.F. Antunes de almeida, T. Horbe, G. Corassa, L.P. Pott, I. Ciampitti, G.D. Hintz, T. Hefley, R.A. Schwalbert, V. Prasad |
70. The Evaluation of Spatial Response to Potassium in SoybeansIn agriculture, the nutrients that are in the largest demand are nitrogen (N), phosphorus (P), and potassium (K), as product demand increases so does demand for fertilizers. In the case of potassium, most soils can provide potassium in amounts that exceed crop demand; however the potassium within the soil is not always readily available to the crop, this leads to producers apply potassium to their crops even though soil tests suggests otherwise. One such crop where potassium is in deman... S. Akin, B. Arnall |
71. Evaluating the Potential of In-season Spatial Prediction of Corn Yield and Responses to Nitrogen by Combining Crop Growth Modeling, Satellite Remote Sensing and Machine LearningNitrogen (N) is a critical yield-limiting factor for corn (Zea mays L.). However, over-application of N fertilizers is a common problem in the US Midwest, leading to many environmental problems. It is crucial to develop efficient precision N management (PNM) strategies to improve corn N management. Different PNM strategies have been developed using proximal and remote sensing, crop growth modeling and machine learning. These strategies have both advantages and disadvantages. There is... X. Zhen, Y. Miao, K. Mizuta, S. Folle, J. Lu, R.P. Negrini, G. Feng, Y. Huang |
72. Spatio-temporal Variability of Intra-field Productivity Using Remote SensingUnderstanding the spatiotemporal variability in intra-farm productivity is crucial for management in making agronomic decisions. Furthermore, these decision-making processes can be enhanced using spatial data science and remote sensing. This study aims to develop a framework to asses the spatio-temporal variability of intra-farm productivity through historical satellite data and climate data. Historical satellite data and rainfall information from diverse fields across the United States (2016... E. Van versendaal, C. Hernandez, P. Kyveryga, I. Ciampitti |
73. Biochar Synthesis, Its Impact on Different Soils and Canola GrowthBiochar has been demonstrated as a soil amendment to improve soil health and plant yield. The present study aimed at investigating the potential of wheat straw on canola morphology and yield grown in different soils. The influence of biochar on soil physical and chemical properties was also assessed..Biochar was prepared by pyrolysis of wheat straw in a fixed-bed reactor. Crushed wheat straw was loaded into the reactor in an N2 environment, and the heating was continued up to... M. Hassan |
74. Machine Learning Algorithms in Detecting Long-term Effect of Climatic Factors for Alfalfa Production in KansasThe water levels of the Ogallala Aquifer are depleting so much that agricultural land returns in Kansas are expected to drop by $34.1 million by 2050. It is imperative to understand how frequent droughts and the contrasting rates of groundwater withdrawal and recharge are affected by climate shifts in Kansas. Alfalfa, the ‘Queen of Forages’, is a water demanding crop which supplies high nutritional feed for beef industry that offered Kansas producers a $500 million production valu... F. Nazrul, J. Kim, S. Dey, S. Palla, D. Sihi, B. Whitaker, G. Jha |
75. Dimensionality Reduction and Similarity Metrics for Predicting Crop Yields in Sparse Data MicroclimatesThis study explores and develops new methodologies for predicting agricultural outcomes, such as crop yields, in microclimates characterized by sparse meteorological data. Specifically, it focuses on reducing the dimensionality in time series data as a preprocessing step to generate simpler and more explainable forecast models. Dimensionality reduction helps in managing large data sets by simplifying the information into more manageable forms without significant loss of information. We explor... L. Huender, M. Everett |
76. Using Simulation Modeling to Evaluate the Corn Response to Deficit Irrigation Imposed During Reproductive PeriodIn Alabama, as in many regions of the southeastern states, flash droughts and rising temperatures present significant challenges to the sustainability of agricultural systems. Specifically maize, a crop with a high water demand, faces production risks due to these adverse conditions. The study explores the optimum irrigation scheduling strategies on maize (Zea mays L.) in the reproductive growth stages through the evaluation of the impact of three irrigation treatments, defined by Maximum All... J.S. Velasco, B.V. Ortiz, L. Nunes, R. Prasad, G. Hoogenboom |