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1. Developing Empirical Method to Estimate Phosphorous in Potato Plants Using Spectroscopy-based ApproachApplication of non-destructive sensors opens a promising opportunity to provide efficient information on nutrient contents based on leaf or canopy reflectance in different crops. In potatoes, nutrient levels are estimated by conducting chemical tests for the petioles. In thinking of deploying sensors for potato nutrient estimation, it is necessary to study the spectrum based on petiole chemical testing rather than leaf chemical testing. Thus, this study aimed to investigate whether there is a... R. Abukmeil, A. Almallahi |
2. On-the-go Gamma Spectrometry and Its Evaluation Via Support Vector Machines: Really a Valuable Tool for Site-independent Soil Texture Prediction?With progressive implementation of precision agriculture (PA) techniques in current agricultural/ viticultural practice, the need for high-resolution information on soil properties at low effort and cost is increasing. Moreover, climate change and extended drought periods do even increase this demand. Evaluating soil fertility and carbon storage potential of arable fields and vineyards, e.g. for future economic assessment of ecosystem services, requires spatially resolved soil data. Soil text... S. PÄtzold, T.W. Heggemann, R. Wehrle |
3. A Hyperlocal Machine Learning Approach to Estimate NDVI from SAR Images for Agricultural FieldsThe normalized difference vegetation index (NDVI) is a key parameter in precision agriculture used globally since the 1970s. The NDVI is sensitive to the biochemical and physiological properties of the crop and is based on the Red (~650 nm) and NIR (~850 nm) spectral bands. It is used as a proxy to monitor crop growth, correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions which might al... R. Pelta, O. Beeri, T. Shilo, R. Tarshish |
4. Gamma-ray Spectrometry to Determine Soil Properties for Soil Mapping in Precision AgricultureSoil maps are critical for various land use applications and form the basis for the successful implementation of precision agriculture in crop production. Soil maps provide the spatial distribution of important soil physical and chemical properties to a farmer. The farmer uses this information to make critical management decisions for profitable and sustainable food production. South Africa is a water scarce country where rainfall is mainly seasonal and unreliable. Under these circumstances, ... J.G. Dreyer, L. Ameglio |
5. Predicting Secondary Soil Fertility Attributes Using XRF Sensor with Reduced Scanning Time in Samples with Different Moisture ContentTo support future in situ/on-the-go applications using X-ray fluorescence (XRF) sensors for soil mapping, this study aimed at evaluating the XRF performance for predicting organic matter (OM), base saturation (V), and exchangeable (ex-) Mg, using a reduced analysis time (e.g., 4 s) in soil samples with different moisture contents. These attributes are considered secondary for XRF prediction because they do not present emission lines in the XRF spectrum. Ninety-nine soil samp... T.R. Tavares, J.P. Molin, T.R. Da silva , H.W. De carvalho |
6. The Use of Spatial and Temporal Measures to Enhance the Sensitivity of Satellite-based Spectral Vegetation Indices to (Water) Stress in Maize FieldsClimate change and water scarcity are reducing the available irrigation water for agriculture thus turning it into a limited resource. Today calculating and estimating crop water requirements are achieved through the ETc FAO-56 model where the effect of climate on crop water requirement is determined through the water evaporation from the soil and plant (ETref), and a calendar crop coefficient (Kc). Models t... Y. Goldwasser, V. Alchanati, E. Goldshtein, Y. Cohen, A. Gips, I. Nadav |
7. Organ Scale Nitrogen Map: a Novel Approach for Leaf Nitrogen Concentration EstimationCrop nitrogen trait estimations have been used for decades in the frame of precision agriculture and phenotyping researches. They are crucial information towards a sustainable agriculture and efficient use of resources. Remote sensing approaches are currently accurate tools for nitrogen trait estimations. They are usually quantified through a parametric regression between remote sensing data and the ground truth. For instance, chlorophyll or nitrogen concentration are accurately estimated usi... A. Carlier, S. dandrifosse, B. Dumont, B. Mercatoris |
8. Sun Effect on the Estimation of Wheat Ear Density by Deep LearningEar density is one of the yield components of wheat and therefore a variable of high agronomic interest. Its traditional measurement necessitates laborious human observations in the field or destructive sampling. In the recent years, deep learning based on RGB images has been identified as a low-cost, robust and high-throughput alternative to measure this variable. However, most of the studies were limited to the computer challenge of counting the ears in the images, without aiming to convert... S. Dandrifosse, E. Ennadifi, A. Carlier, B. Gosselin, B. Dumont, B. Mercatoris |
9. Machine Learning Techniques for Early Identification of Nitrogen Variability in MaizeCharacterizing and managing nutrient variability has been the focus of precision agriculture research for decades. Previous research has indicated that in-situ fluorescence sensor measurements can be used as a proxy for nitrogen (N) status in plants in greenhouse conditions employing static sensor measurements. Indeed, practitioners of precision N management require determination of in-season plant N status in real-time at field scale to enable the most efficient N fertiliz... D. Mandal, R.D. Siqueira, L. Longchamps, R. Khosla |
10. Soil Variability Mapping with Airborne Gamma-ray Spectrometry and MagneticsThe knowledge of spatial distribution of agricultural soils physical and chemical properties is critical for profitable and sustainable crop and food production. The collection of soil data presents however obvious problems arising from sampling a dense, opaque and very heterogeneous medium. Conventional methods consisting of ground-based grid survey are laborious, expensive and lack appropriate spatial resolution to allow best farm management decision. Over the past 50 years, airborne geophy... L. Ameglio, E. Stettler, D. Eberle |
11. Printed Nitrate Sensors for In-soil MeasurementsManaging nitrate is a central concert for precision agriculture, from delineating management zones, to optimizing nitrogen use efficiency through in-season applications, to minimizing leaching and greenhouse gas emissions. However, measurement methods for in-soil nitrate are limiting. State-of-the-art soil nitrate analysis requires taking soil or liquid samples to laboratories for chemical or spectrographic analysis. These methods are accurate, but costly, labor intensive, and cover limited g... C. Baumbauer, P. Goodrich, A. Arias |
12. Comparison of Canopy Extraction Methods from UAV Thermal Images for Temperature Mapping: a Case Study from a Peach OrchardCanopy extraction using thermal images significantly affects temperature mapping and crop water status estimation. This study aimed to compare several canopy extraction methodologies by utilizing a large database of UAV thermal images from a precision irrigation trial in a peach orchard. Canopy extraction using thermal images can be attained by purely statistical analysis (S), a combination of statistical and spatial analyses (SS), or by synchronizing thermal and RGB images, following RGB sta... L. Katz, A. Ben-gal, I. Litaor, A. Naor, A. Peeters, E. Goldshtein, V. Alchanatis, Y. Cohen |
13. Investigating the Potential of Visible and Near-infrared Spectroscopy (VNIR) for Detecting Phosphorus Status of Winter Wheat Leaves Grown in Long-term TrialThe determination of plant nutrient content is crucial for evaluating crop nutrient removal, enhancing nutrient use efficiency, and optimizing yields. Nutrient conventional monitoring involves colorimetric analyses in the laboratory; however, this approach is labor-intensive, costly, and time-consuming. The visible and near-infrared spectroscopy (VNIR) or hyperspectral non-imaging sensors have been an emerging technology that has been proved its potential for rapid detection of plant nutrient... Y. El-mejjaouy, B. Dumont, A. Oukarroum, B. Mercatoris , P. Vermeulen |
14. Toward Smart Soybean Variety Selection Using UAV-based Imagery and Machine LearningThe efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield and resilience to stress, achieved in one year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials due to their large population, complex genetic behavior, and high genotype-en... J. Zhou, J. Zhou |
15. Estimating Soil Carbon Stocks with In-field Visible and Near-infrared SpectroscopyAgricultural lands can be a sink for carbon and play an important role in offsetting carbon emissions. Current methods of measuring carbon sequestration—through repeated temporal soil samples—are costly and laborious. A promising alternative is using visible, near-infrared (VNIR) diffuse reflectance spectroscopy. However, VNIR data are complex, which requires several data processing steps and often yields inconsistent results, especially when using in situ VNIR measurements. Using... C.J. Ransom, C. Vong, K.S. Veum, K.A. Sudduth, N.R. Kitchen, J. Zhou |
16. Analytical and Technological Advancements for Soybean Quality Mapping and Economic DifferentiationIn the past, measuring soybean protein and oil content required the collection of soybean seed samples and laboratory analyses. Modern on-the-go near-infrared (NIR) sensing technologies during the harvest and proximal remote sensing (aerial and satellite imagery) before harvest time can be used to provide an early estimate of seed quality levels, benchmark in-season predictions with at-harvest final seed quality and enable seed differentiation for farmers leading to better marketing strategie... A. Prestholt, C. Hernandez, I. Ciampitti , P. Kyveryga |
17. Hay Yield Estimation Using UAV-based Imagery and a Convolutional Neural NetworkYield monitoring systems are widely used commercially in grain crops to map yields at a scale of a few meters. However, such high-resolution yield monitoring and mapping for hay and forage crops has not been commercialized. Most commercial hay yield monitoring systems only obtain the weight of individual bales, making it difficult to map and understand the spatial variability in hay yield. This study investigated the feasibility of an unmanned aerial vehicle (UAV)-based remote sensing system ... K. Lee, K.A. Sudduth, J. Zhou |
18. Diagnosis of Grapevine Nutrient Content Using Proximal Hyperspectral ImagingNutrient deficiencies on grapevines could affect the fruit yield and quality, which is a major concern in vineyards. Nutrient deficiencies may be recognizable by foliar symptoms that vary by mineral nutrient and stress severity, but it is too late to manage when visible deficiency symptoms become apparent. The nutrient analysis in the laboratory is the way to get an accurate result, but it is time and cost-intensive. The differences in leaf nutrient levels also alter spectral characteristics ... C. Kang, M. Karkee, Q. Zhang, N. Shcherbatyuk, P. Davadant, M. Keller |
19. Snap-shot Hyperspectral Camera for Potassium Prediction of Peach Trees Using Multivariate AnalysisHyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or pest disease detection. As sensing technology advances, measuring nutrient levels and disease detection also progresses. This study aimed to predict the levels of potassium (K) content in peach leaves with the new snapshot hyperspectral camera. The study was conducted at the Clemson University Musser Fruit Research Farm (Seneca, SC, USA, 34.... J.J. Maja, M. Abenina, M. Cutulle, J. Melgar, H. Liu |
20. Impact of Cover Crop and Soil Apparent Electrical Conductivity on Cotton Development and YieldCotton is one of the major crops in the New Madrid Seismic Zone (NMSZ) of the U.S. Lower Mississippi River Valley region. Because cotton production doesn’t leave a lot of crop residue in the field, low soil organic matter levels are common. While the benefits of crop rotation are well known, cotton is often grown year after year in the same fields for economic reasons. Soils in the region are generally quite variable, with areas of very high sand content. Winter cover crops and reduced ... E. Vories, K. Veum, K. Sudduth |
21. Measuring Soil Carbon with Intensive Soil Sampling and Proximal Profile SensingSoils have a large carbon storage capacity and sequestering additional carbon in agricultural fields can reduce CO2 levels in the atmosphere, helping to mitigate climate change. Efforts are underway to incentivize agricultural producers to increase soil organic carbon (SOC) stocks in their fields using various conservation practices. These practices and the increased SOC provide important additional benefits including improved soil health, water quality and – in some cases –... E. Lund, T. Lund, C. Maxton |
22. Multi-sensor Imagery Fusion for Pixel-by-pixel Water Stress MappingEvaluating water stress in agricultural fields is fundamental in irrigation decision-making, especially mapping the in-field water stress variability as it allows real-time detection of system failures or avoiding yield loss in cases of unplanned water stress. Water stress mapping by remote sensing imagery is commonly associated with the thermal or the short-wave-infra-red (SWIR) bands. However, integration of multi-sensors imagery such as radar imagery or sensors with only visible and near-i... O. Beeri, R. Pelta, Z. Sade, T. Shilo |
23. Functional Soil Property Mapping with Electrical Conductivity, Spectral and Satellite Remote SensorsProximal electrical conductivity (EC) and spectral sensing has been widely used as a cost-effective tool for soil mapping at field scale. The traditional method of calibrating proximal sensors for functional soil property prediction (e.g., soil organic matter, sand, silt, and clay contents) requires the local soil sample data, which results in a field-specific calibration. In this large-scale study consisting of 126 fields, we found that the traditional local calibration method had suffered w... X. Xiong, D. Myers, J. Debruin, B. Gunzenhauser, N. Sampath, D. Ye, H. Underwood, R. Hensley |
24. Proximal Sensing of Penetration Resistance at a Permanent Grassland Site in Southern FinlandProximal soil sensing allows for assessing soil spatial heterogeneity at a high spatial resolution. These data can be used for decision support on soil and crop agronomic management. Recent sensor systems are capable of simultaneously mapping several variables, such as soil electrical conductivity (EC), spectral reflectance, temperature, and water content, in real-time. In autumn 2021, we used a commercial soil scanner (Veris iScan+) to derive information on soil spatial variability for a per... H.E. Ahrends, A. Lajunen |
25. Employment of the SSEB and CROPWAT Models to Estimate the Water Footprint of Potato Grown in Hyper-arid Regions of Saudi ArabiaQuantifying crops’ water footprint (WF) is essential for sustainable agriculture especially in arid regions, which suffers from harsh environmental conditions and severe shortage of freshwater resources such as Saudi Arabia. In this study, WF of irrigated potato crop was estimated for the implementation of precision agriculture techniques. The CROPWAT and the Simplified Surface Energy Balance (SSEB) approaches were adopted. Soil, plant, and yield samples were randomly collected from six... R. Madugundu, K. Al-gaadi, E. Tola |
26. Mapping Soil Health and Grain Quality Variations Across a Corn Field in TexasSoil health is a key property of soils influencing grain yield and quality. Within-field mapping of soil health index and grain quality can help farmers and managers to adjust site-specific farm management decisions for economic benefits. A study was conducted to map within-field soil health and grain protein and oil content variations using apparent electrical conductivity (ECa) and terrain attributes as their predictors. Two hundred and two topsoil samples were analyzed to determine soil he... K. Adhikari, D.R. Smith, C. Hajda, P.R. Owens |
27. Application of Drone Data to Assess Damage Intensity of Bacterial Leaf Blight Disease on Rice Crop in IndonesiaThe Government of Indonesia has launched agricultural insurance program since 2016. A key in agricultural insurance is damage assessment which is required to be as precise, quick, quantitative and inexpensive as possible. Current method is to inspect the damage by human eyes of specialist having experiences. This method, however, costs much and is difficult to estimate disease infected fields precisely in wide area. So, there is increasing need to develop effective, simplified and low cost me... C. Hongo, S. Isono, G. Sigit, B. Utoyo, E. Tamura |
28. Evaluation of Image Acquisition Parameters and Data Extraction Methods on Plant Height Estimation with UAS ImageryAerial imagery from unmanned aircraft systems (UASs) has been increasingly used for field phenotyping and precision agriculture. Plant height is one important crop growth parameter that has been estimated from 3D point clouds and digital surface models (DSMs) derived from UAS-based aerial imagery. However, many factors can affect the accuracy of aerial plant height estimation. This study examined the effects of image overlap, pixel resolution, and data extraction methods on estimati... C. Yang, C. Suh, W. Guo, H. Zhao, J. Zhang, R. Eyster |
29. Knowledge-based Approach for Weed Detection Using RGB ImageryA workflow was developed to explore the potential use of Phase One RGB for weed mapping in a herbicide efficacy trial in wheat. Images with spatial resolution of 0.8 mm were collected in July 2020 over an area of nearly 2000 square meters (66 plots). The study site was on a research farm at the University of Saskatchewan, Canada. Wheat was seeded on June 29, 2020, at a rate of 75 seeds per square meter with a row spacing of 30.5 cm. The weed species seeded in the trial were kochia, wild oat, ... T. Ha, K. Aldridge, E. Johnson, S.J. Shirtliffe, S. Ryu |
30. UAV-based Hyperspectral Monitoring of Peach Trees As Affected by Silicon Applications and Water Stress StatusPrevious research has shown that the application of reduced doses of Silicon (Si) improves crop tolerance to water stress, which is common in commercial young peach trees because irrigation is not usually applied during their first two years. In this study, aerial images were used to monitor the impact of different Si and water treatments on the hyperspectral response of peach trees. An experiment with 60 young (under 1 year old) peach trees located at the Musser Fruit Research Center (Seneca... J. Peña, J. Melgar, A. De castro, J. Maja, K. Nascimento-silva |
31. N-management Using Structural Data: UAV-derived Crop Height As an Estimator for Biomass, N Concentration, and N Uptake in Winter WheatIn the last 15 years, sensors mounted on Unmanned Aerial Vehicles (UAVs) have been intensively investigated for crop monitoring. Besides known remote sensing approaches based on multispectral and hyperspectral sensors, photogrammetric methods became very important. Structure for Motion (SfM) and Multiview Stereopsis (MVS) analysis approaches enable the quantitative determination of absolute crop height and crop growth. Since the first paper on UAV-derived crop height was published by Bendig e... G. Bareth, A. Jenal, H. Hüging |
32. Cotton Boll Detection and Yield Estimation Using UAS Lidar Data and RGB ImageCotton boll distribution is a critical phenotypic trait that represents the plant's response to its environment. Accurate quantification of boll distribution provides valuable information for breeding cultivars with high yield and fiber quality. Manual methods for boll mapping are time-consuming and labor-intensive. We evaluated the application of Lidar point cloud and RGB image data in boll detection and distribution and yield estimation. Lidar data was acquired at 15 m using a DJI Matri... Z. Lin, W. Guo, N. Gill |
33. Integration of Unmanned Aerial Systems Images and Yield Monitor in Improving Cotton Yield EstimationThe yield monitor is one of the most adopted precision agriculture technologies because it generates dense yield data to quantify the spatial variability of crop yield as a basis for site-specific management. However, yield monitor data has various errors that prevent proper interpretation and precise field management. The objective of this study was to evaluate the application of unmanned aerial systems (UAS) images in improving cotton yield monitor data. The study was conducted in a dryland... H. Gu, W. Guo |
34. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS ImageryDeep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high re... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal |
35. Evaluation of Unmanned Aerial Vehicle Images in Estimating Cotton Nitrogen ContentEstimating crop nitrogen content is a critical step for optimizing nitrogen fertilizer application. The objective of this study was to evaluate the application of UAV images in estimating cotton (Gossypium hirsutum L.) N content. This study was conducted in a dryland cotton field in Garza County, Texas, in 2020. The experiment was implemented as a randomized complete block design with three N rates of 0, 34, and 67 kg N ha-1. A RedEdge multispectral sensor was used to acqu... R. Karn, H. Gu, O. Adedeji, W. Guo |
36. Establishment of a Canola Emergence Assessment Methodology Using Image-based Plant Count and Ground Cover AnalysisManual assessment of emergence is a time-consuming practice that must occur within a short time-frame of the emergence stage in canola (Brassica napus). Unmanned aerial vehicles (UAV) may allow for a more thorough assessment of canola emergence by covering a wider scope of the field and in a more timely manner than in-person evaluations. This research aims to calibrate the relationship between emerging plant population count and the ground cover. The field trial took place at the Uni... K. Krys, S. Shirtliffe, H. Duddu, T. Ha, A. Attanayake, E. Johnson, E. Andvaag, I. Stavness |
37. Utilization of UASs to Predict Sugarcane Yields in Louisiana Prior to HarvestOne of the most difficult tasks that both sugarcane producers and processors face every year is estimating the yields of sugarcane fields prior to the start of harvest. This information is needed by processors to determine when the harvest season is to be initiated each year and by producers to decide when each field should be harvested. This is particularly important in Louisiana because the end of the harvest season is often affected by freeze events. These events can severely damage the cr... R.M. Johnson, B. Ramachandran |
38. Increasing the Accuracy of UAV-Based Remote Sensing Data for Strawberry Nitrogen and Water Stress DetectionThis paper presents the methods to increase the accuracy of unmanned aerial vehicles (UAV)-based remote sensing data for the determination of plant nitrogen and water stresses with increased accuracy. As the demand for agricultural products is significantly increasing to keep up with the growing population, it is important to investigate methods to reduce the use of water and chemicals for water conservation, reduction in the production cost, and reduction in environmental impact. UAV-based r... S. Bhandari, A. Raheja |
39. Estimation of Cotton Biomass Using Unmanned Aerial Systems and Satellite-based Remote SensingSatellite and unmanned aerial system (UAS) images are effective in monitoring crop growth at various spatial, temporal, and spectral scales. The objective of the study was to estimate cotton biomass at different growth stages using vegetation indices (VIs) derived from UAS and satellite images. This research was conducted in a cotton field in Hale County, Texas, in 2021. Data collected include 54 plant samples at different locations for three dates of the growing season. Multispectral images ... O.I. Adedeji, B.P. Ghimire, H. Gu, R. Karn, Z. Lin, W. Guo |
40. Enhancing Spatial Resolution of Maize Grain Yield DataGrain yield data is frequently used for precision agriculture management purposes and as a parameter for evaluating agronomy experiments, but unexpected challenges sometimes interfere with harvest plans or cause total losses. The spatial detail of modern grain yield monitoring data is also limited by combine header width, which could be nearly 14 m in some crops. Remote sensing data, such as multispectral imagery collected via satellite and unmanned aerial systems (UAS), could be used t... J. Siegfried, R. Khosla, D. Mandal, W. Yilma |
41. Assessment of Goss Wilt Disease Severity Using Machine Learning Techniques Coupled with UAV ImageryGoss Wilt has become a common disease in corn fields in North Dakota. It has been one of the most yield-limiting diseases, causing losses of up to 50%. The current method to identify the disease is through visual inspection of the field, which is inefficient, and can be subjective, with misleading results, due to evaluator fatigue. Therefore, developing a reliable, accurate, and automated tool for assessing the severity of Goss's Wilt disease has become a top priority. The use of un... A. Das, P. Flores, Z. Zhang , A. Friskop, J. Mathew |
42. Precision Nitrogen and Water Management for Optimized Sugar Beet Yield and Sugar ContentSugar beet (SB) production profitability is based on maximizing three parameters: beet yield, sucrose content, and sucrose recovery efficiency. Efficient nitrogen (N) and water management are key for successful SB production. Nitrogen deficits in the soil can reduce root and sugar yield. Overapplication of N can reduce sucrose content and increase nitrate impurities which lowers sucrose recovery. Application of N in excess of SB crop need leads to vigorous canopy growth, while compromising ro... O.S. Walsh, S. Shafian |
43. Potential of UAS Multispectral Imagery for Predicting Yield Determining Physiological Parameters of CottonThe use of unmanned aerial systems (UAS) in precision agriculture has increased rapidly due to the availability of reliable, low-cost, and high-resolution sensors as well as advanced image processing software. Lint yield in cotton is the product of three physiological parameters: photosynthetically active radiation intercepted by canopy (IPAR), the efficiency of converting intercepted active radiation to biomass (RUE), and the ratio of economic yield to total dry matter (HI). The relationship... A. Pokhrel, S. Virk, J.L. Snider, G. Vellidis, V. Parkash |
44. Multispectral Assessment of Chickpea in the Northern Great PlainsChickpea is an increasingly important crop in the Montana agricultural system. From 2017 to 2021 the U.S. has planted an average of about 492,000 acres per year with Montana chickpea production accounting for around 44% of the U.S. total (USDA/NASS QuickStats accessed on 2/11/2021). This has led to an increase in breeding efforts for elite varieties adapted to the unique conditions in the Northern Great Plains. Breeding of chickpea often relies on traditional phenotyping techniques that are l... J.M. Vetch |