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1. Toward More Precise Sugar Beet Management Based On Geostatistical Analysis Of Spatial Variabilty Within FieldsAbstract: Sugar beet (Beta vulgaris L.) yields in England are predicted to increase in the future, due to the advances in plant breeding and agronomic progress, but the intra-field variations in yield due to the variability in soil properties is considerable. This paper explores the within-field spatial variation in environmental variables and crop development during the growing season and their link to spatial variation in sugar beet y... A.J. Murdoch, S.A. Mahmood |
2. Estimating Spatial Variation In Annual Pasture YieldYield mapping is an essential tool for precision management of arable crops. Crop yields can be measured once, at harvest, automatically by the harvesting machinery, and be used to inform a wide range of activities. However yield mapping has had minimal adoption by pastoral farmers. Yield mapping is also a potentially valuable tool for precision management of pastures. However it is difficult to practically map yields on pastures, as they... S.J. Dennis, W. Clarke-hill, A. Taylor, R. Dynes, K. O'neill, T. Jowett |
3. The Spatial And Temporal Variability Analysis Of Wheat Yield in suburban of BeijingAbstract: The yield map is the basis of the fertilization maps and plant maps. In order to diagnose the cause of variation accurately, not only the spatial variation of annual yield data, but also the successive annual yield data of temporal variability should be understood.The introduction of yield monitor system, global positioning system (GPS), and geographic information system have provided new methods to obtain wheat yield in precision agriculture.... Z. Meng, Z. Wang, G. Wu, W. Fu, X. An |
4. First Results Of Development Of A Smart Farm In The NetherlandsGNSS technology has been introduced on about 20 % of the Dutch arable farms in The Netherlands today. Use of sensor technology is also slowly but gradually being adopted by farmers, providing them large amounts of digital data on soil, crop and climate conditions. Typical data are spatial variation in soil organic matter, crop biomass, crop yield, and presence of pests and diseases. We still have to make major steps to use all this data in a way that agriculture becomes more sus... T. Feher, C. Kocks, C. Kempenaar, K. Westerdijk |
5. A Comprehensive Model for Farmland Quality Evaluation with Multi-source Spatial InformationFarmland quality represents various properties, including two parts of natural influencing factors and social influencing factors. The natural factors and social factors are interrelated and interaction, which determine the developing direction of farmland system. In order to overcome the limitation of subjective factors and fuzzy incompatible information, a more scientific evaluation method of farmland quality should be developed to reflect the essential characteristic of farml... Y. Dong, Y. Wang, X. Song, X. Gu |
6. Physiological Repsonses Of Corn To Variable Seeding Rates In Landscape-Scale Strip TrialsMany producers now have the capability to vary seeding rates on-the-go. Methods are needed to develop variable rate seeding approaches in corn but require an understanding of the physiological response of corn to soil-landscape and weather conditions. Interplant competition fundamentally differs at varied seeding rate and may affect corn leaf area, transpiration, plant morphology, and assimilate partitioning. Optimizing these physiological effects with optimal seeding rates in a site-spe... D.B. Myers, N.R. Kitchen, K.A. Sudduth, B.J. Leonard |
7. Spatial Variation And Correlation Between Electric Conductivity (EM38), Penetration Resistance And CO2 Emissions From A Cultivated Peat SoilPeatlands in their natural state accumulate organic matter and bind large quantities of carbon (5 - 50 g C/m2/year). The drainage and cultivation of peat soils increase the aeration of the soil, which increase the brake down of the organic matter. The degradation of the organic material release greenhouse gases such as CO2, N2O and CH4. CO2 emissions dominate when the soil has high oxygen levels, while CH4 mainly ... &.E. Berglund |
8. Penetration Resistance And Yield Variation At Field ScaleIn order to better explain spatial variations within fields, soil physical properties need to be studied in more depth. Relationships between soil physical parameters and yield, especially in the subsoil, are seldom studied since the characterization of soil variability at field or subfield scale using conventional methods is a labor intensive, very expensive, and time-consuming procedure, particularly when high-resolution data is required. However, soil physical prope... E. Bölenius, J. Arvidsson |
9. Optimization Of Maize Yield: Relationship Between Management Zones, Hybrids And Plant PopulationCorn is highly sensitive to variations in plant population and it is one of the most important practices influencing in grain yield. Knowledge about plant physiology and morphology allow understanding how the crop interacts with plant population variation. Considering that for each production system there is a population that optimizes the use of available resources it is necessary to manage plant population to reach maximum grain yield on each particular environment. This study... A.A. Anselmi, J.P. Molin, R. Khosla |
10. Water And Nitrogen Use Efficiency Of Corn And Switchgrass On Claypan Soil LandscapesClaypan soils cover a significant portion of Missouri and Illinois crop land, approximately 4 million ha. Claypan soils, characterized with a pronounced argilic horizon at or below the soil surface, can restrict nutrient availability and uptake, plant water storage, and water infiltration. These soil characteristics affect plant growth, with increasing depth of the topsoil above the claypan horizon having a strong positive correlation to grain crop production. In the case of low... A. Thompson, D.L. Boardman, N. Kitchen, E. Allphin |
11. Heavy Metal PB2+ Pollution Detection In Soil Using Terahertz Time-domain Spectroscopy For Precision AgricultureSoil is an important natural resource for human beings. With the rapid development of modern industry, heavy metals pollution in soil has made prominent influences on farmland environment. It was reported that, one fifth of China's cultivated lands and more than 217,000 farms in the US have been polluted at different levels by heavy metals. The crop grows in the polluted soil and the heavy metal ions transfer from soil to the plant and agro-products. As a result, the crop yi... C. Zhao, B. Li |
12. Soil And Crop Spatial Variability In Cotton Grown On Deep Black Cotton SoilsSoil spatial variation is observed under similar management situation in cotton growing soils of Northern Karnataka. In view of this an experiment was conducted to study the spatial variability in soil with respect soil reaction (pH), Electrical conductivity (Ec), Organic carbon (OC%), all major (N,P,K), secondary (Ca, Mg and S) and micronutrients (Fe, Zn, Cu and Mn) by assessing soil nutrients in deep black cotton soils of the experimental station ... C.C. Parashuramegowda |
13. 3D Map in the Depth Direction of Field for Precision AgricultureBy a change in eating habits with economic development and the global population growth, we have been faced with the need for increased food production again. In order to solve the food problem in the future, the introduction of agriculture organization is progressing in emerging countries as well as developed countries. However, the occurrence of natural disasters and abnormal weather, which is becoming a worldwide problem at present, is further weakening the crops of far... H. Umeda, S. Shibusawa, Q. Li, K. Usui, M. Kodaira |
14. Developing A High-Resolution Land Data Assimilation And Forecast System For Agricultural Decision SupportTechnological advances in weather and climate forecasting and land surface and hydrology modeling have led to an increased ability to predict soil temperature, and soil moisture, near-surface weather elements. These variables are critical building blocks to the development of high-level agriculture-specific models such as pest models and crop yield models. The National Center for Atmospheric Research (NCAR) has developed a high-resolution agriculture-oriented land-data assimilat... W. Mahoney, M. Barlage, D. Gochis, F. Chen |
15. Assessing Definition Of Management Zones Trough Yield MapsYield mapping is one of the core tools of precision agriculture, showing the result of combined growing factors. In a series of yield maps collected along seasons it is possible to observe not only the spatial distribution of the productivity but also its spatial consistency among different seasons. This work proposes the study of distinct methods to analyze yield stability in grain crops regarding its potential for defining management zones from a historical sequence of yield maps. Two ... M.T. Eitelwein, J.P. Molin, M. Spekken, R.G. Trevisan |
16. Spatial Dependence Of Soil Compaction In Annual Cycle Of Different Culture Of Cane Sugar For Sandy SoilThe Currently practiced mechanization for the production of sugar cane involves a heavy traffic of machinery and equipment. Studying the culture in its development environment generates a huge amount of information to fit the top managements and varieties for specific environments. The sugar cane cultivation has a heavy traffic of machinery and equipment, having more than 20 operations per cycle, and being more intense during harvest, providing incre... I. Marasca, F.C. Masiero, D.A. Fiorese, S.S. Guerra, K.P. Lancas |
17. A Method To Estimate Irrigation Efficiency With Evapotranspiration DataIrrigation efficiency is defined as the ratio of irrigation water consumed by the crops to the water diverted (Wg) from a river or reservoir or wells. This terminology serves for better irrigation systems designation and irrigation management practices improvement. But it is hard or high cost with labor intensity to estimate irrigation efficiency from field measurement. This paper proposes an estimating method of irrigation efficiency at the scale of irrigat... H. Zeng, B. Wu, N. Yan |
18. Precision Agriculture In Sugarcane Production. A Key Tool To Understand Its Variability.Precision agriculture (PA) for sugarcane represents an important tool to manage local application of fertilizers, mainly because sugarcane is third in fertilizer consumption among Brazilian crops, after soybean and corn. Among the limiting factors detected for PA adoption in the sugarcane industry, one could mention the cropping system complexity, data handling costs, and lack of appropriate decision support systems. The objective of our research group ha... P.S. Graziano magalhães, G.M. Sanches, O.T. Kolln, H.C. Franco, O.A. Braunbeck, C. Driemeier |
19. Exploiting The Variability In Pasture Production On New Zealand Hill Country.New Zealand has about four million hectares in medium to steep hill country pasture to which granular solid fertiliser is applied by airplane. On most New Zealand hill country properties where cultivation is not possible the only means of influencing pasture production yield is through the addition of fertilizers and paddock subdivision to control grazing and pasture growth rates. Pasture response to fertilizer varies in production zones within the farm which can be modell... M.Q. Grafton, P.J. Mcveagh, R.R. Pullanagari, I.J. Yule |
20. Study Of Spatio-Temporal Variation Of Soil Nutrients In Paddy Rice Planting FarmIt is significant to analysis the spatial and temporal variation of soil nutrients for precision agriculture especially in large-scale farms. For the data size of soil nutrients grows once after sampling which mostly by the frequency of one year or months, to discover the changing trends of exact nutrient would be instructive for the fertilization in the future. In this study, theories of GIS and geostatistics were used to characterize the spatial and temporal variability of soi... C. Wang, T. Chen, J. Dong, C. Li |
21. Site-Specific Variability Of Grape Composition And Wine QualityPrecision Viticulture (PV) is the application of site-specific tools to delineate management zones in vineyards for either targeting inputs or harvesting blocks according to grape maturity status. For the creation of management zones, soil properties, topography, canopy characteristics and grape yield are commonly measured during the growing season. The majority of PV studies in winegrapes have focused on the relation of soil and vine-related spatial data with grape co... S. Fountas, Y. Kotseridis, A. Balafoutis, E. Anastasiou, S. Koundouras, S. Kallithraka, M. Kyraleou |
22. Probability Distributions And Alternative Transformations Of Soil Test NO3-N And PO4-P, Implications For Precision AgricultureRecommendations for fertilizer N in crop production and precision agriculture depend on statistical analyses of data which represent soil NO3-N and PO4-P fertility typical of management zones and fields. Non-normal distributions of soil test N are commonly log transformed prior to statistical analysis for interpolation with methods such as kriging, regression, or principle component analysis. These data are transformed to ensure that analysis meet the assumptions of normality... A. Moulin |
23. Does Nitrogen Balance Surplus Done At Field Level Help To Assess Environmental Effects Of Variable Nitrogen Application In Winter Wheat?Increased nitrogen use efficiency (NUE) is important as a specific consideration to decrease negative impacts of nitrogen (N) on the environment and provide better crop quality. Therefore, in many European countries N is used with restrictions due to UE regulations, set to increase NUE. This is particularly important in wheat production because this crop in EU accounts for 48% of cereal production and uses about 25% of total N-fertilizer applied. One of the methods applied to increase NU... S.M. Samborski |
24. Automated Segmentation and Classification of Land Use from Overhead ImageryReliable land cover or habitat maps are an important component of any long-term landscape planning initiatives relying on current and past land use. Particularly in regions where sustainable management of natural resources is a goal, high spatial resolution habitat maps over large areas will give guidance in land-use management. We propose a computational approach to identify habitats based on the automated analysis of overhead imagery. Ultimately, this approach could be used to assist expert... C. Pradalier, A. Richard, V. Perez, R. Van couwenberghe, A. Benbihi, P. Durand |
25. Identifying and Filtering Out Outliers in Spatial DatasetsOutliers present in the dataset is harmful to the information quality contained in the map and may lead to wrong interpretations, even if the number of outliers to the total data collected is small. Thus, before any analysis, it is extremely important to remove these errors. This work proposes a sequential process model capable of identifying outlier data when compared their neighbors using statistical parameters. First, limits are determined based on the median range of the values of all the... L. Maldaner, J. Molin, T. Tavares, L. Mendez, L. Corrêdo, C. Duarte |
26. Development of a High Resolution Soil Moisture for Precision Agriculture in IndiaSoil moisture and temperature are key inputs to several precision agricultural applications such as irrigation scheduling, identifying crop health, pest and disease prediction, yield and acreage estimation, etc. The existing remote sensing satellites based soil moisture products such as SMAP are of coarse resolution and physics based land surface model such as NLDAS, GLDAS are of coarse resolution as well as not available for real time applications. Keeping this in focus, we are d... K. Das, J. Singh, J. Hazra |
27. Agricultural Remote Sensing Information for Farmers in GermanyThe European Copernicus program delivers optical and radar satellite imagery at a high temporal frequency and at a ground resolution of 10m worldwide with an open data policy. Since July 2017 the satellite constellation of the Sentinel-1 and -2 satellites is fully operational, allowing e.g. coverage of Germany every 1-2 days by radar and every 2-3 days with optical sensors. This huge data source contains a variety of valuable input information for farmers to monitor the in-field variability a... H. Lilienthal, H. Gerighausen, E. Schnug |
28. Using Geospatial Data to Assess How Climate Change May Affect Land Suitability for Agriculture ProductionFinding solutions to the challenge of sustainably feeding the world’s growing population is a pressing research need that cuts across many disciplines including using geospatial data. One possible area could be developing agricultural frontiers. Frontiers are defined as land that is currently not cultivated but that may become suitable for agriculture under climate change. Climate change may drive large-scale geographic shifts in agriculture, including expansion in cultivation at the th... K. Kc, L. Hannah, P. Roehrdanz, C. Donatti, E. Fraser, A. Berg, L. Saenz, T.M. Wright, R.J. Hijmans, M. Mulligan |
29. Development of an Overhead Optical Yield Monitor for a Sugarcane Harvester in LouisianaA yield monitor is a device used to measure harvested crop weight per unit area for a specific location within a field. The device documents yield variability in harvested fields and ultimately can be used to create a geographical-referenced yield map. Yield maps can be used to identify low yielding areas where poor soil fertility, disease, or pests may adversely affect yield. Management practices can then be adjusted to correct these issues, resulting in an increase in yields and... R.R. Price, R.M. Johnson, R.P. Viator |
30. Application of Routines for Automation of Geostatistical Analysis Procedures and Interpolation of Data by Ordinary KrigingOrdinary kriging (OK) is one of the most suitable interpolation methods for the construction of thematic maps used in precision agriculture. However, the use of OK is complex. Farmers/agronomists are generally not highly trained to use geostatistical methods to produce soil and plant attribute maps for precision agriculture and thus ensure that best management approaches are used. Therefore, the objective of this work was to develop and apply computational routines using procedures and geosta... N.M. Betzek, E.G. Souza, C.L. Bazzi, P.G. Magalhães, A. Gavioli, K. Schenatto, R.W. Dall'agnol |
31. Analysis of Soil Properties Predictability Using Different On-the-Go Soil Mapping SystemsUnderstanding the spatial variability of soil chemical and physical attributes allows for the optimization of the profitability of nutrient and water management for crop development. Considering the advantages and accessibility of various types of multi-sensor platforms capable of acquiring large sensing data pertaining to soil information across a landscape, this study compares data obtained using four common soil mapping systems: 1) topography obtained using a real-time kinematic (RTK) glob... H. Huang, V. Adamchuk, A. Biswas, W. Ji, S. Lauzon |
32. GIS Web and Mobile Development with Interfaces in QGIS for Variable Rate FertilizationIn this paper we described the implementation of a GIS for Precision Agriculture for sugarcane crop in Colombia. An spatial equation for Variable Rate Fertilization Model was defined using as inputs estimated harvest data, nutrients in soil and fertilizer efficiently. Models for soil and harvest variability are also defined. A personalized plugin for precision agriculture was developed into QGIS software, there is the option of upload maps to a Web and mobile app using the Desktop software an... R. Cuitiva baracaldo, O. Munar vivas, G. Carrillo romero |
33. Experiences in the Development of Commercial Web-Based Data Engines to Support UK Growers Within an Industry-Academic PartnershipThe lifecycle of Precision Agriculture data begins the moment that the measurement is taken, after which it may pass through each multiple data processes until finally arriving as an output employed back in the production system. This flow can be hindered by the fact that many farm datasets have different spatial resolutions. This makes the process to aggregate or analyse multiple Precision Agriculture layers arduous and time consuming. Precision Decisions Ltd located in Yorks... J. Taylor, Y. Shahar, P. James, C. Blacker, S. Leese, R. Sanderson, R. Kavanagh |
34. 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 |
35. 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 |
36. 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 |
37. 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 |
38. 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 |
39. 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 |
40. 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 |
41. 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 |
42. 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 |
43. 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 |
44. 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 |
45. 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 |
46. 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 |
47. 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 |
48. 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 |