scholarly journals Delineation of management zones with spatial data fusion and belief theory

2019 ◽  
Vol 21 (4) ◽  
pp. 802-830
Author(s):  
Claudia Vallentin ◽  
Eike Stefan Dobers ◽  
Sibylle Itzerott ◽  
Birgit Kleinschmit ◽  
Daniel Spengler
Keyword(s):  
Data Fusion ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Relevant Information ◽  
High Yield ◽  
Model Parameters ◽  
Yield Data ◽  
Management Zones

AbstractPrecision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.

scholarly journals Uncertainties associated with the delineation of management zones in precision agriculture

2020 ◽  
Author(s):  
Tomás R. Tenreiro ◽  
Margarita García-Vila ◽  
José A. Gómez ◽  
Elías Fereres
Keyword(s):  
Spatial Data ◽  
Precision Agriculture ◽  
Initial Conditions ◽  
Principal Component ◽  
Ground Cover ◽  
Temporal Variations ◽  
Yield Response ◽  
Temporal Asymmetry ◽  
Management Zones ◽  
Sampling Schemes

<p>The characterization of spatial variations in soil properties and crop performance within precision agriculture, and particularly the delineation of management zones (MZ) and sampling schemes, are complex assignments currently far from being resolved. Considerable advances have been achieved regarding the analysis of spatial data, but less attention has been devoted to assess the temporal asymmetry associated with variable <em>crop×year</em> interactions. In this case-study of a 9 ha field located in Spain, we captured interactions between both spatial and temporal variations for two contrasting seasons of remotely sensed crop data (NDVI) combined with several geomorphological properties (i.e., elevation, slope orientation, soil apparent electrical conductivity - ECa, %Clay, %Sand, pH). We developed an algorithm combining Principal Component Analysis (PCA) and clustering k-means and succeeded to delineate four MZ’s with a satisfactory fragmentation degree, each one associated with a different <em>Elevation×ECa×NDVI</em> combination. Simulated yield maps were generated using NDVI maps correlated to ground cover to establish initial conditions in simulation settings with a crop model. Yield maps were spatially correlated but fitted into variograms with irregular spatial structure. Both CV and spatial patterns did not show consistency from year to year. The results indicate that MZ’s temporal instability is an important issue for site-specific management as agronomic implications varied greatly with <em>crop×year</em> setting. We observed differences, not only regarding NDVI patterns but also in yield response to the combination of <em>Elevation×ECa</em> (and <em>Texture</em>) depending on the seasonal rainfall. A reduction of 14% of the ’Goodness of Variance Fit’ was observed for simulated yield from the first to the second <em>crop×year</em>, highlighting the difficulties in the delineation of MZ’s with persistent confidence. The interpretation of <em>MZ×Yield</em> associations was not straight forward from the metrics selected here as it also depended on agronomic knowledge. We believe that precision agriculture will benefit greatly from improved protocols for MZ delineation and sampling schemes. However, the uncertainty associated with temporal asymmetry of yield clustering and MZ’s interpretation reveals that ‘automated digital agricultural systems’ are still far from reality.</p>

A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture

CATENA ◽  
2018 ◽  
Vol 167 ◽  
pp. 293-304 ◽  
Author(s):  
A. Castrignanò ◽  
G. Buttafuoco ◽  
R. Quarto ◽  
D. Parisi ◽  
R.A. Viscarra Rossel ◽  
...  
Keyword(s):  
Data Fusion ◽  
Precision Agriculture ◽  
Sensor Data ◽  
Management Zones ◽  
Sensor Data Fusion ◽  
Fusion Approach

scholarly journals What does precision crop production hold for the future of soil science and plant nutrition?

2018 ◽  
pp. 411-421
Author(s):  
István Sisák ◽  
András Benő ◽  
Mihály Kocsis
Keyword(s):  
Precision Agriculture ◽  
Crop Production ◽  
Agricultural Land ◽  
New Technology ◽  
Methodological Approach ◽  
Sample Collection ◽  
Production Potential ◽  
Yield Data ◽  
Multiple Regression Models ◽  
Service Sectors

The concept of precision agriculture is straightforward at the scientific level but even basic goals are blurred at the level of everyday practice in the Hungarian crop production despite the fact that several elements of the new technology have already been applied. The industrial and the service sectors offer many products and services to the farmers but crop producers do not get enough support to choose between different alternatives. Agricultural higher education must deliver this support directly to the farmers and via the released young graduates. The price of agricultural land must be higher if well-organized data underpin the production potential of the fields. Accumulated database is a form of capital. It must be owned by the farmers but in a data-driven economy its sharing will generate value for both farmers and the society as a whole. We present a methodological approach in which simple models were applied to predict yield by using only those yield data which spatially coincide with the soil data and the remaining yield data and the models were used to test different sampling and interpolation approaches commonly applied in precision agriculture. Three strategies for composite sample collection and three interpolation methods were compared. Multiple regression models were developed to predict yields. R2 values were used to select among the applied methods.

scholarly journals Determining the management zones with hierarchic and non-hierarchic clustering methods

2014 ◽  
Vol 60 (Special Issue) ◽  
pp. S44-S51 ◽  
Author(s):  
J. Galambošová ◽  
V. Rataj ◽  
R. Prokeinová ◽  
J. Prešinská
Keyword(s):  
Precision Agriculture ◽  
Clustering Methods ◽  
Clustering Method ◽  
Yield Data ◽  
Number Of Clusters ◽  
Management Zones ◽  
Electromagnetic Data ◽  
Clustering Criterion

Delineation of the management zones of a field is commonly used in precision agriculture technology. There are many techniques used to identify management zones. The most used technique is k-means clustering, where the number of clusters is managed by the user. The paper deals with clustering the yield data and electromagnetic data of a 17 ha field using the Ward’s method followed by the k-means clustering method. The cubic clustering criterion was used to determine the number of clusters. Based on results, it can be concluded that it is beneficial to combine the k-means clustering method with the hierarchic method (Ward’s method).

Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy

2020 ◽  
Author(s):  
Calogero Schillaci ◽  
Edoardo Tomasoni ◽  
Marco Acutis ◽  
Alessia Perego
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Northern Italy ◽  
Yield Data ◽  
Batch Mode ◽  
Area Index ◽  
Sentinel 2

<p>To improve nitrogen fertilization is well known that vegetation indices can offer a picture of the nutritional status of the crop. In this study, field management information (maize sowing and harvesting dates, tillage, fertilization) and estimated vegetation indices VI (Sentinel 2 derived Leaf Area Index LAI, Normalized Difference Vegetation Index NDVI, Fraction of Photosynthetic radiation fPAR) were analysed to develop a batch-mode VIs routine to manage high dimensional temporal and spatial data for Decision Support Systems DSS in precision agriculture, and to optimize the maize N fertilization in the field. The study was carried out in maize (2017-2018) on a farm located in Mantua (northern Italy); the soil is a Vertic Calciustepts with a fine silty texture with moderate content of carbonates. A collection of Sentinel 2 images (with <25% cloud cover) were processed using Graph Processing Tool (GPT). This tool is used through the console to execute Sentinel Application Platform (SNAP) raster data operators in batch-mode. The workflow applied on the Sentinel images consisted in: resampling each band to 10m pixel size, splitting data into subsets according to the farm boundaries using Region of Interest (ROI). Biophysical Operator based on Biophysical Toolbox was used to derive LAI, fPAR for the estimation of maize vegetation indices from emergence until senescence. Yield data were acquired with a volumetric yield sensing in a combine harvester. Fertilization plans were then calculated for each field prior to the side-dressing fertilization. The routine is meant as a user-friendly tool to obtain time series of assimilated VIs of middle and high spatial resolution for field crop fertilization. It also overcomes the failures of the open source graphic user interface of SNAP. For the year 2018, yield data were related to the 34 LAI derived from Sentinel 2a products at 10 m spatial resolution (R<sup>2</sup>=0.42). This result underlined a trend that can be further studied to define a cluster strategy based on soil properties. As a further step, we will test whether spatial differences in assimilated VIs, integrated with yield data, can guide the nitrogen top-dress fertilization in quantitative way more accurately than a single image or a collection of single images.</p>

scholarly journals Effects of Ridge Tillage and Straw Mulching on Cultivation the Fresh Faba Beans

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1054
Author(s):  
Bo Li ◽  
Xinyu Chen ◽  
Xiaoxu Shi ◽  
Jian Liu ◽  
Yafeng Wei ◽  
...  
Keyword(s):  
Rice Straw ◽  
Precision Agriculture ◽  
Field Experiments ◽  
Root Length Density ◽  
Root Surface ◽  
High Yield ◽  
Root Surface Area ◽  
Straw Mulching ◽  
Faba Beans ◽  
Ridge Tillage

Ridge tillage is an effective agronomic practice and a miniature precision agriculture; however, its effects on the growth of faba beans (Vicia faba L.) are poorly understood. This study aimed to determine the effect of ridge tillage and straw mulching on the root growth, nutrient accumulation and yield of faba beans. Field experiments were conducted during 2016 and 2017 cropping seasons and comprised four treatments: ridge tillage without any mulching (RT), flat tillage without any mulch (FT), flat tillage with rice straw mulched on the ridge tillage (FTRSM) and ridge tillage with rice straw mulched on the ridge tillage (RTRSM). The RT and RTRSM increased soil temperature and decreased soil humidity and improved soil total nitrogen, total phosphorus, available potassium and organic matter. RT and RTRSM increased the root length density, root surface area, root diameter and root activity of faba beans at flowering and harvest periods. The RT and RTRSM also increased the nitrogen, phosphorus, potassium absorption and the yield of faba beans. These results indicated that ridge tillage and straw mulching affect faba bean growth by improving soil moisture conditions and providing good air permeability and effective soil nutrition supply. This study provides a theoretical basis for the high yield cultivation improvement of faba beans.

scholarly journals The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 114
Author(s):  
Katarzyna Pentoś ◽  
Krzysztof Pieczarka ◽  
Kamil Serwata
Keyword(s):  
Electrical Conductivity ◽  
Magnetic Susceptibility ◽  
Precision Agriculture ◽  
Soil Compaction ◽  
Soil Layer ◽  
Soil Parameters ◽  
Soil Electrical Conductivity ◽  
Electrical Parameters ◽  
Management Zones ◽  
Additional Information

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones.

scholarly journals Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 208
Author(s):  
Daniel Queirós da Silva ◽  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Danilo Rabino ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Real Data ◽  
Relevant Information ◽  
Normal Operation ◽  
Area Index ◽  
Sensory Data ◽  
The Right

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

A Review on SWAT Model for Stream Flow Simulation

2013 ◽  
Vol 726-731 ◽  
pp. 3792-3798
Author(s):  
Wen Ju Zhao ◽  
Wei Sun ◽  
Zong Li Li ◽  
Yan Wei Fan ◽  
Jian Shu Song ◽  
...  
Keyword(s):  
Spatial Data ◽  
Stream Flow ◽  
Assessment Tool ◽  
Swat Model ◽  
Flow Simulation ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Rs And Gis ◽  
Model Research ◽  
Gis And Rs

SWAT (Soil and Water Assessment Tool) model is one of distributed hydrological model, based on spatial data offered by GIS and RS. This article mainly introduces the SWAT model principle, structure, and it is the application of stream flow simulation in China and other countries, then points out the deficiency existing in the process of model research. In order to service in water resources management work better, experts and scholars further research the rate constant and uncertainty of the simplification of the model parameters, and the combination of RS and GIS to use, and hydrological scale problems.

Identification of Agricultural Management Zones Through Clustering Algorithms with Thermal and Multispectral Satellite Imagery

2017 ◽  
Vol 25 (Suppl. 1) ◽  
pp. 121-140
Author(s):  
R. B. Arango ◽  
A. M. Campos ◽  
E. F. Combarro ◽  
E. R. Canas ◽  
I. Díaz
Keyword(s):  
Satellite Imagery ◽  
Precision Agriculture ◽  
Clustering Algorithms ◽  
Economic Benefits ◽  
Agricultural Management ◽  
Landsat 8 ◽  
Special Equipment ◽  
Management Zones ◽  
Ndvi Index ◽  
Cost Efficient

Precision Agriculture entails the appropriate management of the inherent variability of soil and crops, resulting in an increase of economic benefits and a reduction of environmental impact. However, site-specific treatments require maps of the soil variability to identify areas of land that share similar properties. In order to produce these maps, we propose a cost-efficient method that combines clustering algorithms with publicly available satellite imagery. The method does not require exploring the parcels with any special equipment or taking samples of the soil for laboratory analysis. The proposed method was tested in a case study for three vineyard parcels with topographical dissimilarities. The study compares different spectral and thermal bands from the Landsat 8 satellite as well as vegetation and moisture indices to determine which one produces the best clustering. The experimental results seem promising for identification of agricultural management zones. The findings suggest that thermal bands produce better clustering than those based on the NDVI index.

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