42. Validation of precision agriculture soil mapping services under practical conditions

2021 ◽  
Author(s):  
C. Kempenaar ◽  
F. Tigchelhoff ◽  
J.A. Booij ◽  
S. Nysten ◽  
C.G. Kocks
Keyword(s):  
Precision Agriculture ◽  
Soil Mapping ◽  
Agriculture Soil

scholarly journals Performance Evaluation of Artificial Intelligence on Soil Property Detection

2018 ◽  
Vol 4 (10) ◽  
pp. 5
Author(s):  
Smriti Singhatiya ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Artificial Intelligence ◽  
Precision Agriculture ◽  
Crop Production ◽  
Nutrient Status ◽  
Soil Testing ◽  
Support Vector ◽  
Soil Test ◽  
New Era ◽  
Agriculture Soil ◽  
Historical Role

Now-a-days there is a need to study the nutrient status in lower horizons of the soil. Soil testing has played historical role in evaluating soil fertility maintenance and in sustainable agriculture. Soil testing shall also play its crucial role in precision agriculture. At present there is a need to develop basic inventory as per soil test basis and necessary information has to be built into the system for translating the results of soil test to achieve the crop production goal in new era. To achieve this goal artificial intelligence approach is used for predicting the soil properties.  In this paper for analysing these properties support vector regression (SVR), ensembled regression (ER) and neural network (NN) are used. The performance is evaluated with respect to MSE and RMSE and it is observed that ER outperforms better with respect to SVR and NN.

scholarly journals Progress in soil geography I: Reinvigoration

2019 ◽  
Vol 43 (6) ◽  
pp. 827-854 ◽  
Author(s):  
Bradley A Miller ◽  
Eric C Brevik ◽  
Paulo Pereira ◽  
Randall J Schaetzl
Keyword(s):  
Land Use Planning ◽  
Precision Agriculture ◽  
Critical Role ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Science ◽  
Accurate Information ◽  
Soil Maps ◽  
Geospatial Technologies ◽  
Soil Geography

The geography of soil is more important today than ever before. Models of environmental systems and myriad direct field applications depend on accurate information about soil properties and their spatial distribution. Many of these applications play a critical role in managing and preparing for issues of food security, water supply, and climate change. The capability to deliver soil maps with the accuracy and resolution needed by land use planning, precision agriculture, as well as hydrologic and meteorologic models is, fortunately, on the horizon due to advances in the geospatial revolution. Digital soil mapping, which utilizes spatial statistics and data provided by modern geospatial technologies, has now become an established area of study for soil scientists. Over 100 articles on digital soil mapping were published in 2018. The first and second generations of soil mapping thrived from collaborations between Earth scientists and geographers. As we enter the dawn of the third generation of soil maps, those collaborations remain essential. To that end, we review the historical connections between soil science and geography, examine the recent disconnect between those disciplines, and draw attention to opportunities for the reinvigoration of the long-standing field of soil geography. Finally, we emphasize the importance of this reinvigoration to geographers.

Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

2020 ◽  
Author(s):  
Nada Mzid ◽  
Stefano Pignatti ◽  
Irina Veretelnikova ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Central Italy ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Field Scale ◽  
Umbria Region ◽  
Digital Soil Maps

<p>The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).</p>

Integration And Importance Of Soil Mapping Results In The Precision Agriculture

2019 ◽  
Author(s):  
Mehmet Ali Cullu ◽  
Mustafa Teke ◽  
Nusret Mutlu ◽  
Ufuk Turker ◽  
Ali Volkan Bilgili ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Soil Mapping

scholarly journals Geotechnologies and Soil Mapping for Delimitation of Management Zones as an Approach to Precision Viticulture

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
José Maria Filippini Alba ◽  
Carlos Alberto Flores ◽  
Alberto Miele
Keyword(s):  
Precision Agriculture ◽  
Chemical Properties ◽  
Unsupervised Classification ◽  
Soil Mapping ◽  
Grid Pattern ◽  
Management Zones ◽  
Chemical Properties Of Soils ◽  
Elevation Model ◽  
Definition Of ◽  
Physical And Chemical

Data of the physical and chemical properties of soils from three vineyards located in Vale dos Vinhedos, Bento Gonçalves, Rio Grande do Sul state, in southern Brazil, were processed. Soil mapping was performed by means of four profiles and the digital elevation model in detailed scale. Then, superficial soils (0–20 cm) were sampled according to a grid pattern. Analysis of variance (ANOVA), kriging, and unsupervised classification methods were applied on physical and chemical data of superficial soils sampled according to grid pattern. This study aimed to compare both methods, the conventional soil mapping and the map produced with superficial soil sampling, about their potential for definition of the management zones, as an approach for precision agriculture. Maps elaborated by conventional soil mapping overlapped partially with the maps derived from superficial sampling, probably due to the specific methodological differences of each case. Anyway, both methods are complementary because of the focus on vertical variability and horizontal variability, respectively. In that sense, slope appears as significant edaphic parameter, due to its control on water circulation in the profile of soil.

scholarly journals From Conventional to Precision Fertilization: A Case Study on the Transition for a Small-Medium Farm

2021 ◽  
Vol 3 (2) ◽  
pp. 438-446
Author(s):  
Massimo Brambilla ◽  
Elio Romano ◽  
Pietro Toscano ◽  
Maurizio Cutini ◽  
Marcello Biocca ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Crop Production ◽  
Production Quality ◽  
Soil Mapping ◽  
Variable Rate ◽  
Research Facility ◽  
Commercial Yield ◽  
Precision Fertilization ◽  
Production Dynamics

At the CREA research facility of Treviglio (Bergamo, Italy), to provide farmers with valuable hints for the transition from conventional to precision agriculture, information on crop production dynamics (Maize and Triticale) has been obtained using real-time soil mapping (resistivity technique) and production quality and quantity monitoring with a commercial yield mapping apparatus. The geostatistical processing of data resulted in the same zoning for Triticale, meaning that the characteristics of soil influenced crop behavior more than the variability resulting from other factors, which suggests that improvements in product yields can be planned and achieved acting, for instance, on variable rate distribution of fertilizers. The importance of the acquired data can help farmers to manage factors that are external to their plots of land.

Taking digital soil mapping to the field: Lessons learned from the Water Smart Agriculture soil mapping project in Central America

2020 ◽  
Vol 22 ◽  
pp. e00285 ◽  
Author(s):  
Phillip R. Owens ◽  
Minerva J. Dorantes ◽  
Bryan A. Fuentes ◽  
Zamir Libohova ◽  
Axel Schmidt
Keyword(s):  
Central America ◽  
Lessons Learned ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Agriculture Soil ◽  
Smart Agriculture

scholarly journals Potential of hyperspectral remote sensing for field scale soil mapping and precision agriculture applications

2012 ◽  
Vol 7 (4) ◽  
pp. 43 ◽  
Author(s):  
Raffaele Casa ◽  
Fabio Castaldi ◽  
Simone Pascucci ◽  
Stefano Pignatti
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Hyperspectral Remote Sensing ◽  
Soil Mapping ◽  
Field Scale

Application of Improved K-Means Algorithm Density in the Grades of Cultivated Land Fertility Evaluation

2014 ◽  
Vol 644-650 ◽  
pp. 2047-2050
Author(s):  
Li Ying Cao ◽  
He Long Yu ◽  
Gui Fen Chen ◽  
Ting Ting Yang
Keyword(s):  
Soil Fertility ◽  
Precision Agriculture ◽  
Cluster Center ◽  
Selection Algorithm ◽  
Density Region ◽  
Data Set ◽  
Agriculture Soil ◽  
Initial Cluster ◽  
High Density Region ◽  
Clustering Center

precision agriculture, soil fertility evaluation is the foundation of variable rate fertilization, the initial clustering centers of K means algorithm soil fertility levels in the traditional evaluation methods generated randomly from the data set, the clustering result is not stable. This paper proposes an improved K-means algorithm density algorithm to optimize the initial clustering center selection algorithm based on K, the most far away to each other in high density region point as the initial cluster center. Experiments show that, the improved K-means algorithm can eliminate the dependence on the initial cluster center; the clustering result has been greatly improved.

scholarly journals Adaptation of regional digital soil mapping for precision agriculture

2016 ◽  
Vol 17 (5) ◽  
pp. 588-607 ◽  
Author(s):  
Mats Söderström ◽  
Gustav Sohlenius ◽  
Lars Rodhe ◽  
Kristin Piikki
Keyword(s):  
Precision Agriculture ◽  
Soil Mapping ◽  
Digital Soil Mapping
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