A data-driven approach to soil moisture collection and prediction using a wireless sensor network and machine learning techniques

Agriculture has been one of the most underinvestigated areas in technology, and the development of Precision Agriculture is still in its early stages. This thesis proposes a data-driven methodology that aims to address some of the current problems in Precision Agriculture development. Soil moisture, a key factor in the crop growth cycle, is selected as an example to demonstrate the effectiveness of our data-driven approach. The success of the data-driven approach depends on two factors: (1) the quality of the data gathered and (2) the effectiveness of its analysis and interpretation. Previous studies have focused on addressing these factors separately, by either developing hardware for collecting soil moisture data or building efficient data analysis models. In our work, we take a holistic approach by addressing problems on both ends and designing an integrated system for Precision Agriculture that uses a wireless sensor network and machine learning techniques. On the collection side, a reactive wireless sensor node is developed that aims to capture the dynamics of soil moisture while sampling at relatively low frequency to save energy. The sensor node dynamically adjusts its sampling frequency based on soil moisture readings and can be easily configured to meet the specific needs applications. The hardware is prototyped using MicaZ mote and VH400 soil moisture sensor. On the data analysis side, a site-specific soil moisture prediction framework is proposed based on models generated by the statistically sound machine learning techniques SVM (support vector machine) and RVM (relevance vector machine). The framework can integrate inputs from other reliable data sources to improve its accuracy. The proposed framework is evaluated under a historical dataset on 9 sites across Illinois. It achieves low error rates (15%) and high correlations (95%) between predicted values and actual values when forecasting soil moisture about 2 weeks ahead

Prediction of water retention of soils from the humid tropics by the nonparametric k-nearest neighbor approach

Nonparametric approaches such as the k-nearest neighbor (k-NN) approach are considered attractive for pedotransfer modeling in hydrology; however, they have not been applied to predict water retention of highly weathered soils in the humid tropics. Therefore, the objectives of this study were: to apply the k-NN approach to predict soil water retention in a humid tropical region; to test its ability to predict soil water content at eight different matric potentials; to test the benefit of using more input attributes than most previous studies and their combinations; to discuss the importance of particular input attributes in the prediction of soil water retention at low, intermediate, and high matric potentials; and to compare this approach with two published tropical pedotransfer functions (PTFs) based on multiple linear regression (MLR). The overall estimation error ranges generated by the k-NN approach were statistically different but comparable to the two examined MLR PTFs. When the best combination of input variables (sand + silt + clay + bulk density + cation exchange capacity) was used, the overall error was remarkably low: 0.0360 to 0.0390 m(3) m(-3) in the dry and very wet ranges and 0.0490 to 0.0510 m(3) m(-3) in the intermediate range (i.e., -3 to -50 kPa) of the soil water retention curve. This k-NN variant can be considered as a competitive alternative to more classical, equation-based PTFs due to the accuracy of the water retention estimation and, as an added benefit, its flexibility to incorporate new data without the need to redevelop new equations. This is highly beneficial in developing countries where soil databases for agricultural planning are at present sparse, though slowly developing

Ensemble evaluation of hydrological model hypotheses

It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. As an example application, 72 variants of a single soil moisture accounting store are tested as simplified hypotheses of runoff generation at six experimental grassland field-scale lysimeters through model rejection and a novel diagnostic scheme. The fields, designed as replicates, exhibit different hydrological behaviors which yield different model performances. For fields with low initial discharge levels at the beginning of events, the conceptual stores considered reach their limit of applicability. Conversely, one of the fields yielding more discharge than the others, but having larger data gaps, allows for greater flexibility in the choice of model structures. As a model learning exercise, the study points to a “leaking” of the fields not evident from previous field experiments. It is discussed how understanding observational uncertainties and incorporating these into model diagnostics can help appreciate the scale of model structural error

Review of literature relating to the modeling of soil temperatures based on meteorological factors

Abstracts of 72 papers, journal articles, and other publications are presented. The applicabilities of each is assessed for use in improving winterkill parameters for a winter wheat model

Data-model comparison of temporal variability in long-term time series of large-scale soil moisture

Acknowledgments This work has been supported by the Swedish University strategic environmental research program Ekoklim and the Swedish Research Council Formas (project 2012-790). The soil moisture data were downloaded from the Ameriflux website: funding for AmeriFlux data resources was provided by the U.S. Department of Energy's Office of Science. GPCC Precipitation data, GHCN Gridded V2 data, NARR data, and CPC US Unified Precipitation data were obtained from the Web site of NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, at http://www.esrl.noaa.gov/psd/.Peer reviewedPublisher PD

Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense dataset

A non-linear Granger-causality framework to investigate climate-vegetation dynamics

Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate-vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate-vegetation dynamics