Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network

Development and Application of an IoT-Based System for Soil Water Status Monitoring in a Soil Profile

Soil water content (θ), matric potential (h) and hydraulic conductivity (K) are key parameters for hydrological and environmental processes. Several sensors have been developed for measuring soil θ-h-K relationships. The cost of such commercially available sensors may vary over several orders of magnitude. In recent years, some sensors have been designed in the framework of Internet of Things (i.e., IoT) systems to make remote real-time soil data acquisition more straightforward, enabling low-cost field-scale monitoring at high spatio-temporal scales. In this paper, we introduce a new multi-parameter sensor designed for the simultaneous estimation of θ and h at different soil depths and, due to the sensor's specific layout, the soil hydraulic conductivity function via the instantaneous profile method (IPM). Our findings indicate that a second-order polynomial function is the most suitable model (R2 = 0.99) for capturing the behavior of the capacitive-based sensor in estimating θ in the examined soil, which has a silty-loam texture. The effectiveness of low-cost capacitive sensors, coupled with the IPM method, was confirmed as a viable alternative to time domain reflectometry (TDR) probes. Notably, the layout of the sensor makes the IPM method less labor-intensive to implement. The proposed monitoring system consistently demonstrated robust performance throughout extended periods of data acquisition and is highly suitable for ongoing monitoring of soil water status.

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone

The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things.

A Data-Driven Approach for Building the Profile of Water Storage Capacity of Soils

The soil water storage capacity is critical for soil management as it drives crop production, soil carbon sequestration, and soil quality and health. It depends on soil textural class, depth, land-use and soil management practices; therefore, the complexity strongly limits its estimation on a large scale with conventional-process-based approaches. In this paper, a machine learning approach is proposed to build the profile of the soil water storage capacity. A neural network is designed to estimate the soil moisture from the meteorology data input. By taking the soil moisture as a proxy in the modelling, the training captures those impact factors of soil water storage capacity and their nonlinear interaction implicitly without knowing the underlying soil hydrologic processes. An internal vector of the proposed neural network assimilates the soil moisture response to meteorological conditions and is regulated as the profile of the soil water storage capacity. The proposed approach is data-driven. Since the low-cost soil moisture sensors have made soil moisture monitoring simple and the meteorology data are easy to obtain, the proposed approach enables a convenient way of estimating soil water storage capacity in a high sampling resolution and at a large scale. Moreover, an average root mean squared deviation at 0.0307m3/m3 can be achieved in the soil moisture estimation; hence, the trained model can be deployed as an alternative to the expensive sensor networks for continuous soil moisture monitoring. The proposed approach innovatively represents the soil water storage capacity as a vector profile rather than a single value indicator. Compared with the single value indicator, which is common in hydrology, a multidimensional vector can encode more information and thus has a more powerful representation. This can be seen in the anomaly detection demonstrated in the paper, where subtle differences in soil water storage capacity among the sensor sites can be captured even though these sensors are installed on the same grassland. Another merit of vector representation is that advanced numeric methods can be applied to soil analysis. This paper demonstrates such an advantage by clustering sensor sites into groups with the unsupervised K-means clustering on the profile vectors which encapsulate soil characteristics and land properties of each sensor site implicitly.

Study of the distribution of Glycyrrhiza uralensis production areas as well as the factors affecting yield and quality

Wild licorice in China is mainly distributed in northern China, such as Gansu, Ningxia, and Inner Mongolia Provinces. The origin of wild licorice has varied among historical periods. The cultivated origin of planted licorice has the same as 59.26% of wild licorice. The distribution of cultivated licorice was shifted to the northwest relative to that of wild licorice. The yield and quality of cultivated licorice vary greatly from different origins, showing a certain pattern of variation from west to east. The same batch of licorice seedlings was planted at 8 sites overlapping the main licorice production areas in China. The yield and quality of licorice in the Baicheng experimental plot were low. The yield of licorice in the Jingtai and Altay experimental plots was high, but the quality was poor. The quality of licorice in Chifeng and Yuzhong experimental sites was high, but the yield was low. Principal component analysis of environmental and soil factors generated five characteristic roots with a cumulative contribution rate of 80%, three of which were related to soil and referred to as the soil charge factor, soil water factor, and soil nutrient factor, and the load coefficients of the water and nutrient factor were the largest. Soil conditions, especially water and nutrients, might have a substantial effect on the observed changes in the licorice production area. Generally, the regulation of water and nutrients merits special attention when selecting areas for the production and cultivation of licorice. This study can provide reference for the selection of cultivated licorice production areas and the research of high-quality cultivation techniques.

A review on the plant resources of important medicinal licorice

ETHNOPHARMACOLOGICAL RELEVANCE

Glycyrrizae Radix et Rhizoma has various effects, including tonifying the spleen and qi, clearing heat and toxic substances, eliminating phlegm, relieving cough and pain, and harmonizing the effects of other medicines. It is widely used in the pharmaceutical and food industries.

AIMS OF THE STUDY

This review systematically collates the identification of Glycyrrhiza Linn. species with medicinal value and their distributions. The morphological and genetic characteristics, distribution, quantity of reserves in China, suitable environment, and area of suitable habitat of important medicinal species were reviewed. The effects of the natural environment and cultivation management (water and nutrients) on the growth and quality of licorice were reviewed. The aim is to make people have a systematic understanding of the status of medicinal plant resources of the G. Linn., and understand the natural and human factors that affect its quality, so as to provide help for the collection of resources of the important medicinal licorice and the human control of its quality in the future.

MATERIALS AND METHODS

We conducted extensive searches of the primary literature, master's and doctoral theses, and pharmacopeias of many countries using PubMed, Geenmedical, CNKI, Web of Science, SCI-hub and other databases. The keywords used in searches included "classification of Glycyrrhiza," "medicinal Glycyrrhiza," "distribution of Glycyrrhiza," and "suitable environment for Glycyrrhiza" The results of research conducted by our research group on the morphological and genetic characteristics, natural distribution, and effects of artificial regulation on the growth and quality of licorice were summarized.

RESULTS

There are approximately 29 species of G. Linn. worldwide, including 15 species with medicinal value. These species occur on all continents except Antarctica across 41 countries. Only one licorice is recorded in Indian pharmacopoia,two species are recorded in US and Japanese pharmacopoeias,and three species are recorded in most national pharmacopoeias: G. glabra Linn., G. uralensis Fisch. and G. inflata Batalin. These three medicinal licorice species are mainly distributed in Eurasia, especially Central Asia. The main morphological differences between these three medicinal licorice species are in the leaves, inflorescences, pods, and seeds, and they can be distinguished by ITS and psbA-trnH sequences. The reserves of wild licorice in China have decreased annually to 1.04 million(t) in 2010. The cultivation area of G. uralensis Fisch in China is currently approximately 26,900 hm². Soil conditions have a substantial effect on the yield and quality of G. uralensis Fisch, especially water and nutrients. Appropriate irrigation and fertilization measures can enhance the quality of G. uralensis Fisch.

CONCLUSIONS

G. Linn. species and their natural distributions were summarized. The morphology, genetic characteristics, suitable environment, and area of suitable habitat of three medicinal licorice species collected in major countries were described. The main environmental conditions and cultivation measures affecting their growth and medicinal quality were determined. This article provides a comprehensive review on G. Linn. medicinal plant resources to enhance the future use of these resources.

Natural detoxification of antibiotics in the environment: A one health perspective

The extended concept of one health integrates biological, geological, and chemical (bio-geo-chemical) components. Anthropogenic antibiotics are constantly and increasingly released into the soil and water environments. The fate of these drugs in the thin Earth space ("critical zone") where the biosphere is placed determines the effect of antimicrobial agents on the microbiosphere, which can potentially alter the composition of the ecosystem and lead to the selection of antibiotic-resistant microorganisms including animal and human pathogens. However, soil and water environments are highly heterogeneous in their local composition; thus the permanence and activity of antibiotics. This is a case of "molecular ecology": antibiotic molecules are adsorbed and eventually inactivated by interacting with biotic and abiotic molecules that are present at different concentrations in different places. There are poorly explored aspects of the pharmacodynamics (PD, biological action) and pharmacokinetics (PK, rates of decay) of antibiotics in water and soil environments. In this review, we explore the various biotic and abiotic factors contributing to antibiotic detoxification in the environment. These factors range from spontaneous degradation to the detoxifying effects produced by clay minerals (forming geochemical platforms with degradative reactions influenced by light, metals, or pH), charcoal, natural organic matter (including cellulose and chitin), biodegradation by bacterial populations and complex bacterial consortia (including "bacterial subsistence"; in other words, microbes taking antibiotics as nutrients), by planktonic microalgae, fungi, plant removal and degradation, or sequestration by living and dead cells (necrobiome detoxification). Many of these processes occur in particulated material where bacteria from various origins (microbiota coalescence) might also attach (microbiotic particles), thereby determining the antibiotic environmental PK/PD and influencing the local selection of antibiotic resistant bacteria. The exploration of this complex field requires a multidisciplinary effort in developing the molecular ecology of antibiotics, but could result in a much more precise determination of the one health hazards of antibiotic production and release.