Strobe -- Towards Low Cost Soil Sensing Using Wi-Fi

Soil pH and electrical conductivity (EC) are two major chemical properties affecting nutrient availability and rice grain yield. Therefore, this study was conducted to investigate the variation of pH and EC in the topsoil layer (0-15 cm) of rice fields as affected by major water source used for rice cultivation, rice-based cropping system adopted and rice growing soil orders in different agro-climatic zones (ACZs) in Sri Lanka. A total of 998 soil samples were collected from lowland rice fields. Both pH and EC were measured in 1:5 soil: water extracts. The pH of soil samples was in the range of 3.0-7.7 with a mean value of 5.0. Moreover, 75% of the soil samples had pH values below the optimum range for rice cultivation (5.5-7.0). Values of pH observed in Dry Zone soils were higher (5.2) than those in Wet Zone (4.4). Soil EC values ranged between 1.0 - 3,100 μScm−1 with a mean value of 148.5 μScm−1. Soil EC was similar among climatic zones (P>0.05). Upcountry Intermediate zone recorded the highest soil EC than that in other ACZs (P<0.05). Moreover, 73%, 22%, 3%, 2% and 0.1% of soil samples recorded EC values in the ranges of less than 150 (non-saline), 150-400 (slightly saline), 400-800 (moderately saline), 800-2000 (highly saline) and more than 2000 (very highly saline) μScm−1, respectively. Considering micro (e.g. paddy track) and macro (e.g. ACZ) scale spatial heterogeneity in soil pH and EC, appropriate site-specific strategies need to be adopted to improve soil pH and EC to suit sustainable rice crop production.

Saline irrigation scheduling for potted geranium based on soil electrical conductivity and moisture sensors

The accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of semiarid regions. The objective of this study was to achieve the best estimation of electrical conductivity variables from salt-affected soils in a south Mediterranean region using Sentinel-2 multispectral imagery. In order to realize this goal, a test was carried out using electrical conductivity (EC) data collected in central Tunisia. Soil electrical conductivity and leaf electrical conductivity were measured in an olive orchard over two growing seasons and under three irrigation treatments. Firstly, selected spectral salinity, chlorophyll, water, and vegetation indices were tested over the experimental area to estimate both soil and leaf EC using Sentinel-2 imagery on the Google Earth Engine platform. Subsequently, estimation models of soil and leaf EC were calibrated by employing machine learning (ML) techniques using 12 spectral bands of Sentinel-2 images. The prediction accuracy of the EC estimation was assessed by using k-fold cross-validation and computing statistical metrics. The results of the study revealed that machine learning algorithms, together with multispectral data, could advance the mapping and monitoring of soil and leaf electrical conductivity.

The soil-overturning properties is an important matter in the working results by rotary tillage, but the mesuring method is not still established in Japan. The purpose of this study is to obtain establishment of the mesuring method and the accurate ratio of soil displacement by rotary tillage. This paper deals with two methods calcurating the ratio of soil displacement.In this experiment, we adopted the method to use the soil mixed kalium chloride (KCI), as electrical conductivity and concentration of solution of KCI were in proportion to each other. After the electrical conductivity of the soil which mixed two level's soils differing in electrical conductivity was mesured, we discussed on a hypothetical equation to calculate the electrical conductivity of the mixed soils. The deviation between mesured electrical conductivity and calculated one showed the range of -3.4-2.3%. However it is assumed that this degree of errors are practically admitted.From application of the hypothetical equation, the two methods (simultaneous equations of three unknowns and conditional inequality) to obtain the ratio of soil displacement were derived. Mesuring the electrical conductivity of soils which were picked before and after rotary tillage, we can easily estimate by these methods the each ratio that a soil layer before tillage moved to the three layers after tillage.The method by simultaneous equations of three unknowns must perform the experiment of three plots in which the mixture soil of KCI was put at each layer before rotary tillage. Moreover, this method needs to make the uniform conditions of the three experimental plots.In case of the method by conditional inequality, the ratio of soil displacement is calculated with a certain range. Therefore if this range increases, the estimating inequality become useless. However, increasing the electrical conductivity of mixture soil of KCI, it is possible to decrease the range within 1%.

Soil salinization is one of the major land degradation problems affecting about 932 million hectares (Mha) in the World, and about 6.74 Mha in India. Since soil salinization affects the plant growth which affects agricultural production and food security, it needs to be monitored and managed well. The existing field devices for assessing soil salinity are expensive (> US${\$}$ 150 for the sensor alone). So, a lowcost sensor (about US70) has been designed for assessing soil salinity by measuring soil moisture and electrical conductivity (EC). The device uses oscillation frequency shift technique for measuring soil moisture, and electrical resistivity is used for bulk EC measurement. The sensor prototype has been designed and tested using downward infiltration method with 14 soil samples. Regression models have been developed for soil moisture estimation from frequency measurements. Pore water EC has been estimated from bulk EC measurements for determining soil salinity. The regression models developed for soil moisture estimation from frequency has R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ranging from 0.92 - 0.95, which is significant for accepting the models. The bulk EC measurements were transformed to pore water EC, and a threshold regression model has been developed from for determining soil salinity at different moisture levels. The threshold pore water EC regression model has an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.74, which is acceptable for determining salinity from pore water EC values.

This research explores the use of soil electrical conductivity (EC) in rice cultivation, focusing on soil EC correlation with vegetation indices (NDVI, NDRE) and soil EC impact on yield. A total of 228 soil EC, NDVI, and NDRE data points were collected from an 11,000 m² field in Hau Giang and Can Tho City. Soil EC was measured at the beginning of the season, and at 39 and 49 days after sowing. NDVI was recorded on days 39, 49, 60, and 72, with initial values set to zero due to no crop presence. Results show that soil EC decreased from the start to day 49, while NDVI and NDRE increased. Spearman correlation analysis revealed strong relationships between early soil EC and NDVI at days 39 and 49 (r = 0.82, 0.71), and between soil EC at day 49 (EC 49) and NDRE at later stages (r = 0.594, 0.565). Additionally, 150 soil EC and yield samples from five Mekong Delta regions were used in an LSTM model (70% training, 30% testing), achieving high accuracy (RMSE = 0.0919, MAE = 0.0634, R2 = 0.8492). The findings suggest that soil EC is a valuable indicator for monitoring crop growth and managing rice production, particularly in areas affected by salinity due to climate change.

Electrical conductivity of some soils in Trinidad as affected by density, water and peat content

To clarify the nutrient characteristics of 'salt island' and 'fertile island' effects in saline-alkali soil, the native Tamarix chinensis of the Yellow River Delta (YRD) was selected to measure its soil pH,electrical conductivity (EC), organic carbon (SOC),total nitrogen (N),total phosphorus (P) and their stoichiometry characteristics at different soil depths. The results showed that soil pH and EC increased with the increasing soil depth. Soil EC and P in the 0-20 cm layer decreased and increased from canopied area to interspace,respectively. SOC,N,N/P and C/P in the 20-40 cm soil layer decreased, and C/N increased from the shrub center to interspace. SOC and N contents between island and interspace both decreased but P content decreased firstly and then increased with the increasing soil depth. Soil pH correlated positively with EC. In addition, pH and EC correlated negatively with C,N,P contents and their ecological stoichiometry.

Tomato nitrogen accumulation and fertilizer use efficiency on a sandy soil, as affected by nitrogen rate and irrigation scheduling

This study aimed to elucidate the response of soil microbial communities to saline soil amelioration via biological organic fertilizer. A year-long experiment was conducted on coastal saline soil, employing water and fertilizer strategies. Three treatments were compared: dry field (control, CK), paddy field (W), and combined dry and irrigated fields with biological organic fertilizer (BW). Soil DNA was extracted and sequenced using high-throughput methods, revealing significant reductions in soil electrical conductivity (EC) and pH with W and BW treatments. Moreover, the BW treatment notably increased soil organic carbon content by 17.2%, as well as soil urease and alkaline phosphatase activity. Fungal community richness increased, with the BW treatment showing a 36% rise in the ACE index and a 24% increase in the Shannon index, while the Simpson index decreased by 59%. Dominant fungal phyla were Ascomycota, Mortierellomycota, and Basidiomycota, with Basidiomycota prevailing at the genus level. Redundancy analysis (RDA) indicated that soil pH, EC, and organic carbon were key determinants of fungal community distribution, with the BW treatment correlating negatively with pH and salt and positively with soil organic carbon (SOC). Fungal functional groups varied among treatments, with saprophytic fungi predominating, but the BW treatment showed a higher relative abundance of animal pathogenic fungi. In summary, the integration of biological organic fertilizer with flooding ameliorates soil properties and influences the changes in soil fungal community structure and function in the short term. These results could enhance the scientific basis for the efficient utilization and development of saline soil resources in coastal areas.

The study’s specific objective is to identify prospective cashew nut (Anacardium occidentale) producing areas in the Bankura district based on soil availability and land suitability in red and lateritic soil. The index of crop suitability (ICS) analysis was carried out utilizing the Fuzzy-analytical hierarchy process (F-AHP) approach and 16 geo-environmental variables such as soil texture, soil organic carbon (OC), Soil electrical conductivity (EC), soil pH, slope, rainfall, etc. The criteria were also developed following FAO's crop suitability index for the distinction between S (suitable) and N (unsuitable) order. According to the cashew suitability class, 9.24% of cashew growth regions can be categorised as highly suitable, 34.57% as moderately suitable, and 19.59% as marginally suitable. The F-AHP weight shows the important factors of cashew cultivation that are soil OC and EC. The results demonstrate that cashew production is more beneficial in the red and lateritic soil.

Paddy fields with flooded water are commonly known as the main source of methane (CH 4 ) emission. Measurement of methane gas is complicated and costly due to utilizing advanced instrumentation such as Gas Chromatography (GC). In addition, some soil parameter data that is effected on methane emissions were sometimes limited. The current study proposed the artificial neural network (ANN) models to predict methane emission from paddy fields using soil parameters of soil moisture (SM), soil temperature (Ts) and soil electrical conductivity (EC). The models were performed based on the experimental data in one planting season during 20 January to 13 May 2018. The ANN models were developed by backpropagation learning algorithm. The sigmoid function was used as an activation function with three layer, i.e. input, hidden and output layers. There were six ANN models with different input parameters. Model 1 with three input parameters (SM, Ts, EC) was the best model with highest R2 (0.97) and lowest RMSE (32.9 mg/m2/d). However, the model needs more input parameters. The Model 2 can be used as an alternative to predict methane emission if there were only two measured parameter, i.e., SM and Ts. This model was fairly satisfied as indicated by R2 of 0.64 and moderate RMSE of 122.8 mg/m2/d

We investigated the community structure of ground-active arthropods as well as vegetation and soil properties in Caragana shrub plantations under grazing and exclosure management across spring, summer and autumn in Yanchi County of Ningxia in northern China. The aim of this study was to uncover the responses of ground-active arthropods in shrub plantations to grazing management in desertified regions. The results showed that: 1) plant height, soil fine sand content, and soil electrical conductivity were significantly lower, whereas soil bulk density and coarse sand were significantly higher in shrub plantations under grazing than exclosure. 2) There were 40 families from 13 orders captured. The dominant groups included Formicidae and Tenebrionidae families, which comprised 68.75% of the total individuals. There were four common groups occupying 20.82% of the total individuals. The remaining 34 groups were relatively rare, only accounting for 10.44% of the total. Across the three seasons, the composition of ground-active arthropod community was significantly different between grazing and exclosure, which indicates the sensitivity and adaptability of ground-active arthropods to environmental changes including grazing management and seasonal changes. 3) There was a significant effect of grazing on total abundance of ground-active arthropods in shrub plantations, with significantly higher values under grazing than under exclosure. There was no significant effect of grazing management on group richness and diversity of ground-active arthropods in shrub plantations. 4) There was a correlation of total abundance, evenness index and the Simpson index with plant abundance, vegetation height, soil moisture, soil pH and electrical conductivity. There was a significant correlation of the Shannon index with vegetation height, soil moisture and soil fine sand. Plant density, and soil pH, soil moisture and soil temperature were the key factors driving the structure of ground-active arthropod communities in shrub plantations under gra-zing management across seasons based on the partial RDA results. It was concluded that the variations of plant height, soil pH, soil moisture and soil temperature under grazing management could result in different ground-active arthropod taxon to changing habitats. The conservation effect of shrub on ground-active arthropod could reduce the negative impacts of grazing. Spring grazing could enhance ground-active arthropod abundances in shrub plantations. It was necessary to pay attention to prevention of insects which are caused by spring grazing in shrub plantations.

Corrosion is one of the major factors leading to the failure of buried pipelines. Soil properties such as aeration, moisture content and level of compaction are known to cause variations in the level of corrosion of buried metallic structures. It is known that, at a particular soil moisture content, the corrosion rate reaches a maximum value. While this phenomenon is generally understood, an explanation from a soil mechanics perspective with mechanisms for soil water continuity and mass transport processes is currently lacking. This work fills this void by modelling the moisture-controlled diffusion transport and electrical conductivity in soil coupled to the electrochemical activity on the buried metal surface. Variations in the electrical conductivity and oxygen diffusion in sand at different degrees of saturation were determined experimentally. The results were used as input parameters in a finite element model. Results from the coupled finite element model were compared with experimental results from electrochemical corrosion tests. The tests were conducted on cast iron specimen buried in sand and the corrosion behaviour under various aeration regimes were studied. The presence of an optimum moisture/aeration regime, where the corrosion rate becomes a maximum was demonstrated and the mechanisms behind this phenomenological behaviour are discussed in this paper. The modelling and experimental results are expected to be useful in developing non-intrusive testing methods for underground corrosion.

Agriculture is the main sources of all countries throughout the world. Many plants are easily affected to diseases due to changes in various factors such as soil moisture, varying climatic conditions, nutrients, temperature etc. Automatic plant disease detection and monitoring system using k-means algorithm helps to monitor the plant diseases through moisture values of the soil. It helps to reduce water use. The system is developed with threshold values of soil moisture sensor that is programmed into a microcontroller based gateway to control water quantity. It involves two processes such as disease detection and monitoring the soil. At first stage, wireless camera is used to capture the plant image and processed using image processing by Matlab software. Here the leaf images of paddy crops are already trained by artificial neural network by k-means algorithm. At second stage, soil moisture sensor values are compared with threshold values which are given to microcontroller. Depending upon the results of microcontroller, motor is pumped ON/OFF which is driven by driver circuit and relay. If the plant is infected to diseases, the information about the diseases is sent to the authorised person through GSM module. This system helps to increases the crop yield by identifying and classifying the diseases before it affects the entire field. Development of this system leads to high productivity.