Smartphone-Based Spectrometric Analyzer for Accurate Estimation of pH Value in Soil

Das, Priyanka; Paul, Sarmistha; Bhattacharya, Suvendu; Nath, Pabitra

doi:10.1109/jsen.2020.3027587

Cited by 13 publications

(9 citation statements)

References 35 publications

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“…After grinding, sieving, and acidification, the content of SOC, TN and TP was determined using the wet oxidation methods (Xu et al 2018). Soil pH value was analyzed using a pH meter and conducting spectrometric analysis with a universal indicator dye (Das et al 2021).…”

Section: Soil Sampling and Laboratory Analysismentioning

confidence: 99%

Mapping soil properties in tropical rainforest area using UAV-based hyperspectral images and LiDAR points

Chen,

Shi,

et al. 2024

Preprint

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Aims This study aimed to integrate UAV-based hyperspectral images and LiDAR points to indirectly estimate the soil nutrient properties in tropical rainforest areas. Methods A total of 175 features, including vegetation indices, texture characteristics, and forest parameters, were extracted from the study area. Five machine learning models, Partial Least Squares Regression (PLSR), Random Forest (RF), AdaBoost, Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost), were constructed to predict soil nutrients. Furthermore, Bayesian optimization algorithm (BOA) was introduced to obtain more optimal model hyperparameters. Results The results showed that BOA can better explain the complex interactions between features and hyperparameters, leading to an average improvement of model performance by 89.38% compared to default parameter models. The GBDT model optimized by BOA outperformed other models in predicting soil pH and TN, with improvements of 512.50% and 36.36%, respectively. The XGBoost model with optimized parameters performed well in predicting SOC and TP (with gains of 206.67% and 95% improvements, respectively). In addition, point cloud features derived from LiDAR data outperformed vegetation indices in predicting soil nutrient properties, enhancing inversion accuracy by effectively characterizing vegetation growth conditions and terrain changes. Conclusions This study indicated that combining the advantages of UAV-based hyperspectral images and LiDAR points will advance the methodology for digitally mapping soil nutrient properties in forested areas, achieving large-scale soil nutrient management and monitoring.

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Section: Soil Sampling and Laboratory Analysismentioning

confidence: 99%

Mapping soil properties in tropical rainforest area using UAV-based hyperspectral images and LiDAR points

Chen,

Shi,

et al. 2024

Preprint

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“…The procedure allows to determine of the pH precisely in the solution after contact with the soil in the range 4–9. As part of the solution, an add-on for spectrophotometric measurements under constant conditions (without the influence of external light) was printed in 3D printing technology [ 30 ].…”

Section: Assessment Of Basic Chemical and Physical Soil Propertiesmentioning

confidence: 99%

“…The image acquisition system was used to capture triplicate images from 90 mineral soil samples, representing a wide textural variability from sand to clay The rapid and noninvasive prediction of soil sand, silt, and clay [ 52 ] pH The sensing system consists of a 3D printed compact optical set-up which can be attached to the smartphone’s rear camera. The procedure involves measuring the change of transmission intensity, which is correlated with the change in the sample’s pH value A cost-effective, compact, and handheld smartphone-based sensing tool for accurate estimation of pH values of agricultural farmlands [ 30 ] Water content Rapid and reasonable estimation of water content from a set of cellphone images A proximal soil sensor to be used further for easy, rapid, and cost-effective analysis [ 31 ] Soil nutrients content Phosphorus (in phosphate form) Standard ascorbic acid protocol application where heteropoly acid-phosphomolybdic acid reagent changes colour upon treatment with ascorbic acid, which can be detected by the designed sensing system With proposed sensing system, a common citizen can easily measure the phosphate concentration without the requirement of any laboratory equipment [ 38 ] Soil organic matter Soil organic matter content prediction based on smart phone photos and RGB colour distribution measurement Good correlation between the colour parameters and soil organic matter was found [ 35 ] Soil organic matter Smartphone-based soil image segmentation technique and subsequent machine learning optimization methodology with a set of soil images for rapidly predicting soil organic matter Soil organic matter prediction with significant time and cost savings [ 33 ] pH, N-nitrate, available phosphorus (P), and exchangeable potassium (K) A smartphone camera was used for the reading of the quick test colour Easy performed quick test for soil nutrients content determination [ 53 ] …”

Section: Soil Contamination Monitoringmentioning

confidence: 99%

Analytical applications of smartphones for agricultural soil analysis

Vakh

2023

Anal Bioanal Chem

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Soil is one of the most important farming resources. Appropriate managing of its quality promotes productive and sustainable agriculture. The valuable farm practice in soil quality managing is based on regular soil analysis with the aim of determining the exact amount of nutrients or other chemical, physical, and biological soil properties. Soil analysis usually requires sample collection at the desired sampling depth followed by sample delivery to chemical laboratories. However, laboratory analyses are resource-intensive and costly, and require a lot of time, effort, and equipment. A low-cost, fast, and effective alternative for soil quality control is the application of smartphones to perform chemical analyses directly in the field or on the farm. In this paper, an overview of recent developments on smartphone-based methodologies for agricultural purposes and portable evaluation of soil quality and its properties is presented. The discussion focuses on recent applications of smartphone-based devices for the determination of basic soil parameters, content of organic matter, mineral fertilizers, and organic or inorganic pollutants. Obvious advantages of using smartphones, such as convenience and simplicity of use, and the main shortcomings, such as relatively poor precision of the results obtained, are also discussed. The general trend shows the huge interest from researchers to move the technology into the field with the aim of providing cost-effective and rapid soil analysis. This paper can broaden the understanding of using smartphones for chemical analysis of soil samples, as it is a relatively new area and is expected to be developed rapidly. Graphical Abstract

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“…These parameters can be separated to environmental parameters, which include air temperature, air humidity, intensity of visible light, and barometric pressure, and soil parameters, which include soil moisture, soil temperature, soil pH, electrical conductivity of soil, and soil nutrient level. As pH is a crucial parameter for various chemical and biological processes, obtaining accurate real-time pH data is therefore important for precise monitoring [ 5 ]. In addition, soil parameters are indicative of the health and fertility of agricultural land.…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Agro-Toolbox for Soil Analysis and Environmental Monitoring

Pechlivani,

Papadimitriou,

Pemas

et al. 2023

Micromachines

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The agricultural sector faces numerous challenges in ensuring optimal soil health and environmental conditions for sustainable crop production. Traditional soil analysis methods are often time-consuming and labor-intensive, and provide limited real-time data, making it challenging for farmers to make informed decisions. In recent years, Internet of Things (IoT) technology has emerged as a promising solution to address these challenges by enabling efficient and automated soil analysis and environmental monitoring. This paper presents a 3D-printed IoT-based Agro-toolbox, designed for comprehensive soil analysis and environmental monitoring in the agricultural domain. The toolbox integrates various sensors for both soil and environmental measurements. By deploying this tool across fields, farmers can continuously monitor key soil parameters, including pH levels, moisture content, and temperature. Additionally, environmental factors such as ambient temperature, humidity, intensity of visible light, and barometric pressure can be monitored to assess the overall health of agricultural ecosystems. To evaluate the effectiveness of the Agro-toolbox, a case study was conducted in an aquaponics floating system with rocket, and benchmarking was performed using commercial tools that integrate sensors for soil temperature, moisture, and pH levels, as well as for air temperature, humidity, and intensity of visible light. The results showed that the Agro-toolbox had an acceptable error percentage, and it can be useful for agricultural applications.

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Smartphone-Based Spectrometric Analyzer for Accurate Estimation of pH Value in Soil

Cited by 13 publications

References 35 publications

Mapping soil properties in tropical rainforest area using UAV-based hyperspectral images and LiDAR points

Mapping soil properties in tropical rainforest area using UAV-based hyperspectral images and LiDAR points

Analytical applications of smartphones for agricultural soil analysis

IoT-Based Agro-Toolbox for Soil Analysis and Environmental Monitoring

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