Nutrient Monitoring Research Articles

A paper-based analytical device for the on-site multiplexed monitoring of soil nutrients extracted with a cafetière

Sustainable agricultural production has the aim to maximise the yield and quality of harvests. For that, farmers need to monitor nutrient levels within soils to apply fertilizers accordingly and to prevent land degradation and soil exhaustion. Current methods involve time-consuming sampling and transport to a laboratory; and most farmers do not have the resources to measure frequently at multiple locations, limiting their sustainable production. In order to address this challenge, a paper-based analytical device (PAD) for the multiplexed detection of soil nutrients, i.e. phosphate, nitrate and pH, extracted with a cafetière-based method, is developed in this work. The compact and easy-to-use platform enables an accurate soil analysis in less than 20 min; 3 min for extraction and 15 min for readout. Two detection channels for nitrate and phosphate analysis, and one circular detection area for pH, including the selective reagents to each analyte, are defined within the PAD. Upon contact with the specific nutrients, the platform produces a colorimetric response that is easily readable by naked eye and smartphone camera. The detection reactions were optimized in the range 1 − 22.5 mg L−1, 10 −100 mg L−1 and 5.0 – 8.5 for phosphate, nitrate and pH, respectively, in agreement with the relevant levels in soils. The volume of water, the number of pushes of the plunger, the extraction time and the mass of soil were also optimized for the nutrient extraction method with the cafetière. The optimal workflow was validated with commercial soils and compared with the conventional UV-Vis method and the labels from the soil package, showing excellent agreement. This paper-based platform provides the possibility of a cheap, sensitive and specific monitoring of soil nutrients by minimally trained operators, such as farmers, enabling in-the-field analyses of multiple key soil nutrients in resource-limited settings and therefore addressing the challenge of routine monitoring for sustainable agriculture.

Use of Probes and Sensors in Agriculture—Current Trends and Future Prospects on Intelligent Monitoring of Soil Moisture and Nutrients

Soil monitoring is essential for promoting sustainability in agriculture, as it helps prevent degradation and optimize the use of natural resources. The introduction of innovative technologies, such as low-cost sensors and intelligent systems, enables the acquisition of real-time data on soil health, increasing productivity and product quality while reducing waste and environmental impact. This study examines various agricultural monitoring technologies, focusing on soil moisture sensors and nutrient detection, along with examples of IoT-based systems. The main characteristics of these technologies are analyzed, providing an overview of their effectiveness and the key differences among various tools for optimizing agricultural management. The aim of the review is to support an informed choice of the most appropriate sensors and technologies, thus contributing to the promotion of sustainable agricultural practices.

A skin-mountable flexible biosensor based on Cu-MOF/PEDOT composites for sweat ascorbic acid monitoring

Continuous monitoring of sweat nutrients offers valuable insights into metabolic cycling and health levels. However, existing methods often lack adaptability and real-time capabilities. Here, we propose a skin-mountable flexible biosensor integrated with metal-organic framework (MOF)-derived composites for real-time monitoring of sweat ascorbic acid (AA) levels. The biosensor features a miniaturized, highly integrated system capable of an imperceptible, stretchable skin patch with dimensions of 16.9 × 9.9 × 0.1 mm3, ensuring conformal integration with curvilinear skin contours. The introduction of a copper-based MOF anchored with poly(3,4-ethylenedioxythiophene) (Cu-MOF/PEDOT) significantly enhances sensing performance toward AA, achieving a detection limit of 0.76 μM and a sensitivity of 725.7 μA/(mM·cm2). Moreover, a miniaturized flexible circuit enables wireless communication, resulting in a lightweight, wearable platform weighing only 1.3 g. Structural and electrochemical analyses confirm the favorable sensitivity, reversibility, and stability of the biosensor, while in-vivo validation in human subjects further reveals the capability to track sweat AA variations during nutrient intake and sustained exercise, showcasing its potential in metabolic cycle assessment and health management. The biosensor presents a promising avenue for scalable health monitoring using adaptable and user-friendly technologies.

Climate change adaptation benefits from rejuvenated irrigation systems at Kiwere and Magozi schemes in Tanzania

ABSTRACT We examine whether soil moisture and nutrient monitoring tools and Agricultural Innovation Platforms improve farmers adaptive capacity to climate change in the context of two small-scale irrigation schemes in Tanzania. Analysis of household surveys and farmer field books show that these interventions have significantly increased household income and diversification and reduced water use and conflicts. This has contributed to rejuvenating the schemes and increased the willingness to collaborate. Farmers within these schemes report less COVID-19 impact than farmers within surrounding schemes. We argue that the interventions have increased farmers’, and their communities’, resilience and capacity to adapt to climate change.

IoT System for Floating Raft Hydroponics: Nutrient Monitoring and Automation

Abstract The utilization of hydroponic cultivation in agriculture is increasing due to its high productivity even in limited spaces. However, in practice, monitoring and controlling nutrients in hydroponic systems often require intensive human supervision and intervention. This issue can be addressed through technological advancements, particularly with the adoption of microcontrollers, sensors, and the Internet of Things (IoT). Previous research has developed IoT-based monitoring and control systems for DFT hydroponics using the Blynk platform. This study aims to develop an IoT-based nutrient monitoring and automation system for floating raft hydroponics. Floating raft hydroponics was chosen for its simplicity, lower installation costs, and space optimization. The system developed in this study uses the HC-SR04 ultrasonic sensor with an average error of 1.229%, the DS18B20 water temperature sensor with an error of 0.415%, and the TDS sensor with an error of 2.055%. The sensor and actuator measurement data are presented via the Antares Web. This research has the potential to minimize the necessity for manual intervention in hydroponic systems, thereby enhancing both efficiency and consistency in nutrient delivery.

Soil Nutrients Inversion in Open‐Pit Coal Mine Reclamation Area of Loess Plateau, China: A Study Based on ZhuHai‐1 Hyperspectral Remote Sensing

ABSTRACTSoil nutrients are crucial to assess land reclamation quality, and the use of various types of remote sensing data for soil nutrient inversion has been a key focus for soil monitoring. However, fewer studies have been conducted using satellite‐based hyperspectral remote sensing. To explore the potential of satellite‐based hyperspectral remote sensing in soil nutrient monitoring, this study selected soil organic matter, total nitrogen, available phosphorus, and available potassium content data from 83 sample sites using ZhuHai‐1 hyperspectral data. After spectral transformation and feature extraction, various inversion models were constructed, including partial least squares regression, support vector machine, recurrent neural network, and random forest. After verification by accuracy, the best spectral‐model combination was used for inversion. The results showed that the R‐squared range of the inversion models was 0.67748–0.78115. High content areas of soil organic matter and available potassium exhibited concentrated and contiguous features, while high content areas of total nitrogen and available phosphorus were more fragmented and fine‐grained. Alfalfa grassland plays a vital role in improving reconstructed soil in the early reclamation stage, and agricultural activities have differential impacts on soil nutrient accumulation. This study provides a theoretical basis for verifying the application capability of ZhuHai‐1 hyperspectral satellite data in soil monitoring.

Optimizing Soil Fertility through Machine Learning: Enhancing Agricultural Productivity and Sustainability

Nowadays, the sustainability of agriculture and food security have an increasing importance on soil fertility. Soil fertility is defined as the capacity of a land to grow crops and its potential crop productivity. However, factors such as increasing population, climate change, land use changes and environmental pollution threaten soil fertility. These threats can result in problems such as erosion, soil salinisation and organic matter depletion. Soil fertility is critical for the long-term health of agriculture and food security. Artificial intelligence techniques used to determine and manage soil fertility analyse the minerals present in the soil as well as other factors. These analyses assess the amount of minerals present in the soil, the availability of nutrients and important parameters such as pH. This information guides farmers in selecting the most appropriate crops. Furthermore, the integration of Internet of Things (IoT) technologies allows real-time monitoring of minerals and nutrients in the soil and optimising irrigation and fertilisation processes based on this data. These developments have the potential to improve soil fertility management and increase agricultural productivity.

Single-shot LIBS: A rapid method for in situ and precise nutritional evaluation of hydroponic lettuce

Hydroponic farming has emerged as a promising method that can enable year around crop production, particularly in regions with non-arable land. Ensuring precise control over nutrient levels and growing conditions is imperative for optimizing crop quality and nutritional value. However, the existing state-of-the-art nutrient assessment methods demand tedious sample preparation and often prove to be either destructive or offline, lacking in options for in situ monitoring. Previous approaches to nutritional evaluation using laser-induced breakdown spectroscopy (LIBS) utilized multiple laser shots or labor-intensive sample preparation to achieve enhanced sensitivity. In this context, we propose a single-shot LIBS system with a custom-made optical collection unit coupled to spectrograph to improve sensitivity and reduce sample damage by employing low excitation energy levels (~ 1.5 mJ). This study demonstrates in situ nutrient monitoring of hydroponically grown lettuce leaves and roots using single-shot LIBS analysis, paving the way for enhanced crop cultivation practices and improved agricultural productivity. Additionally, we discuss energy optimization strategies aimed at improving sensitivity and achieving a high signal-to-background ratio, which are essential for effective and safe nutrient monitoring and analysis in hydroponic farming systems. The results and analysis reveal that highly reproducible and sensitive LIBS spectra can be obtained directly from lettuce plants without any prior sample preparation.Graphical

Quantitative Analysis of Iron Activity in Agricultural Soils Using X-ray Fluorescence Spectrometry: Comparative Study with an Undisturbed Reference Site in Niankhene, Senegal

An essential micronutrient for plant growth and development, iron (Fe) affects a number of physiological processes, including respiration and photosynthesis. This study compare the amounts of iron in agricultural zones and reference zones, describe the geographic distribution of iron, and assess its accessibility to cultures. The aim is to assess the deficiencies and excess soil disturbance in the Niankhène agricultural site while maintaining a stable site. Soil samples were taken at 0–20 cm and 20–40 cm depths from 47 agricultural stations and three reference points using X-ray fluorescence (XRF) spectroscopy. Significant variation in Fe concentrations was found by the research, with an average of roughly 3310.95 ppm and moderate variation between samples. An analysis conducted in comparison with reference locations revealed a widespread lack of Fe in the research area, with 85.71% of samples displaying depletion and only 14.29% displaying accumulation of Fe. This discrepancy emphasizes the necessity of focused soil management techniques to raise Niankhene's agricultural output. The results highlight how crucial it is to use site-specific soil management strategies in order to overcome Fe deficits and raise crop yields. This research highlights the possibility for establishing targeted fertilization and corrective procedures to maximize Fe availability by utilizing XRF technology for reliable soil nutrient monitoring. The study's findings offer insightful information for enhancing fertilization techniques and improving soil management techniques, which will support sustainable farming methods and increased crop yield in the area.

A real-time on-site precision nutrient monitoring system for hydroponic cultivation utilizing LIBS

Indoor hydroponic farming is an advanced cultivation technique with diverse sustainability benefits, such as facilitating local produce, minimizing transportation costs and emissions, and enabling year-round crop cultivation. To optimize crop growth for enhanced yield, improved crop quality, and reduced environmental footprint, precise monitoring and replenishment of essential nutrients within hydroponic systems is crucial. Current methods employed in most commercial farms for online nutrient supply monitoring is limited to pH and conductivity measurements. These techniques can only offer an indication of the overall change in the complex nutrient mixture and lack the capability to precisely identify the specific nutrient or quantify the nutrient content. Most of the existing techniques for measuring individual nutrient levels are expensive and invasive, necessitating sample preparation, frequent recalibration, and skilled personnel for operation. In this context, we propose and demonstrate a real-time, on-site monitoring system for the precise analysis of hydroponic nutrient supply based on laser-induced breakdown spectroscopy (LIBS). We also discuss the system design considerations, parametric optimizations, limit of detection (LOD), and limit of quantitation (LOQ) of key nutrient components such as potassium (K), sodium (Na), calcium (Ca), and magnesium (Mg), using the proposed approach. The detection range of the developed LIBS-based monitoring system can encompass the typical concentration range observed in hydroponic nutrient solutions used at agricultural farms. This technique offers rapid online monitoring of individual nutrient components, providing precise, real-time analysis and the potential to enable comprehensive automation capabilities for current and future hydroponic farms.Graphical abstract

Drainage water management, woodchip bioreactor, and saturated riparian buffer as stacked conservation practices for improving crop yields and water quality

Stacking edge-of-field practices may improve nutrient removal from crops. To examine the effects of stacking edge-of-field conservation practices, a woodchip bioreactor (WBR) and saturated riparian buffer (SRB) were installed in series by intercepting tile flow from a field having a drainage water management system. Nutrient monitoring from 5 years evaluated nutrient export annually and based on the precipitation intensity. Drainage water was monitored for total suspended solids (TSS), nitrate-N, total-P, total-N, and ortho-P at the inlet and outlet of WBR and control structure of SRB. Nutrient export reductions of WBR and SRB were determined for precipitation events that were categorized as low <12.7 mm, mid 12.7–25.4 mm, high 25.4–50.8 mm, and very high >50.8 mm. Over the five seasons, nitrate-N export was reduced 88 % at the WBR outlet and 78 % at SRB outlet when used in a stacked series configuration. The efficacy of edge-of-field practices was affected by the intensity of precipitation events. The low and mid-intensity precipitation events generated 67 % of the total discharge from the subsurface drainage system which accounted for 74 % of the influent nitrate-N. During low and mid-intensity precipitation events, discharge was reduced by 58–65 %, nitrate-N was reduced by 49–88 % and total-P was reduced by 65–73 % by using stacked practice of WBR and SRB. During high and very high-intensity precipitation events only nitrate-N export was reduced by 61–66 %. This indicates that when designing stacked edge-of-the-field practices the cumulative effect of the practices and their performance during different precipitation events should be taken into account when managing conservation practice-based cropping systems.

Exploring the potential of multi-source satellite remote sensing in monitoring crop nutrient status: A multi-year case study of cranberries in Wisconsin, USA

A timing and precise diagnosis of crop nutrient status is essential for optimizing management practices that promote environmentally friendly and enhanced crop yields. Although plant tissue analysis has conventionally been employed to evaluate the nutritional status of crops, this method cannot capture the spatial variability of crop nutrients. In contrast, satellite-based remote sensing can monitor the nutrient status of crops across expansive areas. This study explored the capability of multi-source satellite images (PlanetScope-4: 3 m, 4 bands; PlanetScope-8: 3 m, 8 bands; Sentinel-2: 10–60 m, 13 bands; PRISMA: 30 m, 239 bands) in mapping 12 foliar nutrients in cranberries. Three machine learning approaches, including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), were used to relate foliar nutrients to different types of satellite-derived features (SR: surface reflectance; VI: vegetation indices; TF: texture features) or their combinations (SR+VI, VI+TF and SR+VI+TF). Model performance was compared across different foliar nutrients, modelling approaches and combinations of model input features using R2 (the coefficient of determination) and RRMSE (relative root mean square error, = root mean square error/nutrient range × 100 %). Input features that were important to foliar nutrient modelling were identified. The model performance difference among nutrients was consistent between Planet-4 and Sentinel-2, as well as between Planet-8 and PRISMA. In the Planet-4 and Sentinel-2 derived models, N was best predicted (average R2 = 0.77, average RRMSE=15 %), followed by macronutrients S (0.60–0.63, 11 %), Mg (0.58–0.65, 10–11 %), Ca (0.49–0.51, 9 %), Na (0.69, 22 %), P (0.49, 9 %) and K (0.20, 8 %), and then by all micronutrients(i.e., Fe, Mn, B, Cu and Zn: R2 = 0.04–0.61; RRMSE=16–28 %). In the Planet-8 and PRISMA derived models, macronutrients (i.e., N, P, K, Mg, Ca, S and Na) had lower R2 and RRMSE (R2 = 0.06–0.59; RRMSE=7–57 %) than micronutrients (i.e., Fe, Mn, B, Cu and Zn: R2 = 0.18–0.60; RRMSE=19–66 %). The successful retrieval of foliar nutrients from satellite imagery was influenced by many factors, including the intercorrelation between nutrients and model input features, the data availability at critical growth stages, and satellite images characteristics (e.g., spatial and spectral resolutions). Except for foliar nitrogen, foliar nutrients typically do not exhibit distinct absorption features associated with C, H, N, or O molecular bonds in the 400–2500 nm range. Our results indicate that their successful retrieval can be primarily attributed to the association between foliar nutrients and other leaf components (e.g., pigments, water, and dry matter) that do display spectral features within this range. Our study demonstrated the potential of integrating multi-source satellite data for precise nutrient monitoring over large scales.

A handheld rapid detector of soil total nitrogen based on phase-locked amplification technology

Soil total nitrogen (STN) serves as a critical indicator for agricultural productivity. Rapid STN detection is crucial for nutrient monitoring in farmland and for guiding precision fertilization. Traditional near-infrared spectroscopy-based STN detectors necessitate darkrooms or subterranean settings to negate ambient light interference, restricting their practical use. This study develops a handheld rapid STN detector employing phase-locked amplification technology to address this issue. The device comprises optical, circuit, and control units. The optical unit features a ring-shaped split fiber structure to mitigate soil surface irregularities’ effects on the signal, with a central InGaAs photodiode for signal reception. Eight wavelengths (970 nm, 1050 nm, 1085 nm, 1200 nm, 1300 nm, 1450 nm, 1550 nm, and 1600 nm) are selected for the light sources through an information theory optimized genetic algorithm. The circuit unit employs direct digital synthesis and operational amplifiers for light signal generation and modulation. Reflected soil light signals are demodulated using an InGaAs photodiode and a phase-locked amplifier, effectively filtering ambient light noise. The control unit integrates an STM32F103C8T6 microcontroller and a Raspberry Pi 4B. The STM32 microcontroller manages signal generation, channel selection, frequency control, A/D conversion, and display, while the Raspberry Pi handles data processing, uploading, and transmission. A BP neural network model optimized by genetic algorithms is embedded for real-time STN data acquisition. Cloud-based data storage and visualization are facilitated through the Alibaba Cloud IoT platform. Performance testing reveals the device’s stability and interference resistance, with a standard deviation of electrical signal under varying light conditions below 3 mV. Compared to commercial spectrometers, the correlation coefficient (r) for reflectance data at eight wavebands exceeds 0.9. Accuracy testing shows an R2 above 0.8 and an RMSE of 0.30 g/kg when comparing detector-obtained STN values with standard chemical methods. These results demonstrate the detector’s efficacy in ambient light isolation, stability, and accuracy in agricultural settings, offering technical support for variable fertilization in precision farming.

Advanced nutrient monitoring: Cost-effective cerium ion detection with smart device integration

This research is critically important for large-scale poultry farming, where vigilant monitoring is necessary to assess nutrient levels in poultry feed. Detecting cerium in poultry feed through colorimetric techniques is crucial for evaluating nutrient intake, feed quality, animal health, and broader environmental impacts. This study significantly contributes to ongoing research and monitoring efforts, aiding in maintaining an optimal mineral balance and enhancing chicken quality and overall poultry performance. The study utilized Alizarin Red dye (AR), both individually and in combination with Eriochrome Black T (EBT), to identify the presence of cerium ions. Various experimental conditions such as pH, concentration, temperature, and specificity were thoroughly examined. The UV-Vis absorption spectra indicated that the average minimum detectable limit for cerium ions is approximately 5 ppm, with a detection range of 0.2–3 mM. Additionally, a cost-effective paper-based sensor and a portable colorimetric method for detecting cerium ions were innovatively designed. This paper-based sensor ensures precise detection at room temperature, demonstrating high sensitivity and selectivity. The detection system was integrated with smart devices, enabling the swift capture of Red, Green, and Blue (RGB) values for practical real-time applications. This integration allows for rapid on-site identification of cerium ions. The introduction of this affordable, accurate, and portable colorimetric method for monitoring cerium ions represents a promising advancement in developing accessible tools within this field.

Biomass and nutrient flow dynamics and sustainability practices to de-risk environmental challenges in the sub-saharan Africa farming system

In sub-Saharan Africa, productivity risks stem from weather variability, while environmental risks include soil nutrient depletion due to unsustainable farming practices that include monoculture, inadequate or lack of soil and water conservation measures, and low-nutrient application. As a result, shifts from the prevailing fallow system to permanent cultivation lead to soil degradation. The present study aimed to quantify the fluxes of biomass, nutrients, and nutrient balances from different fertilizer sources to de-risk the challenges related to agriculture and the environment in Mali. A farm household survey was conducted over two years (July 2018 to June 2020) with 45 households. The survey enabled us to categorize farm households into three typologies: high resource endowment (HRE), medium resource endowment (MRE), and low resource endowment (LRE). Data on sustainability indicators from cropland, livestock, farm input use, and redistribution units enabled the analysis of biomass and nutrient flow dynamics from households to farmlands and vice versa. The nutrient monitoring (NUTMON) tool generated nutrient flows and balances. Results showed that the total annual biomass collected per hectare by HRE (22.3t) is significantly higher than that collected by MRE (13.4t) and LRE (5.35t) farms (P ​< ​0.001). Compared to LRE (10.3 ​t ​ha−1 year−1), HRE and MRE farmers produced six times (60 ​t ​ha−1 year−1) and three times (34 ​t ​ha−1 year−1) more manure, respectively. Farm households with better endowment status observed a higher rate of nutrient utilization. For the major crops, nutrient application rates of HRE farms in kg ha−1 (cotton: 12.6 ​N, 4.2 ​P, 18.2 ​K) and (maize: 9.18 ​N, 2.34 ​P, 10.7 ​K) were significantly higher than that of MRE and LRE farms (P ​< ​0.01). The study confirms that household endowment status determines farmlands' nutrient flows and fertility levels. Quantifying biomass transport and understanding nutrient flow dynamics enable the derivation of context-specific solutions to reduce risks associated with productivity and the environment.

IoT Based Soil pH Detection and Crop Recommendation System

Agricultural productivity hinges on soil fertility, influenced by key factors like nitrogen, phosphorus, potassium, pH level, and soil moisture. Yet, achieving optimal crop growth is challenging due to limited farmer knowledge and difficulties in determining precise fertilizer quantities. Conventional soil analysis methods involve manual sampling andcostly lab tests, which are subjective. To address this, aproposed solution integrates IoT-enabled soil nutrient monitoring with machine learning algorithms for croprecommendations. Sensors collect data on crucial parameters like nitrogen, phosphorus, and soil temperature, transmitting it to a cloud-based database. Machine learning analyzes this data to suggest ideal crops, minimizing fertilizer use, reducing labor, and enhancing overall productivity. This innovative approach streamlines crop selection, minimizing unnecessary inputs while maximizing yields. By harnessing IoT and machine learning, farmers gain valuable insights into soil health, enabling precise fertilization and crop selection. This not only boosts agricultural productivity but also contributes to economic growth by fostering sustainable practices andincreased yields.

Wearable sensors deriving from cationic-induced 2D-2D co-assembled films for nutrient monitoring

Real-time nutrient monitoring plays a crucial role in identifying healthcare abnormalities and necessitates the development of advanced detection systems. However, the current monitoring systems encounter limitations attributed to the rigidity of materials, impeding the detection process. To address this challenge, wearable sensors have emerged as a promising solution. In this study, a novel technique utilizing cation-induced oil-water interface co-assembly is presented for the fabrication of two-dimensional (2D) MXene-2D Reduced Graphene Oxide (RGO) films (MX-RGO). Through this technology, the as-fabricated films are successfully transferred onto various flexible and wearable substrates in a single step, showcasing the versatility of the film on different flexible substrates. By deriving our wearable sensor from the 2D-2D co-assembly film, a critical gap in the field of nutritional monitoring technologies is addressed. The MX-RGO film demonstrates exceptional sensing properties, harnessing the ultra-high conductivity of MXene and the stability of RGO. This technological breakthrough enables the selective and accurate tracking of ascorbic acid (AA), a vital nutrient. The findings not only establish a new approach for the co-assembly of 2D-2D film-based materials as an active layer for wearable electronic devices but also hold promise for the future development of high-performance electronic products through the implementation of film-based sensors in medical care.

Design, fabrication and evaluation of a robot for plant nutrient monitoring in greenhouse (case study: Iron nutrient in spinach)

The regulation of nutrients intake in plant life is a critical factor in preserving the quality of crops, enhancing yield rates, and minimizing the fertilizer consumption. Consequently, this research presents the design of an intelligent robot for real-time monitoring of plant nutrients within greenhouse environments. The robot’s ability to detect and address iron deficiency in spinach plants was evaluated. To achieve this, the robot, equipped with a camera, captured visible imaging from surface of spinach plants including both the control group and the group subjected to iron deficiency stress. Statistical analysis of the data was performed at the probability level of 5 %, and genetic algorithm (GA) was utilized to select the most suitable features (G, Area, Energy, Entropy, R and L), from 41 extracted image parameters, including color, morphological, and texture features. An artificial neural network (ANN) was then employed to detect iron deficiency in plants, resulting in an accuracy of 96 %. Subsequently, the Gaussian kernel function of support vector regression (SVR) algorithm was used to predict spinach iron content, yielding the highest determination coefficient (R2 = 0.94) and the lowest values of mean absolute percentage error (MAPE = 5.92), root mean square error (RMSE = 0.43) and standard regression error (SRE = 2.66). The designed robot detected iron-deficient plants with precision, sensitivity, specificity and accuracy of 86 %, 82 %, 84 % and 83 %, respectively. The robot then took the necessary control actions to supply the deficiency of this nutrient by foliar spraying of more than 89 % of the crop area.

Monitoring of soil EC for the prediction of soil nutrient regime under different soil water and organic matter contents

Smart farms and precision agriculture require automatic monitoring and supply of water and nutrients for crops, but sensors to monitor plant available nutrients in soil are not available. Soil electrical conductivity (EC) is related to nutrients in soil solution, which can be affected by soil organic matter, soil texture, temperature, and water content. Therefore, the objective of this study is to evaluate factors influencing soil EC sensor values by monitoring EC under different soil organic matter and water contents. Ten soil samples with various sand and clay contents, EC, pH, and organic matter contents were selected and saturated with water. Volumetric water content and EC of the soil were monitored while drying the soil. Humic acid and manure were added to soils in order to evaluate the effect of organic matter on soil EC. Soil EC values linearly increased with increasing water content at 10–25% which is favorable water content for plant growth. The EC increased when organic matter was added to soils, which was related to ions released from the organic matter. Soil EC calibration factor for soil water content increased when EC of the soil was high and organic matter was added. The sensor EC values in sandy loam and loam soils was related to the ion contents in pore water, and exchangeable ions in soil, respectively. Sensor EC values were highly correlated with organic matter and K contents in soil and can be used as an indicator for plant available nutrients in soil. Therefore, the sensor EC at optimal soil water content for plant growth can be used to monitor changes in plant available nutrients in soil.

Enhancing Soil Nutritional Status in Smart Farming: The Role of IoT‐Based Management for Meeting Plant Requirements

Soil nutrition assessment is a critical factor in improving both the quantity and quality of agricultural yields. The integration of Internet of Things (IoT) technology represents a groundbreaking advancement, allowing for real‐time measurements in smart farming. This article focuses on investigating and evaluating the current challenges in nutrient management within smart farming and explores the future potential of utilizing IoT for nutrient management. To gather data, bibliometric analysis and utilization of the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) flowchart were conducted, searching on the Publish or Perish and Science direct search engines using the keywords “soil nutrient monitoring smart farming internet of things” without quotation marks. A total of 395 articles were initially identified, 20 of which were included in the analysis after verification with Covidence assessment tools. Our review of existing studies revealed that many nutritional management approaches are still in prototype and experimental design, without the use of experimental design agriculture standards, without validation through plant testing, and mainly focus on monitoring systems. This highlights the need for additional research into large‐scale studies, considering data security and the application of this technology in agricultural experimental design across a wide range of agricultural practices. Furthermore, future research on NPK IoT‐based sensors is expected to provide numerous benefits and accessibility to all farmers, ensuring agricultural profitability.