Precision Fertilization Research Articles

A study on hyperspectral soil total nitrogen inversion using a hybrid deep learning model CBiResNet-BiLSTM

Rapid, accurate and non-destructive acquisition of soil total nitrogen (TN) content in the black soil zone is significant for achieving precise fertilization. In this study, the soil types of corn and soybean fields in Jilin Agricultural University, China, were selected as the study area. A total of 162 soil samples were collected using a five-point mixed sampling method. Then, spectral data were obtained and the noisy edge were initially eliminated. Subsequently, the denoised spectral data underwent smoothing by using the Savitzky–Golay (SG) method. After performing the first-order difference (FD) and second-order difference (SD) transformations on the data, it was input to the model. In this study, a hybrid deep learning model, CBiResNet-BiLSTM, was designed for precise prediction of soil TN content. This model was optimized based on ResNet34, and its capabilities were enhanced by incorporating CBAM in the residual module to facilitate additional eigenvalue extraction. Also, Bidirectional Long Short-Term Memory (BiLSTM) was integrated to enhance model accuracy. Besides, partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and back propagation neural network (BP), as well as ResNet(18, 34, 50, 101, 152) models were taken for comparative experiments. The results indicated that the traditional machine learning model PLSR achieved good performance, with R2 of 0.883, and the hybrid deep learning model CBiResNet-BiLSTM had the best inversion capability with R2 of 0.937, with the R2 being improved by 5.4%, compared with the PLSR model. On this basis, we present the LUCAS dataset to demonstrate the generalisability of the model. Therefore, the CBiResNet-BiLSTM model is a fast and feasible hyperspectral estimation method for soil TN content.Graphical abstract

Comprehensive analysis of NGS-based expanded carrier screening and follow-up in southern and southwestern China: results from 3024 Chinese individuals.

This study aimed to screen southern and southwestern Chinese individuals using expanded carrier screening (ECS), which explores the carrier status of recessively inherited diseases in southern and southwestern China, evaluates the clinical effectiveness of ECS application, and helps recognize high-risk fetuses that may have genetic disorders early in pregnancy, to provide better reproductive guidance. ECS for 220 diseases based on next-generation sequencing was performed on 3024 southern and southwestern Chinese individuals (1512 couples). Carrier status was analyzed; genes and loci with high frequencies of variants and on high-risk couples (ARCs) were focused to evaluate the clinical utility of our ECS technology and provide them precise fertility guidance. In total, Pathogenic/likely pathogenic(P/LP) variants were found in 1885 individuals, so the carrier frequency was 62.3%, and 23.2% of the individuals were carriers of multiple diseases. furthermore, 2837 variants were detected, and the average number of P/LP variants carried per subject was 0.938. Additionally, 128 ARCs carried P/LP variants of the same gene, and the theoretical incidence rate in their offspring was as high as 2.12%. This study validated the application of our ECS technique for carrier screening in southern China, identifying carrier status and providing accurate carrier frequencies for hundreds of genetic diseases.

Corrigendum to “Selenium enhancement strategy under precise fertilization in foxtail millet rhizosphere”

Corrigendum to “Selenium enhancement strategy under precise fertilization in foxtail millet rhizosphere”

Tree detection and in-row localization for autonomous precision orchard management

This work presents a framework for localizing ground robots within fruit tree orchards. The standard practice of managing orchards at the large block-level does not maximize the potential of farms — individual plants have different needs due to variations in soil, pests, disease, irrigation, etc. In order to make selective management decisions for individual trees, such as precision fertilization, a robot must be able to accurately localize itself within the row. This is a challenge since in high density, modern orchard systems it is often difficult to obtain accurate GNSS measurements. Our algorithm begins by using deep learning to segment a tree trunk in an RGB-D image and then estimate its width. We then use the trunk segmentations and widths to calculate particle weights in a particle filter-based localization system. We show that integrating trunk width into the particle update step led to a 45% decrease in the distance traveled before convergence, and a 31% decrease in convergence time, alongside a marginal increase in the rate of correct convergence. We also demonstrate autonomous tree-level localization with a large ground robot in realistic field experiments in a commercial apple orchard.

Design of precise fertilization method for greenhouse vegetables based on improved backpropagation neural network

Design of precise fertilization method for greenhouse vegetables based on improved backpropagation neural network

Design Optimization and Experimentation of Triple-Head Gradually Reducing Spiral Precision Fertilizer Apparatus

In order to solve the problems of existing spiral fertilizer apparatuses, such as the variation in cavity filling rate with rotational speed, fluctuation of fertilizer discharge flow, and inability to discharge fertilizer precisely, a triple-head gradually reducing spiral fertilizer apparatus is designed, which gradually compresses fertilizer particles through the triple-head reducing fertilizer spiral structure to achieve complete cavity filling and uniform fertilizer discharge. The main factors that affect the particle motion state and the structural size of the spiral fertilizer through theoretical analysis are determined, and its theoretical fertilizer discharge amount and rotational speed are calculated. Using EDEM (Discrete Element Method Software 2022) to establish a simulation model of a single-head gradually reducing fertilizer apparatus, the spiral lead reduction percentage x1, spiral diameter reduction percentage x2, and rotational speed x3 are determined as experimental factors, and the filling rate μ and spiral torque Yaverage are used as experimental indicators to conduct a simulation study on the secondary universal rotation combination design experiment. The results show that when the rotational speed is 95 r/min, the spiral lead reduction percentage is 60.00~73.21%, the spiral diameter reduction percentage is 86.55~97.05%, the filling rate μ is greater than 95%, and the spiral torque Yaverage is less than 16 N·m. In order to further improve the uniformity of fertilizer discharge and ensure the controllable accuracy of fertilizer discharge, comparative verification experiments are conducted on single-, double-, and triple-head gradually reducing spiral fertilizer discharge devices and ordinary spiral fertilizer discharge devices. The results show that the precision of the gradually reducing spiral fertilizer apparatus is better than that of the ordinary spiral fertilizer apparatus. Moreover, it is determined that the three-head style performed best. The triple-head gradually reducing spiral fertilizer apparatus is also validated by randomly adjusting six rotational speeds. The experiment results show that the average deviation of the fertilizer discharge flow rate of the fertilizer apparatus from the preset value is 3.16%. The two have a minor deviation, and the fertilizer precision is high. Precise control of the amount of fertilizer discharged can be achieved by adjusting the rotational speed, and the research can provide a specific reference for the improved design and precise control of the spiral fertilizer apparatus.

Integrated farming optimization ensures high-yield crop production with decreased nitrogen leaching and improved soil fertility: The findings from a 12-year experimental study

Context or problemSustainable farming practices, including precision fertilization, water-saving irrigation and recycling of organic materials, have been implemented worldwide in recent decades to achieve high crop yields and minimize nonpoint source pollution. However, the comprehensive impacts of these agricultural practices have seldom been systematically evaluated in field production. As agricultural intensification started in the 1980s, most previous studies focused on a single practice in the context of low land productivity. Objective or research questionThe objective of this research is to investigate and evaluate how holistic farming practices affect both crop production and environmental quality. MethodsWe reported findings from a 12-year experiment (2008–2020) in the highly intensive North China Plain (NCP) farming region, and conventional and optimized farming measures were compared. Three field treatments with annual double cropping (winter wheat (Triticum aestivum L.) and summer maize (Zea mays L.)) were chosen in the study, i.e., control without nitrogen (N) application (CK), farmers’ conventional practices (CON), and optimized practices (OPT), were chosen for this study. ResultsCompared with the CON treatment, OPT reduced N fertilizer input by 41.4 % and irrigation water by 27.1 % but produced similar grain yields; OPT increased the N recovery efficiency (REN) and N utilization efficiency (NUT2E) by 90.4 % and 53.0 %, respectively; these values were much greater than the increases in REN (+56.1 %) and NUT2E (+25.5 %) when soil N change was not considered. Similarly, compared with those in the CON treatment, the soil N stock (0–60 cm) in the OPT treatment increased by 8.4 %, and the N loss via leaching, ammonia volatilization and N2O + NO + N2 decreased by 47.1 %, 11.4 % and 28.6 %, respectively. ConclusionsOur study revealed that the integration of optimized practices of organic material recycling, precision fertilization and water-saving irrigation substantially reduced N losses, mainly through decreased N leaching, but maintained fertilizer N in the root-zone soil layer, which is important for a sustainable and high-yield crop production. Implications or significancesThe dissemination of these optimized practices to other regions in China and beyond will be highly important for achieving the dual goals of food security and environmental protection.

Spatial Heterogeneity and Key Influencing Factors of Soil Organic Carbon, Soil Total Nitrogen, and Carbon to Nitrogen Ratio in main plantations of Torreya grandis cv. Merrillii

Soil organic carbon （SOC） and soil total nitrogen （STN） serve as important indicators of the elemental balance within forest ecosystems reflecting soil fertility and quality. Accurate knowledge regarding the spatial variability of regional SOC, STN, and C∶N ratio and their influencing factors is of great significance for precise fertilization and soil health. In this study, a total of 117 topsoil samples （0-20 cm in depth） based on a 1 km×1 km grid were collected in the Torreya grandis cv. Merrillii plantation in Zhejiang Province. A combination of multi-dimensional statistical approaches （random forest model, structural equation model, redundancy analysis, and variation partitioning analysis） and diverse spatial analytical techniques （geostatistics, Moran's I index, etc.） were applied to reveal the spatial distributions and influencing factors of SOC, STN, and C∶N ratio in the Torreya. grandis cv. Merrillii region. The results showed that the average ω（SOC）, ω（STN）, and C∶N ratio were 17.63 g·kg-1, 1.48 g·kg-1, and 12.65, respectively, and their coefficients of variation were 68.08%, 67.41%, and 46.03%, respectively, indicating a moderate degree of variability. In general, the SOC, STN, and C∶N ratio of the Torreya grandis cv. Merrillii plantations were at an intermediate level in the national plantation. The semi-variance results showed that the nugget/sill values of SOC, STN, and C∶N ratio were 49.98%, 45.88%, and 49.93%, respectively, demonstrating a moderate level of spatial autocorrelation. The spatial distribution results showed that SOC, STN, and C∶N ratio decreased from northeast to southwest, with the majority of the region exhibiting above-medium fertility levels of SOC. The results of correlation analysis and redundancy analysis indicated that AN, AP, and AK were significantly correlated with both SOC, STN, and C∶N ratio （P<0.05）. The results of random forest, structural equation model, and variation partitioning analysis evidenced that the main influencing factors of SOC and STN were soil-available nutrients （AN, AP, and AK）. Therefore, our results could provide important insights for enhancing soil carbon and nitrogen pools in special plantations in Zhejiang Province, enhancing the capacity of plantations to adapt to regional climate change through ecological measures such as appropriate fertilization practices and strategic understory vegetation cultivation.

Leaf Nitrogen and Phosphorus Variation and Estimation of Citrus Tree under Two Labor-Saving Cultivation Modes Using Hyperspectral Data

Understanding canopy nitrogen (N) and phosphorus (P) differences is crucial for optimizing plant nutrient distribution and management. This study evaluated leaf N and P content in citrus trees across three cultivation modes: traditional mode (TM), wide-row and narrow-plant mode (WRNPM), and fenced mode (FM). We used hyperspectral data for non-destructive quantification and compared 1080 leaf samples from upper, middle, and lower canopy layers. Four models—Random Forest (RF), Backpropagation Neural Network (BPNN), Partial Least Squares (PLS), and Support Vector Machine (SVM)—were employed for leaf N and P estimation. Results showed that the TM had significantly lower N content compared to the WRNPM and FM, while the WRNPM exhibited higher P content. The canopy layer had minimal impact on N and P in the FM, and leaves in the upper layer had higher nutrient content in the WRNPM and TM. RF provided the best estimation accuracy, with R2 values of 0.66 for N and 0.72 for P. The cultivation mode and canopy layer significantly influenced the estimation accuracy, with the TM yielding the highest R2, followed by the WRNPM and FM obtaining the lowest accuracy. The labor-saving cultivation mode had different nutrient utilization efficiency compared to the TM. The cultivation mode and canopy layer should be considered when hyperspectral data were used for estimating the leaf N and P content. The study can offer new insights for precise fertilization strategies in fruit trees.

Enhancing Agricultural Sustainability through Precision Fertilizer Recommendations: A Machine Learning Approach with Comparative Ensemble Analysis

Enhancing Agricultural Sustainability through Precision Fertilizer Recommendations: A Machine Learning Approach with Comparative Ensemble Analysis

Principles and Significance of Nitrogen Management for Blackberry Production

Blackberry cultivation presents significant opportunities for fruit growers in subtropical regions, where nitrogen (N) is identified as a crucial macronutrient for optimal production. Given the variability in climate and soil conditions, determining the ideal N fertilizer amount can be complex. Effective blackberry cultivation requires careful attention to the principles of nutrient stewardship, including the selection of appropriate N sources, application rates, timing, and placement. Recommended N rates generally range from 25–45 kg/ha in the first year and 45–70 kg/ha in subsequent years, with adjustments based on plant type and regional conditions. The choice of fertilizer, particularly NH4+, is beneficial for blackberry plants, which thrive in acidic soils and show improved biomass and chlorophyll levels with this form of N. Research on N-cycling reveals its importance in supporting new plant growth, such as primocane development. However, improper N management, either excessive or insufficient, can negatively impact flower bud production and, consequently, fruit setting and yield. By using databases such as Google Scholar, Scopus, and Web of Science, this review synthesizes existing research on the role of N in blackberry cultivation, emphasizing the importance of precise fertilization practices tailored to regional climate and soil conditions. By highlighting variations in recommended N amounts and underscoring the principles of nutrient stewardship, this review aims to guide growers in achieving sustainable and high-quality blackberry production.

Assessing LDPE microplastics' impact on green gram (Vigna radiata L. Wilczek) cultivation: A greenhouse pot experiment

Plastic pollution, particularly from microplastics (MPs), poses a significant threat to agricultural ecosystems. This study investigates the impact of Low-Density Polyethylene (LDPE) microplastics on green gram (Vigna radiata) cultivation, focusing on growth, yield, and soil nutrient dynamics. A completely randomized design with nine treatments of varying LDPE concentrations was used in a controlled poly house environment. Soil analysis assessed mechanical and physico-chemical properties using established methods. Green gram variety Co (Gg) 8 was cultivated during the Rabi season, with comprehensive crop management including germination tests, pot culture preparations, precise fertilizer application, and essential cultural operations. Results indicated significant correlations between LDPE concentrations and green gram growth parameters, with higher concentrations having detrimental effects (correlation coefficient = −0.75, p < 0.05). LDPE treatments also influenced plant nutrient uptake, showing inverse correlations with nitrogen (N), phosphorus (P), and potassium (K) uptake (correlation coefficient = −0.82, p < 0.01). Post-harvest soil analysis revealed significant variations in available nutrients, with LDPE-treated soils exhibiting higher N, P, and K levels compared to control groups (correlation coefficient = 0.78, p < 0.01). Infrared spectroscopy confirmed the presence of LDPE microplastics in soil and plant samples, identified by characteristic (CO) and (C–H) stretching vibrations. These findings highlight the complex interactions between LDPE microplastics and agricultural systems, emphasizing the need for sustainable practices to mitigate the adverse effects of plastic pollution on agriculture. Statistical analysis using the CRD framework and correlation and regression analyses ensured robust interpretation of the data.

A Multi-Area Task Path-Planning Algorithm for Agricultural Drones Based on Improved Double Deep Q-Learning Net

With the global population growth and increasing food demand, the development of precision agriculture has become particularly critical. In precision agriculture, accurately identifying areas of nitrogen stress in crops and planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks and overlooks the multi-area tasks CPP. To address this problem, this study proposed a Regional Framework for Coverage Path-Planning for Precision Fertilization (RFCPPF) for crop protection UAVs in multi-area tasks. This framework includes three modules: nitrogen stress spatial distribution extraction, multi-area tasks environmental map construction, and coverage path-planning. Firstly, Sentinel-2 remote-sensing images are processed using the Google Earth Engine (GEE) platform, and the Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract the spatial distribution of nitrogen stress. A multi-area tasks environmental map is constructed to guide multiple UAV agents. Subsequently, improvements based on the Double Deep Q Network (DDQN) are introduced, incorporating Long Short-Term Memory (LSTM) and dueling network structures. Additionally, a multi-objective reward function and a state and action selection strategy suitable for stress area plant protection operations are designed. Simulation experiments verify the superiority of the proposed method in reducing redundant paths and improving coverage efficiency. The proposed improved DDQN achieved an overall step count that is 60.71% of MLP-DDQN and 90.55% of Breadth-First Search–Boustrophedon Algorithm (BFS-BA). Additionally, the total repeated coverage rate was reduced by 7.06% compared to MLP-DDQN and by 8.82% compared to BFS-BA.

Performance and Sustainability of Organic and Conventional Cotton Farming Systems in Egypt: An Environmental and Energy Assessment

Cotton cultivation is resource-intensive, posing significant environmental challenges, especially with conventional farming methods. Growing interest in sustainable agriculture drives the exploration of organic farming as a potential alternative with lower environmental impacts. Despite its benefits, organic farming often faces criticism for lower crop yields, sparking debates on the trade-offs between productivity and environmental impact. This study hypothesizes that organic cotton farming will have a smaller environmental footprint and higher energy efficiency compared to conventional methods. To test this hypothesis, a cradle-to-farm gate energy analysis and life cycle assessment (LCA) were conducted on both organic and conventional seed cotton production systems in the Beheira governorate of Egypt. The ReCiPe 2016 midpoint and endpoint characterization model was used for an environmental impact assessment. The impacts were evaluated using two functional units: one ton of seed cotton and one hectare of cultivated cotton. The findings revealed that organic cotton outperforms conventional cotton in net energy gain, efficiency, and profitability, with higher productivity and lower energy intensity. Regardless of the functional unit used (mass- or land-based), the assessed organic systems generally show a better environmental performance than the conventional systems in the local context, even when accounting for data uncertainty. This is due to lower input intensity and the use of less energy-intensive organic fertilizers and bio-fertilizers. Fertilization and irrigation are key factors influencing environmental impacts, with fertilization affecting midpoint impacts and irrigation affecting endpoint impacts. Therefore, precision fertilization, efficient irrigation practices, and effective nutrient and soil moisture management are recommended to minimize environmental impacts. Subsequent studies could explore whether similar patterns are observed in different geographic regions and evaluate additional social and economic aspects of cotton sustainability beyond environmental impacts. Future agricultural LCAs should use both mass-based and area-based functional units to capture a broader range of environmental effects and evaluate the co-benefits and trade-offs between organic and conventional practices.

Analysis of Macronutrients in Soil Using Impedimetric Multisensor Arrays.

The need to increase food production to address the world population growth can only be fulfilled with precision agriculture strategies to increase crop yield with minimal expansion of the cultivated area. One example is site-specific fertilization based on accurate monitoring of soil nutrient levels, which can be made more cost-effective using sensors. This study developed an impedimetric multisensor array using ion-selective membranes to analyze soil samples enriched with macronutrients (N, P, and K), which is compared with another array based on layer-by-layer films. The results obtained from both devices are analyzed with multidimensional projection techniques and machine learning methods, where a decision tree model algorithm chooses the calibrations (best frequencies and sensors). The multicalibration space method indicates that both devices effectively distinguished all soil samples tested, with the ion-selective membrane setup presenting a higher sensitivity to K content. These findings pave the way for more environmentally friendly and efficient agricultural practices, facilitating the mapping of cropping areas for precise fertilizer application and optimized crop yield.

Fertilizer and Crop Yield Prediction using Machine Learning

The agricultural sector is indispensable to feeding the growing global population, making efficient crop management and yield prediction imperative. Traditional farming practices often rely on subjective decision-making and generalized fertilizer application methods, leading to suboptimal resource utilization and yield outcomes. In this research, we introduce an innovative method Utilizing the capability the bunch of algorithms introduced for machine learning tasks to precise fertilizer recommendation and crop yield prediction. The developed system provides farmers with personalized fertilizer recommendations tailored to their specific soil and crop requirements, thereby minimizing waste and maximizing yield potential. Additionally, real-time monitoring and feedback mechanisms enable adaptive adjustments throughout the growing season, ensuring timely interventions to mitigate adverse outcomes and optimize productivity

Fertilizer application parameters for drip-irrigated peanut based on the fertilizer effect function established from a "3414" field trial.

Scientific fertilization is an important technical means of achieving high and stable peanut yields. Using soil testing and formula fertilization, the "3414" optimal regression design was used and included 14 nitrogen (N), phosphorus (P), and potassium (K) fertilization treatments. Ternary quadratic functions of the fertilizer effect were established according to three-season field experiments and the regression analysis of fertilizer-yield function was performed to explore the optimal fertilizer application mode and ratio for peanuts under mulched drip irrigation (MDI), and a suitable fertilizer application system was established. The ternary quadratic equation relating peanut yield (y) and the fertilizer application rates of N (N), P (P2O5), and K (K2O) was obtained after fitting, i.e., y = 2912.528 + 21.432N + 16.324P + 6.181K - 0.051N2 - 0.109P2 - 0.061K2 + 0.017NP + 0.023NK + 0.086PK, and significance analysis and typicality assessment were performed. The model R 2 was 0.9709, both values are extremely significant (p < 0.01), which indicates that the obtained ternary quadratic fertilizer effect function is typical and could be used for statistical purposes and fertilization recommendations. Three quadratic fertilizer effect functions were obtained. Among them, the equation for K is extremely significant, and the equations of N and P are significant. According to the assumption that the marginal yield is zero and the marginal profit is zero, the fertilizer application rate with the maximum yield, the fertilizer application rate with the best economic benefits, and the corresponding yields were obtained. The optimal fertilizer application rate predicted by the ternary quadratic fertilizer effect function was relatively high, so the three quadratic fertilizer effect functions were used for prediction. Under the test conditions, the recommended fertilizer application rates for peanuts under MDI are 256.6 kg N per ha, 164.2 kg P2O5 per ha, and 213.2 kg K2O per ha, the recommended fertilization ratio is 1:0.64:0.83, and the recommended ratio under formula fertilization is 23:15:19. The study has developed a data-based decision support system for Xinjiang drip-irrigated peanut, which assists farmers and agricultural managers in making more scientific and precise fertilization decisions based on the specific growth requirements of the crops and soil conditions. This evidence-based methodology enhances the precision of agricultural management, which is conducive to increasing crop yields while reducing resource wastage and environmental impact. However, multipoint and multiyear experiments are still needed to ensure that the findings are adaptable to the diverse soil conditions and fluctuating climate patterns that may be encountered in practice.

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.

Precision Fertilization: A critical review analysis on sensing technologies for nitrogen, phosphorous and potassium quantification

Fertilization is paramount for agriculture productivity and food security. Plant nutrition pre-established recipes and nutrient uptake are rarely managed by changing the fertilizer composition at the different stages of the plant life cycle. Herein we perform a literature review analysis – since the year 2000 and onwards – of the state-of-the-art capabilities of Nitrogen, Phosphorous, and Potassium (NPK) sensors for liquid fertilizers (e.g., hydroponics). From the initial search hits of 1660 results, only 53 publications had relevant information for this topic; from these, only 9 had NPK quantitative information. Qualitative analysis was performed by determining the number of publications for each nutrient, according to sample complexity and existing single, multiplexed or hybrid technologies. Quantitative assessment was performed by extracting the bias and linearity, the limit of detection and concentration ranges of sensor operation, framed into the context of the sensor technology development stage and sample compositional complexity. The most common technologies are colorimetry, ion-selective electrodes, optrodes, chemosensors, and optical spectroscopy. The most abundant technologies are for nitrate quantification, from which ion-selective electrodes are the most widely used technology, and sensors for phosphate quantification are the less developed. Most are at low technological levels of development, not dealing with the complexity of agriculture samples due to matrix effects and interference. Measuring the fertilizer composition, nutrient uptake, the state of the chemical network, and controlling the release of nutrients using new functional materials, is one of the most important challenges ahead for the existence of precision fertilization. Intelligent sensing and smart materials are today the most successful strategy for dealing with matrix effects and interferences, being led by ion-selective electrodes and spectroscopy technologies.

Optimal Design and Experiment of Electronically Controlled Inclined Spiral Precision Fertilizer Discharger

Optimal Design and Experiment of Electronically Controlled Inclined Spiral Precision Fertilizer Discharger