A smart diagnostic tool based on deep kernel learning for on-site determination of phosphate, calcium, and magnesium concentration in a hydroponic system

Decision-tree-based ion-specific dosing algorithm for enhancing closed hydroponic efficiency and reducing carbon emissions

The maintenance of ion balance in closed hydroponic solutions is essential to improve the crop quality and recycling efficiency of nutrient solutions. However, the absence of robust ion sensors for key ions such as P and Mg and the coupling of ions in fertilizer salts render it difficult to effectively manage ion-specific nutrient solutions. Although ion-specific dosing algorithms have been established, their effectiveness has been inadequately explored. In this study, a decision-tree-based dosing algorithm was developed to calculate the optimal volumes of individual nutrient stock solutions to be supplied for five major nutrient ions, i.e., NO3, K, Ca, P, and Mg, based on the concentrations of NO3, K, and Ca and remaining volume of the recycled nutrient solution. In the performance assessment based on five nutrient solution samples encompassing the typical concentration ranges for leafy vegetable cultivation, the ion-selective electrode array demonstrated feasible accuracies, with root mean square errors of 29.5, 10.1, and 6.1 mg·L-1 for NO3, K, and Ca, respectively. In a five-step replenishment test involving varying target concentrations and nutrient solution volumes, the system formulated nutrient solutions according to the specified targets, exhibiting average relative errors of 10.6 ± 8.0%, 7.9 ± 2.1%, 8.0 ± 11.0%, and 4.2 ± 3.7% for the Ca, K, and NO3 concentrations and volume of the nutrient solution, respectively. Furthermore, the decision tree method helped reduce the total fertilizer injections and carbon emissions by 12.8% and 20.6% in the stepwise test, respectively. The findings demonstrate that the decision-tree-based dosing algorithm not only enables more efficient reuse of nutrient solution compared to the existing simplex method but also confirms the potential for reducing carbon emissions, indicating the possibility of sustainable agricultural development.

A Connected World: System-Level Support Through Biosensors

The goal of protecting the health of future generations is a blueprint for future biosensor design. Systems-level decision support requires that biosensors provide meaningful service to society. In this review, we summarize recent developments in cyber physical systems and biosensors connected with decision support. We identify key processes and practices that may guide the establishment of connections between user needs and biosensor engineering using an informatics approach. We call for data science and decision science to be formally connected with sensor science for understanding system complexity and realizing the ambition of biosensors-as-a-service. This review calls for a focus on quality of service early in the design process as a means to improve the meaningful value of a given biosensor. We close by noting that technology development, including biosensors and decision support systems, is a cautionary tale. The economics of scale govern the success, or failure, of any biosensor system.

B-site metal modulation of phosphate adsorption properties and mechanism of LaBO3 (B = Fe, Al and Mn) perovskites

La-based adsorbents are widely used for controlling phosphate concentration in water bodies. In order to explore the effect of different B-site metals regulating La-based perovskites on phosphate adsorption, three La-based perovskites (LaBO₃, B = Fe, Al, and Mn) were prepared using the citric acid sol-gel method. Adsorption experiments showed that LaFeO₃ exhibited the highest adsorption capacity for phosphate, which was 2.7 and 5 times higher than those of LaAlO₃ and LaMnO₃, respectively. The characterization results demonstrated that LaFeO₃ has dispersed particles exhibiting larger pore size and more pores than LaAlO₃ and LaMnO₃. Spectroscopy analysis and density functional theory calculation results showed that different B-positions cause a change in the type of perovskite crystals. Among them, the differences between lattice oxygen consumption ratio, zeta potential and adsorption energy are the main reasons for the differences in adsorption capacity. In addition, the adsorption of phosphate by La-based perovskites were well fitted with Langmuir isotherm and pursues the pseudo-second-order kinetic models. The maximum adsorption capacities were 33.51, 12.31 and 6.61 mg/g for LaFeO₃, LaAlO₃ and LaMnO₃, respectively. The adsorption mechanism was mainly based on inner-sphere complexation and electrostatic attraction. This study provides an explanation for the influence of different B sites on phosphate adsorption by perovskite.

Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

Nutrient solution plays an essential role in providing macronutrients to hydroponic plants. Determining nitrogen in the form of nitrate is crucial, as either a deficient or excessive supply of nitrate ions may reduce the plant yield or lead to environmental pollution. This work aims to evaluate the performance of feature reduction techniques and conventional machine learning (ML) algorithms in determining nitrate concentration levels. Two features reduction techniques, linear discriminant analysis (LDA) and principal component analysis (PCA), and seven ML algorithms, for example, k-nearest neighbors (KNN), support vector machine, decision trees, naïve bayes, random forest (RF), gradient boosting, and extreme gradient boosting, were evaluated using a high-dimensional spectroscopic dataset containing measured nitrate-nitrite mixed solution absorbance data. Despite the limited and uneven number of samples per class, this study demonstrated that PCA outperformed LDA on the high-dimensional spectroscopic dataset. The classification accuracy of ML algorithms combined with PCA ranged from 92.7% to 99.8%, whereas the classification accuracy of ML algorithms combined with LDA ranged from 80.7% to 87.6%. The PCA with the RF algorithm exhibited the best performance with 99.8% accuracy.

Selective Detection of Mg 2+ for Sensing Applications in Drinking Water

A new series of ligands containing the 2‐(2‐hydroxy‐3‐ naphthyl)‐4‐methylbenzoxazole (HNBO) fluorophore showed selectivity for Mg²⁺ ions, without the interference of Ca²⁺. The most promising representative L3 resulted the best performing sensor for Mg²⁺ both in solution and embedded in an all‐solid‐state optode, especially towards real samples of drinkable water.