Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Geotechnologies on the phosphorus stocks determination in tropical soils: General impacts on society

Phosphorus (P) is a critical nutrient for primary production in terrestrial and aquatic ecosystems. As P mineral reserves are finite and non-renewable, there is an increasing discussion on its sustainable utilization to safeguard food security for future generations. Understanding the spatial distribution of soil P is central in advancing effective phosphorus management and fostering sustainable agricultural practices. This study aims to digitally map the stocks of available P (AP) and total P (TP) in Brazil at a fine resolution (30 m). Using the Random Forest machine learning algorithm and a database of topsoil (0-20 cm) with 28,572 samples for AP and 3154 for TP, we predicted P stocks based on environmental covariates related to soil formation processes. By dividing Brazil into two sub-regions, representing areas with native coverage and anthropogenic ones, we built independent predictive models for each sub-region. Our results show that Brazil has a TP stock of 531 Tg and an AP stock of 17.4 Tg. The largest soil TP stocks are in the Atlantic Forest biome (73.8 g.m2), likely due to higher organic carbon stocks in this biome. The largest AP stocks were in the Caatinga biome (2.51 g.m2) because of younger soils with low P adsorption capacity. We also found that fertilizer use significantly increased AP stocks in agricultural areas compared to native ones. Our results indicated that AP stocks strongly influenced Brazil's agricultural production, with a correlation coefficient ranging from 0.20 for coffee crops to 0.46 for soybean. The maps generated in this study are expected to contribute to the sustainable use of P in agriculture and environmental systems.

Sustainable strategy for rehabilitating phosphate mining sites and valorisation of phosphate industry by-products and sludge using pistachio tree (Pistacia atlantica), false pepper (Schinus molle), and eucalyptus (Eucalyptus globulus) trees

The development of anthroposols has been proposed as a new environmentally friendly approach to ensuring the successful revegetation of phosphate mining sites. The phosphate industry's by-products, including phosphogypsum (PG), phosphate sludge (PS), and sewage sludge (SS), can be valuable resources in restoring the ecological balance of mined soil areas. The aim of this study was to safely and sustainably restore the ecological integrity of the phosphate mining site through the evaluation of nutrients and heavy metals dynamics in soil and plant tissues of three tree species and treated by-products containing 65 % PG, 30 % PS, and 5 % SS. The tree species used were Pistacia atlantica, Schinus molle, and Eucalyptus globulus. The experimental layout was a randomised complete block design with six replicates and three treatments. Growth diameter, height, nutrient uptakes and heavy metal dynamic were evaluated from the rhizosphere soils and plant tissues over two years. Hierarchical head maps of correlations between the measured growth parameters, soil and nutrient uptakes of the tree species were analysed using a phylogenetic generalised linear mixed model. S. molle and E. globulus had higher average diameter and height than P. atlantica plants. P. atlantica and S. molle showed greater nitrogen, phosphorus, potassium, calcium, and magnesium concentrations than E. globulus trees. Tree growth parameters were closely linked to soil nutrient bioavailability. The heavy metal accumulation ratio was higher in the E. globulus and S. molle leaves than in stems. Using by-products could be valorised for rehabilitating mine sites together with E. globulus and S. molle species.

Evaluation of the feasibility of using inorganic and organic acids for the extraction of amino acids from high-protein material (mealworm larvae) by chemical hydrolysis

High-protein mealworm larvae (Tenebrio molitor) were treated with organic and inorganic acids in order to check the feasibility of applying acid hydrolyzates derived from them. Hydrolysis was optimized for the highest content of amino acids that have beneficial effects on plant development. The response surface method served the purpose of selecting optimal process conditions for such media as phosphoric acid, citric acid, acetic acid and mixtures of phosphoric and citric acids. The elemental composition of the best formulations and their amino acid profile was determined. Plant tests on cucumber (germination and pot tests) revealed the beneficial effect of the hydrolyzates on plant biometric parameters. By offering valuable insights into the relationship between fertilizer treatments and plant growth, our study contributes to developing sustainable agricultural practices and improved crop productivity.

The use of photosynthetic pigments and SPAD can help in the selection of bean genotypes under fertilization organic and mineral

The common bean is cultivated in all regions of the country, representing a product of great economic and social importance. In order to ensure food security in the world, it is necessary to create alternatives to reduce the dependence on fertilizers and seeds, and in this context, organic agriculture is a sustainable alternative to ensure it. Therefore, it becomes necessary to adapt rapid methods to monitor plant nutrition in real-time. The reflectance index determined by SPAD and pigment determination can be a sustainable alternative to identify genotypes in different fertilizations (organic × mineral fertilizer). The research hypothesis is to monitor nutritional management through pigment levels and reflectance index in common bean cultivars and their adaptation into different types of fertilization (organic × mineral fertilizer). Therefore, the objective of the research was to evaluate the common bean genotypes of the type carioca, in different fertilizations (organic × mineral fertilizer), and their effects on photosynthetic pigments, and the relationship between SPAD reflectance index and productivity. The experimental design used was a 2 × 7 factorial in randomized blocks with four replications: The first factor was the fertilization (organic × mineral fertilizer)and the second were the 7 genotypes (UFU-1; UFU-2; UFU-3; UFU-4; UFU-5; UFU-6 UFU-7), with UFU-1 being a hybrid obtained between genotypes UFU-4 and UFU-7; UFU-2 and UFU-3 were commercial genotypes; and UFU-4, UFU-5, UFU-6 and UFU-7 were genotypes from the UFU germplasm bank, located in the city of Monte Carmelo, Brazil. Evaluations were carried out for the agronomic characteristics of the plants, which were: height, number of branches, length and volume of roots, dry matter, leaf area index, number of flowers, number of pods, number of seeds per pod, 100 seed weight, and productivity of the genotypes. The results were compared with chlorophyll content and SPAD reflectance index, and the genotypes showed distinct behavior for each fertilization (organic × mineral fertilizer). The genotypes recommended for the organic fertilizer were UFU-2, UFU-6, and UFU-7, which showed higher productivity. For themineral fertilizer, the best-adapted genotype was UFU-4, with a higher productive yield. In conclusion, we can affirm that the highest chlorophyll and SPAD indices can help select common bean genotypes with higher productivity and adaptation within the organic fertilizer being this the main focus of this research. However, the other variables carried out during this research also demonstrated to have significant effects, so they could be analyzed individually and could offer valuable information in the selection of the best-adapted genotypes.

Valorization of phosphate sludge and its bacterial biomass as a potential bioformulation for improving tomato growth

Phosphorus (P) is a vital limiting nutrient element for plant growth and yield. In Morocco, the natural phosphate rock extractions generate significant amounts of phosphate wash sludge (PS), which could be reused productively, thus creating another added value for farmers. The present study aimed to demonstrate the combination effect of soil amendment by two different PS concentrations (1% and 5%) associated with three phosphate-solubilizing bacteria (PSB) consortia (C1, C2, and C3), isolated from phosphate mining sludge, on plant growth and nutrient uptake in tomato seedlings (Solanum lycopersicum). The results obtained showed that this bioformulation significantly improved P solubilization and plant growth compared to control conditions. Of all the combinations, C3-inoculated soil amended with 5% PS was the most effective in significantly improving plant height and dry and fresh biomass of shoots and roots. P solubilization and its availability for tomato seedlings uptake were maximal with the bioformulation (C3 + 5% PS). This latter enhanced P and potassium (K) uptake by 27.89 and 38.81% in shoots and 38.57% and 74.67% in roots, respectively, compared to non-inoculated soil amended with 5% PS. The highest flowering rate (200 %) was recorded in C3-inoculated soil amended with 5% PS. Supporting these results, the principal component analysis discriminated this bioformulation (C3 + 5% PS) from the other combinations. Our results open up prospects for upgrading phosphate sludge enriched with PSB consortia as a biofertilizer that can be used in ecofriendly agriculture integrated into the circular economy.

Phosphate-Solubilizing Bacteria: Advances in Their Physiology, Molecular Mechanisms and Microbial Community Effects

Phosphorus is an essential nutrient for all life on earth and has a major impact on plant growth and crop yield. The forms of phosphorus that can be directly absorbed and utilized by plants are mainly HPO42- and H2PO4-, which are known as usable phosphorus. At present, the total phosphorus content of soils worldwide is 400-1000 mg/kg, of which only 1.00-2.50% is plant-available, which seriously affects the growth of plants and the development of agriculture, resulting in a high level of total phosphorus in soils and a scarcity of available phosphorus. Traditional methods of applying phosphorus fertilizer cannot address phosphorus deficiency problems; they harm the environment and the ore material is a nonrenewable natural resource. Therefore, it is imperative to find alternative environmentally compatible and economically viable strategies to address phosphorus scarcity. Phosphorus-solubilizing bacteria (PSB) can convert insoluble phosphorus in the soil into usable phosphorus that can be directly absorbed by plants, thus improving the uptake and utilization of phosphorus by plants. However, there is no clear and systematic report on the mechanism of action of PSB. Therefore, this paper summarizes the discovery process, species, and distribution of PSB, focusing on the physiological mechanisms outlining the processes of acidolysis, enzymolysis, chelation and complexation reactions of PSB. The related genes regulating PSB acidolysis and enzymatic action as well as genes related to phosphate transport and the molecular direction mechanism of its pathway are examined. The effects of PSB on the structure and abundance of microbial communities in soil are also described, illustrating the mechanism of how PSB interact with microorganisms in soil and indirectly increase the amount of available phosphorus in soil. And three perspectives are considered in further exploring the PSB mechanism in utilizing a synergistic multi-omics approach, exploring PSB-related regulatory genes in different phosphorus levels and investigating the application of PSB as a microbial fungicide. This paper aims to provide theoretical support for improving the utilization of soil insoluble phosphorus and providing optimal management of elemental phosphorus in the future.

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Optimized Extraction of Polyphenols from Unconventional Edible Plants: LC-MS/MS Profiling of Polyphenols, Biological Functions, Molecular Docking, and Pharmacokinetics Study

Plant bioactive phenolic metabolites have recently attracted the attention of researchers due to their numerous health advantages. Therefore, this study aimed to investigate with advanced techniques the bioactive metabolites and antioxidant and antidiabetic capacity of four unconventional edible plant leaves: lemongrass (Cymbopogon citratus (DC.) Stapf), chicory (Cichorium intybus L.), moringa (Moringa oleifera Lam.), and ryegrass (Lolium perenne L.). The extraction process was optimized using different solvents. These plants' phenolic composition, identification, and characterization have been determined herein using LCESI-QTOF-MS/MS. This research identified 85 phenolic compounds, including 24 phenolic acids, 31 flavonoids, 7 stilbenes and lignans, and 17 other metabolites. Moreover, the study determined that moringa has the highest total phenolic content (TPC; 18.5 ± 1.01 mg GAE/g), whereas ryegrass has the lowest (3.54 ± 0.08 mg GAE/g) among the selected plants. It seems that, compared to other plants, moringa was found to have the highest antioxidant potential and antidiabetic potential. In addition, twenty-two phenolic compounds were quantified in these chosen edible plants. Rosmarinic acid, chlorogenic acid, chicoric acid, ferulic acid, protocatechuic acid, and caffeic acid were the most abundant phenolic acids. In silico molecular docking was also conducted to investigate the structure-function relationship of phenolic compounds to inhibit the alpha-glucosidase. Finally, the simulated pharmacokinetic characteristics of the most common substances were also predicted. In short, this investigation opens the way for further study into these plants' pharmaceutical and dietary potential.

Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves

BACKGROUND

Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves.

RESULTS

Two-layer estimation models were established by combining visible-NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root-mean-square error for prediction of 5.8969 mg kg^-1 . A pseudo-color image was developed using this stacking model to visualize the content and distribution of Cd.

CONCLUSION

The combination of visible-NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive. © 2022 Society of Chemical Industry.

Cold-tolerant phosphate-solubilizing Pseudomonas strains promote wheat growth and yield by improving soil phosphorous (P) nutrition status

It is well-known that phosphate-solubilizing bacteria (PSB) promote crop growth and yield. The information regarding characterization of PSB isolated from agroforestry systems and their impact on wheat crops under field conditions is rarely known. In the present study, we aim to develop psychrotroph-based P biofertilizers, and for that, four PSB strains (Pseudomonas sp. L3, Pseudomonas sp. P2, Streptomyces sp. T3, and Streptococcus sp. T4) previously isolated from three different agroforestry zones and already screened for wheat growth under pot trial conditions were evaluated on wheat crop under field conditions. Two field experiments were employed; set 1 includes PSB + recommended dose of fertilizers (RDF) and set 2 includes PSB – RDF. In both field experiments, the response of the PSB-treated wheat crop was significantly higher compared to the uninoculated control. In field set 1, an increase of 22% in grain yield (GY), 16% in biological yield (BY), and 10% in grain per spike (GPS) was observed in consortia (CNS, L3 + P2) treatment, followed by L3 and P2 treatments. Inoculation of PSB mitigates soil P deficiency as it positively influences soil alkaline phosphatase (AP) and soil acid phosphatase (AcP) activity which positively correlated with grain NPK %. The highest grain NPK % was reported in CNS-treated wheat with RDF (N–0.26%, P–0.18%, and K-1.66%) and without RDF (N-0.27, P-0.26, and K-1.46%), respectively. All parameters, including soil enzyme activities, plant agronomic data, and yield data were analyzed by principal component analysis (PCA), resulting in the selection of two PSB strains. The conditions for optimal P solubilization, in L3 (temperature-18.46, pH–5.2, and glucose concentration–0.8%) and P2 (temperature-17°C, pH–5.0, and glucose concentration–0.89%), were obtained through response surface methodology (RSM) modeling. The P solubilizing potential of selected strains at <20°C makes them a suitable candidate for the development of psychrotroph-based P biofertilizers. Low-temperature P solubilization of the PSB strains from agroforestry systems makes them potential biofertilizers for winter crops.

Proximal hyperspectral sensing of abiotic stresses in plants

Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.

Visible near-infrared hyperspectral imaging and supervised classification for the detection of small intestinal necrosis tissue in vivo

Complete recognition of necrotic areas during small bowel tissue resection remains challenging due to the lack of optimal intraoperative aid identification techniques. This research utilizes hyperspectral imaging techniques to automatically distinguish normal and necrotic areas of small intestinal tissue. Sample data were obtained from the animal model of small intestinal tissue of eight Japanese large-eared white rabbits developed by experienced physicians. A spectral library of normal and necrotic regions of small intestinal tissue was created and processed using six different supervised classification algorithms. The results show that hyperspectral imaging combined with supervised classification algorithms can be a suitable technique to automatically distinguish between normal and necrotic areas of small intestinal tissue. This new technique could aid physicians in objectively identify normal and necrotic areas of small intestinal tissue.

Prediction of Potassium in Peach Leaves Using Hyperspectral Imaging and Multivariate Analysis

Hyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or in pest/disease detection. As sensing technology advancement expands, measuring nutrient levels and disease detection also progresses. This study aimed to predict three different levels of potassium (K) concentration in peach leaves using principal component analysis (PCA) and develop models for predicting the K concentration of a peach leaf using a hyperspectral imaging technique. Hyperspectral images were acquired from a randomly selected fresh peach leaf from multiple trees over the spectral region between 500 and 900 nm. Leaves were collected from trees with varying potassium levels of high (2.7~3.2%), medium (2.0~2.6%), and low (1.3~1.9%). Four pretreatment methods (multiplicative scatter effect (MSC), Savitzky–Golay first derivative, Savitzky–Golay second derivative, and standard normal variate (SNV)) were applied to the raw data and partial least square (PLS) was used to develop a model for each of the pretreatments. The R2 values for each pretreatment method were 0.8099, 0.6723, 0.5586, and 0.8446, respectively. The SNV prediction model has the highest accuracy and was used to predict the K nutrient using the validation data. The result showed a slightly lower R2 = 0.8101 compared with the training. This study showed that HSI could measure K concentration in peach tree cultivars.

High-precision four-dimensional hyperspectral imager integrating fluorescence spectral detection and 3D surface shape measurement

A four-dimensional hyperspectral imager (FDHI) that combines fluorescence spectral detection and 3D surface morphology measurement is proposed. The FDHI consists of a hyperspectral line-scanner, a line structured light stereo vision system, and a line laser. The line laser is used as both the excitation light for the fluorescence and the scanning light line for the 3D profiling. At each scanning step, the system collects both fluorescent and 3D spatial data of the irradiated line region, which are fused to 4D data points based on a line mapping relationship between the datasets, and by scanning across the measurement object, a complete 4D dataset is obtained. The FDHI shows excellent performance with spatial and spectral resolution of 26.0 µm and 3 nm, respectively. The reported FDHI system and its applications provide a solution for 4D detection and analysis of fluorescent objects in meters measurement range, with advantage of high integration as two imaging modules sharing a same laser source.

Soil Microbes Determine Outcomes of Pathogenic Interactions Between Radopholus similis and Fusarium oxysporum V5w2 in Tissue Culture Banana Rhizospheres Starved of Nitrogen, Phosphorus, and Potassium

The contributions of soil biota toward outcomes of pathogenic interactions between Radopholus similis and Fusarium oxysporum V5w2 in tissue culture banana plants starved of nitrogen (N), phosphorus (P), and potassium (K) were investigated. The study was based on three screenhouse factorial experiments (2 × 2 × 2) comprising of potted banana plants with or without R. similis, with or without F. oxysporum V5w2, and either grown in sterile or non-sterile soil. All plants in each of the three experiments received nutrient solutions that were deficient in N, P, or K, respectively. In all the three nutritional regimes, plants inoculated with R. similis were heavily colonized by the nematode with high percentage dead roots and necrosis, while their root biomasses were low. N-starved plants co-inoculated with R. similis and F. oxysporum V5w2 had lower percentage dead roots and tended to have numerically lower nematode density compared to those treated with R. similis only, especially in non-sterile soil. N-starved plants inoculated with R. similis had higher shoot dry weight, were taller with more leaves that were larger, compared to those not inoculated with the nematode. Plants grown in non-sterile soil had lower percentage dead roots, necrosis and R. similis density than those from sterile soil, regardless of the nutrient regime. N-starved plants from non-sterile soil were shorter with smaller leaves having decreased chlorophyll content and lower biomass, compared to those from sterile soil. By contrast, P and K starved plants from non-sterile soil were taller with larger leaves and more biomass, compared to those from sterile soil. Roots inoculated with R. similis had higher endophytic colonization by Fusarium spp., especially when co-inoculated with F. oxysporum V5w2 and grown in sterile soil among the N and K-starved plants. In conclusion, pathogenic interactions between R. similis and F. oxysporum V5w2 are predominantly suppressed by a complex of soil microbes that exert plant growth promoting effects in tissue culture banana plants through N, P, and K dependent processes. Nitrogen is the most important limiting factor in rhizosphere interactions between banana roots, beneficial microbes and the pathogens. Soil sterilization and the stringent aseptic tissue culture techniques still require the development of alternative innovative ways of conserving microbial services for sustainable agriculture.

Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning

Biofortification, the enrichment of nutrients in crop plants, is of increasing importance to improve human health. The wild barley nested association mapping (NAM) population HEB-25 was developed to improve agronomic traits including nutrient concentration. Here, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients, sampled from two field experiments and four developmental stages. Particularly accurate predictions were obtained by partial least squares regression (PLS) modeling of leaf concentrations for N, P and K reaching coefficients of determination of 0.90, 0.75 and 0.89, respectively. We recognized nutrient-specific patterns of variation of leaf nutrient concentration between developmental stages. A number of quantitative trait loci (QTL) associated with the simultaneous expression of leaf nutrients were detected, indicating their potential co-regulation in barley. For example, the wild barley allele of QTL-4H-1 simultaneously increased leaf concentration of N, P, K and Cu. Similar effects of the same QTL were previously reported for nutrient concentrations in grains, supporting a potential parallel regulation of N, P, K and Cu in leaves and grains of HEB-25. Our study provides a new approach for nutrient assessment in large-scale field experiments to ultimately select genes and genotypes supporting plant biofortification.

Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics

In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics.

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.

Physiological and Morphological Responses of Hydroponically Grown Pear Rootstock Under Phosphorus Treatment

Phosphorus (P) is an essential macronutrient for the growth and development of fruit trees, playing an important role in photosynthesis, nucleic acid synthesis, and enzyme activity regulation. The plasticity of plant phenotypic has been investigated in diverse species under conditions of P-deficiency or P-excess. Based on these researches, P level fluctuations in different species result in different characteristics of the response. Nevertheless, little is known about the response of pear seedling rootstock (Pyrus betulifolia Bunge) to the changing of P levels. To explore the effects of different levels of P on the growth of pear seedling rootstock, we performed the hydroponic assays to determine and analyze the biological indexes including growth parameters, photosynthetic rate, root and shoot morphological traits, and concentrations of macro- and micronutrients. The results show that either deficiency or excess of P inhibited the growth and development of pear seedling rootstock. Root growth (down 44.8%), photosynthetic rate (down 59.8%), and acid phosphatase (ACP) activity (down 44.4%) were inhibited under the P-deficiency conditions (0mM), compared with normal P conditions (1mM). On the other hand, dark green leaves, suppression of root elongation (down 18.8%), and photosynthetic rate (down 25%) were observed under regimes of excessive P, compared with normal P conditions (1mM). Furthermore, the root concentration of not only P, but also those of other mineral nutrients were affected by either P treatment. In brief, these results indicated that a careful choice of P fertilizer supply is crucial to ensuring normal growth and development of pear seedling rootstock.

Phosphate bacterial solubilization: A key rhizosphere driving force enabling higher P use efficiency and crop productivity

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Phosphate bacteria bio-solubilization significantly increase crop P acquisition and productivity.

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Phosphate solubilizing bacteria increase RP agronomic efficiency as well as P fertilizers efficiency.

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This process can be optimized through a rational bacterial screening to assure efficient PSB are selected.

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Appropriate formulation of PSB is a sustainable approach to enhance P-fertilizers efficiency.

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Development of innovative PSB-Phosphate formulations is likely to sustain crop production.

Background

Increasing crop production to feed a growing population has driven the use of mineral fertilizers to ensure nutrients availability and fertility of agricultural soils. After nitrogen, phosphorus (P) is the second most important nutrient for plant growth and productivity. However, P availability in most agricultural soils is often limited because P strongly binds to soil particles and divalent cations forming insoluble P-complexes. Therefore, there is a constant need to sustainably improve soil P availability. This may include, among other strategies, the application of microbial resources specialized in P cycling, such as phosphate solubilizing bacteria (PSB). This P-mediating bacterial component can improve soil biological fertility and crop production, and should be integrated in well-established formulations to enhance availability and efficiency in use of P. This is of importance to P fertilization, including both organic and mineral P such as rock phosphate (RP) aiming to improve its agronomic efficiency within an integrated crop nutrition system where agronomic profitability of P and PSB can synergistically occur.

Aim of Review

The purpose of this review is to discuss critically the important contribution of PSB to crop P nutrition in concert with P fertilizers, with a specific focus on RP. We also highlight the need for PSB bioformulations being a sustainable approach to enhance P fertilizer use efficiency and crop production.

Key Scientific Concepts of Review

We first recognize the important contribution of PSB to sustain crop production, which requires a rational approach for both screening and evaluation of PSB enabling an accurate assessment of the bacterial effects both alone and in intertwined interaction with plant roots. Furthermore, we propose new research ideas about the development of microbial bioformulations based on PSB with a particular focus on strains exhibiting synergetic effects with RP.

4D surface shape measurement system with high spectral resolution and great depth accuracy

A 4D surface shape measurement system that combines spectral detection and 3D surface morphology measurements is proposed, which can realize high spectral resolution and great depth accuracy (HSDA system). A starring hyperspectral imager system based on a grating generates precise spectral data, while a structured light stereovision system reconstructs target morphology as a 3D point cloud. The systems are coupled using a double light path module, which realize point-to-point correspondence of the systems' image planes. The spectral and 3D coordinate data are fused and transformed into a 4D data set. The HSDA system has excellent performance with a spectral resolution of 3 nm and depth accuracy of 27.5 μm. A range of 4D imaging experiments are presented to demonstrate the capabilities and versatility of the HSDA system, which show that it can be used in broad range of application areas, such as fluorescence detection, face anti-spoofing, physical health state assessment and green plant growth condition monitoring.