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Nutrient deficiency in Citrus reticulata (mandarin orange) plants is a critical agricultural issue that has far-reaching implications. Such deficiencies not only compromise the overall health and vigor of the mandarin orange trees but also render them more susceptible to diseases and pest infestations. Addressing this concern is of principal importance to ensure sustainable citrus cultivation and enhance crop yield. In this research, we propose a comprehensive approach to tackle the pervasive problem of nutrient deficiencies in Citrus Reticulata var. Fremont leaves. Our approach leverages cutting-edge techniques in image processing and machine learning to provide accurate and efficient detection of these deficiencies. The image data is divided into four classes: N Deficiency, P Deficiency, K Deficiency and Normal. The file sizes are compressed using a lossless compression method, resulting in an average file size reduction of 96.99%. The second stage involves applying segmentation processes to the images using the Canny and Sauvola methods. Third stage involves extracting colour and texture features from the images. The feature values will be used for the classification process in the next stage. The segmentation process employs two methods, namely Canny and Sauvola, which effectively separate the leaves from the background. The detection process is evident in the feature extraction phase, which utilizes two features: colour and texture. The fourth stage involves the classification process based on the segmentation results methods, performed separately using Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. These process results in four datasets: Canny-ANN, Canny-SVM, Sauvola-ANN, and Sauvola-SVM. The highest accuracy is achieved by the Sauvola-ANN method, with a value of 93.75%. [ABSTRACT FROM AUTHOR]

Nitrogen content is one of the essential elements in citrus leaves (CL), and many studies have been conducted to determine the nutrient content in CL using hyperspectral technology. To address the key problem that the conventional spectral data-denoising algorithms directly discard high-frequency signals, resulting in missing effective signals, this study proposes a denoising preprocessing algorithm, complete ensemble empirical mode decomposition with adaptive noise joint sparse representation (CEEMDAN–SR), for CL hyperspectral data. For this purpose, 225 sets of fresh CL were collected at the Institute of Fruit Tree Research of the Guangdong Academy of Agricultural Sciences, to measure their elemental nitrogen content and the corresponding hyperspectral data. First, the spectral data were preprocessed using CEEMDAN–SR, Stein's unbiased risk estimate and the linear expansion of thresholds (SURE–LET), sparse representation (SR), Savitzky–Golay (SG), and the first derivative (FD). Second, feature extraction was carried out using principal component analysis (PCA), uninformative variables elimination (UVE), and the competitive adaptive re-weighted sampling (CARS) algorithm. Finally, partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR) were used to construct a CL nitrogen prediction model. The results showed that most of the prediction models preprocessed using the CEEMDAN–SR algorithm had better accuracy and robustness. The prediction models based on CEEMDAN–SR preprocessing, PCA feature extraction, and GPR modeling had an R 2 of 0.944, N R M S E of 0.057, and R P D of 4.219. The study showed that the CEEMDAN–SR algorithm can be effectively used to denoise CL hyperspectral data and reduce the loss of effective information. The prediction model using the CEEMDAN–SR+PCA+GPR algorithm could accurately obtain the nitrogen content of CL and provide a reference for the accurate fertilization of citrus trees. [ABSTRACT FROM AUTHOR]

Nitrogen content is one of the essential elements in citrus leaves (CL), and many studies have been conducted to determine the nutrient content in CL using hyperspectral technology. To address the key problem that the conventional spectral data-denoising algorithms directly discard high-frequency signals, resulting in missing effective signals, this study proposes a denoising preprocessing algorithm, complete ensemble empirical mode decomposition with adaptive noise joint sparse representation (CEEMDAN–SR), for CL hyperspectral data. For this purpose, 225 sets of fresh CL were collected at the Institute of Fruit Tree Research of the Guangdong Academy of Agricultural Sciences, to measure their elemental nitrogen content and the corresponding hyperspectral data. First, the spectral data were preprocessed using CEEMDAN–SR, Stein’s unbiased risk estimate and the linear expansion of thresholds (SURE–LET), sparse representation (SR), Savitzky–Golay (SG), and the first derivative (FD). Second, feature extraction was carried out using principal component analysis (PCA), uninformative variables elimination (UVE), and the competitive adaptive re-weighted sampling (CARS) algorithm. Finally, partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR) were used to construct a CL nitrogen prediction model. The results showed that most of the prediction models preprocessed using the CEEMDAN–SR algorithm had better accuracy and robustness. The prediction models based on CEEMDAN–SR preprocessing, PCA feature extraction, and GPR modeling had an R2 of 0.944, NRMSE of 0.057, and RPD of 4.219. The study showed that the CEEMDAN–SR algorithm can be effectively used to denoise CL hyperspectral data and reduce the loss of effective information. The prediction model using the CEEMDAN–SR+PCA+GPR algorithm could accurately obtain the nitrogen content of CL and provide a reference for the accurate fertilization of citrus trees.

The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is ve1y impo1tant. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine leaming (ML) models, but the combination of increasing advances in computer vision appears the deep leaming (DL) area research to achieve a great potential in tenns of increasing accuracy. In this paper, two ways of conventional neural networks are used named AlexNet and ResNet models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small d11tasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for ResNet and AlexNet respectively. [ABSTRACT FROM AUTHOR]

The present study aims to discuss the data on levels and distribution of rare earth elements, including Y, (REYs) in leaves of three different citrus species (lemon, orange, and tangerine) and provide additional information about the major, minor and trace elements in two biological certified reference materials (CRMs), Apple leave (NIST SRM 1515) and Rye grass (ERM CD281). In all samples, element concentrations were determined by High Resolution Inductively Coupled Plasma Mass Spectrometry. The obtained data display substantial variability in the distribution of REY elements, not only between different citrus species but also between different genera of plants indicating their different uptake and accumulation abilities. Measured concentrations of REYs in citrus leaves were substantially lower compared to the literature values, although the fractionation indices were comparable. The data for CRMs provide additional information for the 14 elements in NIST SRM 1515 and the 30 elements in ERM CD281, including rare earth elements. [ABSTRACT FROM AUTHOR]

The objective of this study was to characterize chemical composition and functional properties of essential oils extracted from leaves of Citrus sinensis Osbeck, Citrus grandis Osbeck and Citrus aurantifolia Swingle. The essential oil of the C. sinensis and C. grandis leaves contained 44 compounds, accounted for total percentages of 94.17% and 93.78% of the oils, respectively, whereas the essential oil of the C. aurantifolia leaves had 62 compounds, accounted for 97.19% of the oil. Limonene, β-pinene, sabinene, β-ocimene, β-cubebene, terpinen-4-ol, linalool and nerol were main volatile compounds of the essential oil of the C. sinensis leaves, while the essential oil of the C. grandis contained high percentages of limonene, α-phellandrene, β-ocimene, β-caryophyllene, nerol, citronellol, geraniol, geranial and neral. The main volatile compounds of the essential oils of the C. aurantifolia leaves were Limonene, β-pinene, β-ocimene, β-caryophyllene, α-terpineol, citronellol and (+)-citronellal. The essential oil of the C. aurantifolia leaves exhibited the strongest scavenging activities (IC50 = 1.21 mg/ml) as compared to those of the C. sinensis (IC50 = 1.49 mg/ml) and C. grandis leaves (IC50 = 2.18 mg/ml). The essential oil of C. grandis leaves exhibited stronger antimicrobial activities than the essential oils of C. sinensis and C. aurantifolia leaves. [ABSTRACT FROM AUTHOR]

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Brazil is the world’s largest producer of oranges. The Brazilian conventional citrus crop requires repeated application of agrochemicals to achieve satisfactory levels of productivity. The organic citriculture is an alternative production system, which is environmentally friendly and offers a safe food to consumers. However, it is difficult to determine if a food or plant was cultivated in organic or conventional system by just common observation, which makes the customers of organic food market vulnerable against fraudulent entrepreneurs. In this study, we present a data mining approach for the study of Brazilian organic citrus leaves which can aid in the certification of authenticity of the citrus leaves. The elemental composition is determined by inductively coupled plasma-mass spectrometry (ICP-MS). We developed classification models based on support vector machines and artificial neural networks capable of predicting whether a citrus leaf is organic or conventional through analysis of the concentration levels of the 14 chemical elements (Al, Ba, Co, Cr, Cs, Cu, Fe, Mg, Mn, Ni, Rb, Si, Sr, and V) found in both types of leaves. Feature selection filter methods are used to determine the most relevant elements for the classification process. Our best model obtained was a support vector machine with approximately 88% prediction accuracy. The elements Mn, Mg, and Rb were evaluated as the most significant for the classification decision. This is the first paper which addresses the problem of classification of organic orange leaves based on chemical composition. The presented methodology is useful for attesting authenticity of organic citrus leaves and can be adapted for other organic food or substances. [ABSTRACT FROM PUBLISHER]

Delta-aminolevulinic ( δ -ALA) acid is an important intermediate for tetrapyrroles biosynthesis and it has recently received great attention in plant physiology and human toxicology. However, the colorimetric method which is the most common method for determination of δ -ALA is time consuming and is not specific. In this study, a method for determination of δ -ALA in plant tissues was developed based on the trimethylsilyl (TMS) derivative of the pyrrole formed from the reaction of δ -ALA with ethyl acetoacetate via Knorr condensation. The δ -ALA in the HCl extract was reacted with ethyl acetoacetate to form a pyrrole. Then, the pyrrole compound was extracted using ethyl acetate and the solvent was evaporated to dryness. The dried sample was derivatized to its TMS ester and analyzed using GC–MS. The concentration of δ -ALA in citrus leaves incubated with levulinic acid was also determined by the conventional colorimetric method. The linear range was 10–200 ppm in the full scan mode and 0.1–20 ppm in the selected ion monitoring (SIM). The limit of detection was 6 ppm in the full scan and 0.05 ppm in SIM mode, representing a four-fold increase in sensitivity compared to the colorimetric method. The GC–MS method developed in this study is simple, accurate, sensitive, and could also be used to measure δ -ALA in other biological samples. [ABSTRACT FROM AUTHOR]

Traditional methods of obtaining phosphorus content of citrus leaves are time-consuming procedures with complex operations which can be harmful to citrus trees. More over, traditional methods can not meet the demand of rapid and non-destructive monitoring of phosphorus content in large-scale citrus orchards. In this paper, we presented several models suitable for phosphorus content prediction in 4 growth periods using hyperspectral information. The experiments were conducted in the Crab Village of Luogang District, Guangzhou City, Guangdong Province, and the samples were 195 citrus trees planted. During 4 growth periods, i.e. germination, stability, bloom and picking period, hyperspectral reflectance of citrus leaves was respectively measured by spectrometer (ASD FieldSpec 3), and at the same time, phosphorus content of citrus leaves was obtained by using traditional chemical method. Owing to the high dimensionality and redundancy of raw data, an enhanced algorithm was provided based on manifold learning to deal with the high-dimensional spectral vectors for dimension reduction and feature extraction. First of all, the parameters of wavelet de-noising, which was applied to reduce the high-frequency noise, was determined through orthogonal test, and then 5 manifold learning algorithms, i.e. laplacian eigenmaps (LE), locally-linear embedding (LLE), local tangent space alignment (LTSA), isometric mapping (Isomap) and maximum variance unfolding (MVU) were applied to reduce dimension and extract features for de-noising spectrum. The 5 corresponding prediction models of support vector regression (SVR) for phosphorus content of citrus leaves were established based on their features. Besides, we compared the modeling results of different spectral forms. Some critical conclusions were obtained. First, the optimized parameter combination of wavelet de-noising through orthogonal test was: "coif2" as wavelet basis function, the number of decomposition layer being 7 and "heursure" as the threshold, respectively. Second, the experimental results revealed that these 5 manifold learning algorithms were effective for phosphorus content estimation of citrus leaves. When the raw spectrum was used as the input vector, the Isomap-SVR model achieved better performance than other models; the coefficients of determination for the calibration set were 0.9383, 0.9614, 0.9611, 0.9516 and 0.9430, and the corresponding values of root mean square error (RMSE) were 0.0548, 0.0503, 0.0456, 0.0534 and 0.527 at germination, stability, bloom, picking period and whole growth period, respectively; for the validation set, the coefficients of determination were 0.8866, 0.8923, 0.9236, 0.9005 and 0.8870, and the values of RMSE were 0.0710, 0.0688, 0.0583, 0.0667 and 0.0704, respectively, which meant high usability for the industry. Third, when first derivative spectrum was used as the input vector of the samples with wavelet de-noising, in our research, the Isomap-SVR model achieved the best result and the coefficients of determination for calibration set were 0.9383, 0.9614, 0.9611, 0.9516 and 0.9430 respectively, and the corresponding values of RMSE were 0.0518, 0.0405, 0.0408, 0.0458 and 0.0499 respectively at germination, stability, bloom, picking period and whole growth period; and for the validation set, the coefficients of determination were 0.8913, 0.9107, 0.9373, 0.9135 and 0.8949, and the corresponding values of RMSE were 0.0703, 0.0645, 0.0522, 0.0634 and 0.0659 respectively. Finally, our research proves the feasibility of monitoring phosphorus content of citrus leaves, and may provide a theoretical basis for growth monitoring and nutritional diagnosis of citrus trees. [ABSTRACT FROM AUTHOR]

The nutritional status of citrus leaves is very important to the determining of fertilization plans. The spectrum technique is a quick, un-injured method and is becoming widely used for plant nutrient estimation. The possibility and method of using spectrum technique to estimate the nutrient of citrus leaf was explored in this study. A total amount of 135 leaves from the mature spring shoots of navel orange trees (C. sinensis Osbeck, "Newhall") were collected and randomly grouped into two sets of samples: 100 leaves for the calibration set and 35 leaves for the prediction set. The hyperspectral images were scanned upper and lower side of each leaf and then the total nitrogen (N) and phosphorus (P) contents of each leaf were measured. The raw spectra data were extracted to generate average spectra curves, preprocessed with five different methods, and was used to build N and P content prediction models. The performances of the five preprocessing methods, i.e., Savitzky-Golay smoothing (SGS), standard normal variate (SNV), multiplicative scatter correction (MSC), first-derivative (1-Der) and second-derivative (2-Der), were tested with linear partial least squares (PLS) models and nonlinear least squares-support vector machine (LS-SVM) models. The results showed that the SG-PLS and PLS were the best for the N predicting (Rp=0.9049, RMSEP=0.1041) and P (Rp=0.9235, RMSEP=0.0514) in citrus leaves, respectively; the hyperspectral image data from leafs upper side predicting better for the contents of N and P. The study suggested that the hyperspectral image data from the upper side of the citrus leaves are suitable for nondestructive estimation of nutrient content. [ABSTRACT FROM AUTHOR]

Traditional methods of obtaining chlorophyll content of citrus leaves require grinding citrus leaves and applying chemical titrations, which would be harmful to citrus trees and time-consuming. Besides, it's difficult to integrate those chemical methods into variable spraying system as a feedback subsystem. In this paper, we discuss several rapid and non-destructive methods in obtaining chlorophyll content of citrus leaves by using hyperspectral analysis system. Hyperspectral technology obtains synchronously spectrum in continuous space, where we can derive crop growth information visually in a non-destructive way. In this paper, the modeling of chlorophyll content of citrus leaves based on the hyperspectrum was discussed. Field experiments were conducted on 117 planted Luogang citrus trees in the Crab Village of Luogang District, Guangzhou City, Guangdong Province. Hyperspectral reflectance and chlorophyll content of citrus leaves were measured by spectrometer (ASD FieldSpec 3) and traditional spectrophotometry, respectively, during four different growth periods corresponding to germination period, stability period, bloom period and harvesting period. In this way, each sample was presented as an instance-labeled pair, where a high-dimensional vector was regarded as the descriptor along with the measured value of chlorophyll content. All the collected samples constituted a large-scale dataset with totally 468 tuples, 80% of which were utilized as the training set and remaining 20% as the testing set. The model constructed relied on the training set and the testing set was evaluated respectively. Using original spectrum and its transformations as input vector, two models, support vector regression (SVR) based on principle component analysis (PCA) and partial least square regression (PLSR) based on the wavelet denoising were adopted, where PCA was adopted for dimension reduction and the wavelet denoising technique removed high-frequency noise. The two models (SVR and PLSR) were then applied to the final regression analysis for predicting chlorophyll content. The best coefficient of determination (R2) of the calibration set and a validation set of the entire growth period were up to 0.8713 and 0.8670, the root-mean-square error (RMSE) was 0.1517 and 0.1544 respectively. Some main conclusions were obtained: first, when the original reflectance spectrum was used as the input vector and the energy ratio remained 96% for PCA in germination period and stability period, 99% for PCA in bloom period, harvesting period and the whole growth period, SVR with the radial basis function (RBF) as the kernel function achieved the best performance. Second, the wavelet denoising for hyperspectrum data could improve the model performance to some extent. When "sym8" was used as the wavelet basis function, "rigrsure" as the threshold selection, "sln" for rescaling using a single estimation of level noise based on first-level coefficients as the threshold rescaling project and the decomposition layer was 5, PLSR achieved the best result in this research and the coefficient of determination of calibration set and the validation set of the whole growth period were up to 0.8706 and 0.8531, which increased by 8.3% and 9.3% compared with the model without the wavelet denoising. Third, comparative tests between our best model and other models demonstrate the validity and robustness of the two models we derived. Further experimental results revealed that these two models were superior to principle component regression (PCR), stepwise multiple linear regression (SMLR) and back propagation (BP) neural networks. Finally, hyperspectral technology could obtain accurate chlorophyll content of citrus leaves rapidly, quantitatively and non-destructively, our research may provide a theoretical basis for nutrition surveillance of citrus growth. [ABSTRACT FROM AUTHOR]

• Introducing a new citrus leaf diseases dataset called CCL'20 with annotations. • Introducing various recent state-of-the-art deep learning architectures for citrus disease detection. • In-depth comparison of the architectures and fine-tuning them for fast and precise detection of citrus diseases on leaves. • Accurate diagnosis of multiple instances of diseases of variable size on citrus leaves. Citrus (Citrus reticulata) plants are affected by several diseases and require keen attention to detect and cure the diseases in time; otherwise, significant financial loss is incurred. With the advancement of computer vision and deep learning techniques, identifying various diseases is becoming simpler. However, this process requires a proper dataset of infected leaves and a suitable detector to recognise the diseases. Because the publicly available citrus leaf datasets are not annotated, they are not suited for disease detection tasks. Therefore, a new dataset (called CCL'20) comprising images of infected citrus leaves with multiple classes of diseases, including precise annotations, is developed. Primarily, machine learning models are used in plant disease detection, and only limited deep learning models are utilised in agricultural applications. This paper has identified the CNN based detectors best suited for agricultural engineering, such as CenterNet, YOLOv4, Faster-RCNN, DetectoRS, Cascade-RCNN, Foveabox and Deformabe Detr, implemented and fine-tuned them to detect citrus leaf diseases using our CCL'20 dataset. Extensive performance and computational analysis is carried out to determine how effectively these models diagnose different stages of citrus leaf diseases. This paper presents the state-of-the-art CNN detectors for citrus leaf disease detection, evaluated based on their precision, recall, and other valuable parameters such as training parameters, inference time, memory usage, speed and accuracy trade-off for each model. The results show that the Scaled YOLO v4 P7 achieves fast and early prediction of the diseases, and CenterNet2 with Res2Net 101 DCN-BiFPN predicts the early stage of citrus leaf diseases with high accuracy to other recent and efficient detecting models. [ABSTRACT FROM AUTHOR]

Huanglongbing (HLB) is an important citrus disease greatly affecting the citrus industry in Florida and other parts of the world. Early disease detection would control the spread of this disease through the application of suitable management measures. This study evaluates the application of fluorescence sensing for HLB detection of citrus leaves. A commercial handheld fluorescence sensor was used to collect yellow, red, and far-red fluorescence at ultraviolet (UV), blue, green, and red excitations from healthy, nutrient-deficient, and HLB-infected leaves of two different sweet orange cultivars, Hamlin and Valencia. Evaluation of the fluorescence sensing was performed under laboratory (controlled) and field conditions. The Naïve-Bayes and the bagged decision tree classifiers were trained and tested to assess their performance in classifying the healthy and stressed (nutrient-deficient) leaves. Results revealed that the Naïve-Bayes classifier yielded high classification accuracy under laboratory conditions (higher than 85%), while the bagged decision tree classifier yielded high overall classification accuracy under both laboratory and field conditions (higher than 94%). The bagged decision tree classifier performed better than the Naïve-Bayes classifier, resulting in higher classification accuracy, although the computation time was at least 10 times greater than that of the Naïve-Bayes classifier. In addition, feature extraction using forward feature selection indicated that fluorescence features such as yellow fluorescence (UV excitation) and simple fluorescence ratio (green excitation) contributed toward differentiating healthy leaves from nutrient-deficient and HLB-infected leaves. [ABSTRACT FROM AUTHOR]

The spring shoot leaves are important sites of nitrogen (N) metabolism in citrus trees. Understanding the physiological and metabolic response of the spring shoot leaves under varying N fertilization is fundamental to the fertilization management in citrus orchards. Thus, the processes affecting N composition, the activities of N metabolism related enzymes, and the expression of relevant genes were explored in spring shoot leaves under four N levels (0, 207, 275, 413 g N tree−1 y−1, as N0, N207, N275, N413). The results showed that, compared with N0, N275 significantly increased total N by 24.81%, which was mainly attributed to enhancement of structural N by 30.92%, free amino acid N by 40.91% and nitrate N by 41.33%. The relative expression of nitrate reductase (NR) and glutamate dehydrogenase (GDH) under N275 increased by 19.32% and 73.48%, respectively, compared with that under N0 treatment. Compared with N0 treatment, the NR transcription level under N275 treatment increased by 381%. The relative transcription levels of NADP-GDH and GDH1 also increased with increasing N fertilization. However, compared with that under N275, the relative transcription of GDH2 under N413 treatment was inhibited. Therefore, the transcript abundance of NR , NADP-GDH, GDH1 and GDH 2 affected the activities of NR and GDH and thereby contributed to the regulation of N composition in the leaves. In addition, the activities of glutamine synthetase and nitrite reductase were largely unaffected or even declined in the N207, N275 and N413 treatments compared with the N0. This study elucidated the mechanism of primary N metabolism and partitioning in citrus leaves and provided a theoretical basis for N management in citrus orchards. • Nitrogen (N) supply in mature spring citrus leaves affects N allocation and primary N metabolism. • N fertilization stimulates N assimilation and modifies N partitioning in citrus leaves. • Structural N is the main form of N in citrus leaves followed by soluble protein N. • N metabolism enzymes are partly affected by the transcriptional level in response to N fertilization. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new approach is proposed to identify the SSC of citrus using indirect LIBS with its leaves. • By improving the GA, the problem of the local optimal solution for the recognition model is solved. • The influence of element in citrus leaves on citrus's SSC was analyzed by ANOVA. • The indirect LIBS with citrus leaves can nondestructively and reliably identify the citrus's SSC. The soluble solid content (SSC) is vital to distinguish citrus quality, and non-destructive detection on site in the identification of SSC for citrus is urgently demanded. In this paper, a new approach was proposed to accurately identify citrus's SSC using indirect laser-induced breakdown spectroscopy (LIBS) with its leaves. Forty-five Ehime Kashi No.28 citruses with surrounding leaves picked at two different times were prepared for LIBS experiment. The genetic algorithm-support vector machine model had the best identification result with cross-validation set accuracy of 91.2% and the prediction set accuracy of 92.03%. Furthermore, the influence of element in citrus leaves on SSC identification was analyzed by one-way analysis of variance, which showed that magnesium in citrus leaves was notably related to SSC due to its participation in photosynthesis and carbohydrates transportation. All in all, the results demonstrated that the indirect LIBS with citrus leaves could be a non-destructive and reliable approach for accurate identification of citrus SSC on spot. [ABSTRACT FROM AUTHOR]

Although leaves and other vegetative tissues are generally considered as non-climacteric, citrus leaves show a climacteric system II behaviour after detachment. Upon harvest, young, fully expanded ‘Valencia’ orange (Citrus sinensis) leaves (∼60-d-old) exhibited two phases of ethylene production. The first phase, up to 6 d after detachment, was characterized by a low and constant ethylene production (system I pathway), associated with a constitutive expression of ACC synthase 2 (CsACS2), CsERS1, and CsETR1. ACC synthase 1 (CsACS1) was not expressed during this phase and autoinhibition of ethylene production was apparent following treatment with exogenous ethylene or propylene. The second phase, 7–12 d after detachment, was characterized by a climacteric rise in ethylene production, preceded by the induction of CsACS1 and ACC oxidase 1 (CsACO1) gene expression in the system II pathway. This induction was accelerated and augmented by exogenous ethylene or propylene, indicating an autocatalytic system II ethylene biosynthesis. Mature leaves (6–8-months-old) behaved similarly, except that the climacteric peak in ethylene production occurred earlier (day 5). Young and mature leaves varied in the timing of the climacteric ethylene rise and CsACS1 and CsACO1 induction. The two phases of ethylene production, system I and system II, were also detected in wounded leaf discs of both young and mature leaves. The first phase peaked 15 min after excision and the second phase peaked after 6 h. [ABSTRACT FROM PUBLISHER]

When leaves of Citrus sinensis (L.) Osbeck cv. Shamouti senesce, they become more susceptible to abscission and the proximal 2 mm of their lamina-petiole abscission zones exhibit callose deposition. The degree of senescence, assayed with the DARWIN image processor (Telewski et al. 1983), was positively correlated in a linear fashion with callose deposition. Explant of non-senescing leaves were observed. Excision of the leaf at the stem-petiole junction induced callose deposition throughout the petiole, but not in the lamina. Callose deposition began immediately upon excision and reached a maximum at 3 h. It then decreased slightly and remained at the same level for up to 5 days. Exogenous compounds that decrease callose deposition, e.g. laminarase and 2-deoxy-D-glucose, inhibited the rate of abscission of explants. Compounds that promote callose deposition, e.g. uridine diphosphoglucose and mannose, increased the rate of abscission of explants. Exogenous callose, e.g. laminarin, increased the rate of abscission. It is not known how callose might be causally involved in promoting abscission. [ABSTRACT FROM AUTHOR]

• An Accelerated Solvent Extraction (ASE) method for 20 pesticides was developed. • An Energized Dispersive Guided Extraction (EDGE) method was compared. • ASE and EDGE methods were used to extract pesticides from citrus and alfalfa leaves. • The ASE and EDGE instruments had comparable extraction efficiency. • The EDGE instrument extracted samples ~4.5 times faster than the ASE instrument. Various techniques have been evaluated for the extraction and cleanup of pesticides from environmental samples. In this work, a Selective Pressurized Liquid Extraction (SPLE) method for pesticides was developed using a Thermo Fisher Scientific Accelerated Solvent Extraction (ASE) system. This instrument was compared to the newly introduced (2017) extraction instrument, the Energized Dispersive Guided Extraction (EDGE) system, which combines Pressurized Liquid Extraction (PLE) and dispersive Solid Phase Extraction (dSPE). We first optimized the SPLE method using the ASE instrument for pesticide extraction from alfalfa leaves using layers of Florisil and graphitized carbon black (GCB) downstream of the leaf homogenate in the extraction cell (Layered ASE method). We then compared results obtained for alfalfa and citrus leaves with the Layered ASE method to those from a method in which the leaf homogenate and sorbents were mixed (Mixed ASE method) and to similar methods modified for use with EDGE (Layered EDGE and Mixed EDGE methods). The ASE and EDGE methods led to clear, colorless extracts with low residual lipid weight. No significant differences in residual lipid masses were observed between the methods. The UV-Vis spectra showed that Florisil removed a significant quantity of the light-absorbing chemicals, but that GCB was required to produce colorless extracts. Recoveries of spiked analytes into leaf homogenates were generally similar among methods, but in several cases, significantly higher recoveries were observed in ASE extracts. Nonetheless, no significant differences were observed among pesticide concentrations in field samples when calculated with the isotope dilution method in which labelled surrogates were added to samples before extraction. The extraction time with the ASE methods was ~45 minutes, which was ~4.5 times longer than with the EDGE methods. The EDGE methods used ~10 mL more solvent than the ASE methods. Based on these results, the EDGE is an acceptable extraction instrument and, for most compounds, the EDGE had a similar extraction efficiency to the ASE methods. [ABSTRACT FROM AUTHOR]