Immature Citrus Fruit Research Articles

Metabolite Variations during the First Weeks of Growth of Immature Citrus sinensis and Citrus reticulata by Untargeted Liquid Chromatography-Mass Spectrometry/Mass Spectrometry Metabolomics.

Immature citruses are an important resource for the pharmaceutical industry due to their high levels of metabolites with health benefits. In this study, we used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to investigate the changes associated with fruit size in immature citrus fruits in the first weeks of growth. Three orange cultivars (Citrus sinensis 'Navel', Citrus sinensis 'Valencia', and Citrus sinensis 'Valencia Late') and a mandarin (Citrus reticulata Blanco 'Fremont') were separated into eight fruit sizes, extracted, and analyzed. Statistical analyses revealed a distinct separation between the mandarin and the oranges based on 56 metabolites, with an additional separation between the 'Navel' orange and the 'Valencia' and 'Valencia Late' oranges based on 21 metabolites. Then, metabolites that evolved significantly with fruit size growth were identified, including 40 up-regulated and 31 down-regulated metabolites. This study provides new insights into the metabolite modifications of immature Citrus sinensis and Citrus reticulata in the first weeks of growth and emphasizes the significance of including early sampled fruits in citrus maturation studies.

Gut microbiota metagenomics and mediation of phenol degradation in Bactrocera minax (Diptera, Tephritidae).

Gut microbiota mediating insect-plant interactions have many manifestations, either by provisioning missing nutrients, or by overcoming plant defensive reactions. However, the mechanism by which gut microbiota empower insects to survive by overcoming a variety of plant secondary metabolites remains largely unknown. Bactrocera minax larvae develop in immature citrus fruits, which present numerous phenolic compounds that challenge the larvae. To explore the role of gut microbes in host use and adaptability, we uncovered the mechanisms of phenol degradation by gut microbes using metagenomic and metatranscriptomic analyses, and verified the degradation ability of isolated and cultured bacteria. Research on this subject can help develop potential strain for the environmental friendly pest management operations. We demonstrated the ability of gut microbes in B. minax larvae to degrade phenols in unripe citrus. After antibiotic treatment, coniferyl alcohol and coumaric aldehyde significantly reduced the survival rate, body length and body weight of the larvae. The metagenomic and metatranscriptomic analyses in B. minax provided evidence for the presence of genes in bacteria and the related pathway involved in phenol degradation. Among them, Enterococcus faecalis and Serratia marcescens, isolated from the gut of B. minax larvae, played critical roles in phenol degradation. Furthermore, supplementation of E. faecalis and S. marcescens in artificial diets containing coniferyl alcohol and coumaric aldehyde increased the survival rate of larvae. In summary, our results provided the first comprehensive analysis of gut bacterial communities by high-throughput sequencing and elucidated the role of bacteria in phenol degradation in B. minax, which shed light on the mechanism underlying specialist insect adaption to host secondary metabolites via gut bacteria. © 2024 Society of Chemical Industry.

Geographic distribution, prevalence, and incidence of citrus black spot caused by Phyllosticta citricarpa in Tunisia

AbstractCitrus black spot (CBS), caused by Phyllosticta citricarpa, is an important disease of citrus, an economically important fruit crop worldwide and particularly in Tunisia. CBS was recently detected in Tunisia and this study revealed that it is widespread in the governorates of Nabeul and Sousse. The highest incidence of CBS was found in the Bouargoub region (73.7%), while the lowest was observed in Soliman (25.0%) in the Nabeul governorate. The surveys revealed that CBS affected mainly lemon (Citrus limon). The symptoms of CBS observed were hard spots on fruits, twigs, and leaves, as well as false melanose on fruits. Symptoms were observed in both mature and immature citrus fruits, but disease incidence was higher in mature (97.0%) than that in immature fruits (2.95%). In this study, morphological and molecular identifications confirmed the presence of P. citricarpa. This is the first time that the extent of CBS has been documented in a Mediterranean citrus area.

Limonin, a Component of Immature Citrus Fruits, Activates Anagen Signaling in Dermal Papilla Cells.

Hair loss remains a significant problem that is difficult to treat; therefore, there is a need to identify safe natural materials that can help patients with hair loss. We evaluated the hair anagen activation effects of limonin, which is abundant in immature citrus fruits. Limonin increased the proliferation of rat dermal papilla cells (rDPC) by changing the levels of cyclin D1 and p27, and increasing the number of BrdU-positive cells. Limonin increased autophagy by decreasing phosphorylated mammalian target of rapamycin levels and increasing the phospho-Raptor, ATG7 and LC3B. Limonin also activated the Wnt/β-catenin pathway by increasing phospho-β-catenin levels. XAV939, a Wnt/β-catenin inhibitor, inhibited these limonin-induced changes, including induced autophagy, BrdU-positive cells, and cell proliferation. Limonin increased the phosphorylated AKT levels in both two-dimensional cultured rDPC and three-dimensional spheroids. Treatment with the PI3K inhibitor wortmannin inhibited limonin-induced proliferation, and disrupted other limonin-mediated changes, including decreased p27, increased BrdU-positive cells, induced autophagy, and increased ATG7 and LC3B levels. Wortmannin also inhibited limonin-induced cyclin D1 and LC3 expression in spheroids. Collectively, these results indicate that limonin can enhance anagen signaling by activating autophagy via targeting the Wnt/β-catenin and/or PI3K/AKT pathways in rDPC, highlighting a candidate nutrient for hair loss treatment.

Smart tree crop sprayer utilizing sensor fusion and artificial intelligence

Delivering the appropriate amount of chemicals for each plant based on their needs is essential for reducing costs, waste, and labor. Smart agricultural machinery can provide variable-rate technologies using artificial intelligence (AI) and machine vision to optimize spraying applications. In this research, a low-cost smart sensing system for controlling airblast tree crop sprayers was designed and evaluated using citrus as a case study. The prototype comprised a LiDAR, machine vision, GPS, flow meters, sensor fusion, and AI to scan trees for tree height, tree classification, and fruit counting. Specifically, this smart sensing system can detect and classify objects to tree or non-tree (e.g., human, field constructions), measure tree height and canopy density, and detect and count fruit. Based on this information, it controls spraying nozzles to optimize spraying applications. A novel software was written in C++ and ran on an Nvidia Jetson Xavier NX embedded computer to process and control the data utilizing data fusion and AI techniques. The smart sensing system's results for tree height estimate indicated a relatively low average error of 6%. A convolutional neural network (CNN) was used to perform tree classification with an average accuracy of 84% in classifying the collected imagery into mature, young, dead, and non-tree objects. The fruit count module (also a CNN) had an F1 score of 89%, compared to ground-truth labeled images of mature and immature citrus fruits. Finally, the adoption of this new sensing method reduced spraying volume by 28%, compared to traditional spraying applications.

Active thermal imaging for immature citrus fruit detection

Yield mapping for citrus fruit is a challenging task due to factors such as varying illumination conditions, clustering, and occlusions. Mapping the yield for immature citrus fruit presents an additional challenge that is the colours of fruit and leaves are almost identical. Commonly used machine vision techniques using colour cameras become less effective for immature citrus fruit detection. This study explores a novel active thermal imaging method to tackle the problem of colour similarity between immature citrus fruit and leaves. In this study, a thermal camera was combined with a water spray system that applied water mist to citrus trees. The water mist caused temperatures of both the fruit and leaf surfaces to change but at different rates. Multiple parameters of the spray system were experimented with the goal to induce as much temperature differences as possible between fruit and leaf surfaces. The combined system was tested in a citrus grove for fruit detections. Deep learning models were built based on the active thermal imaging system and tracking and fruit counting algorithms were created to count fruit in thermal videos. A mean average precision of 87.2% was achieved by the models and an accuracy of 96% was achieved when comparing the number of fruit counted by the algorithms with the true number of fruit counted manually in the field.

Object Detection under Natural Illumination Conditions using Superpixels and Local Binary Pattern Feature

This paper proposes a novel object detection method under natural illumination conditions in which a set of features, texture features and Local Binary Pattern (LBP) features, are extracted from the images acquired by a colour camera. The goal of this study is to develop a robust and fast algorithm to detect immature green citrus fruit in individual trees from colour images acquired under natural outdoor conditions. Colour and shape features are used to remove the complex background as much as possible. Since leaves share much more similarities with green citrus fruit in colour and to some extent in the shape, texture features are used for citrus detection. Statistical features, Tamura features and LBP features are used to build the KNN classifier. Experimental results show that the proposed approach provides fairly good object detection performance and confirms an efficient way for outdoor green citrus detection.

풋귤 농약잔류 실태조사 및 가공방법에 따른 잔류특성

풋귤 농약잔류 실태조사 및 가공방법에 따른 잔류특성

Rh6 gene modulates the visual mechanism of host utilization in fruit fly Bactrocera minax.

Vision plays a critical role in host location and oviposition behavior for herbivorous insects. However, the molecular mechanisms underlying visual regulation in host recognition and oviposition site selection in insects remains unknown. The aim of this study was to explore the key visual genes that are linked to the host plant location of the fruit fly, Bactrocera minax. Using a host specialist fruit fly, B. minax, which lays eggs only into immature green citrus fruit, we undertook behavioral, transcriptomic, and RNAi research to identify the molecular basis for host fruit color recognition. In laboratory and field assays we found that adults prefer green over other colors, and this preference is significantly increased in sexually mature over immature flies. Furthermore, we identified that the Rh6 gene, responsible for green spectral sensitivity, has elevated expression in mature flies over immature flies. RNAi suppression of Rh6 eliminated the preference for green, resulting in a significant decrease in the number of eggs laid by B. minax in green unripe citrus. These results show that the Rh6 gene modulates the visual mechanism of host utilization in B. minax, providing a genetic basis for visual host location in a non-model insect herbivore. © 2018 Society of Chemical Industry.

Immature Citrus reticulata Extract Promotes Browning of Beige Adipocytes in High-Fat Diet-Induced C57BL/6 Mice

Obesity has become a global public health issue. Promoting browning of white adipose tissue (WAT) helps to maintain energy homeostasis. Previous studies have found that citrus fruit exhibits a number of biological activities. Although most citrus fruit drop has been considered agricultural waste, the ability to use it may be desirable. In this study, we investigate the antiobesity effects of immature citrus fruits in high-fat diet (HFD)-fed mice. The main phytochemical components of immature Citrus reticulata in water extraction analyzed by HPLC are synephrine, narirutin, hesperidin, nobiletin, and tangeretin (16.0 ± 1.08, 4.52 ± 0.31, 9.14 ± 0.32, 2.54 ± 0.07, 1.67 ± 0.05 mg/g, respectively). Oral administration of 1% immature Citrus reticulata extract (ICRE) for 11 weeks markedly reduced body weight gain, epididymal fat weight, fasting blood glucose, serum triglyceride, and total cholesterol ( P < 0.05 for all). In addition, histological analysis revealed that dietary ICRE decreased adipocyte size and hepatic steatosis compared to the HFD group ( P < 0.05 for both). Furthermore, we found that mice treated with ICRE have improved cold tolerance during acute cold challenge. These effects were associated with increased expression of uncoupling protein 1 (UCP1) and thermogenic genes in inguinal WAT. Taken together, these results suggest that ICRE can prevent obesity and lipid accumulation through induction of brown-like adipocyte formation.

Immature green citrus fruit detection using color and thermal images

Citrus fruit detection is one of the most important and challenging steps in citrus yield mapping. The distinct color differences between the ripe fruit and leaves allowed previously-described imaging-based methods to achieve good results. However, immature green citrus fruit detection, which aims to provide valuable information for citrus yield mapping at earlier stages is much more difficult because the fruit and leaf colors are very similar. This study combines color and thermal images for immature green fruit detections. Experiments identified optimal conditions for thermal imaging. A multimodal imaging platform was built to integrate color and thermal cameras. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color-Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. An increase in recall rate from 78.1% when using only color images to 90.4% after fusing the color and thermal images was obtained at similar precision rates, and an increase in precision rate from 86.6% to 95.5% was obtained at similar recall rates. The fusion of the color and thermal images effectively improved immature green citrus fruit detection.

Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis

Detecting immature fruit in groves provides a promising benefit for growers to plan application of nutrients and estimate their yield and profit prior to harvesting. The goal of this study was to develop a robust algorithm to detect and count immature citrus fruit in images of the tree canopy. Images were all taken in low natural light conditions with a flashlight, and the green component of the colour images was used for further analysis. Local intensity maxima were detected and local binary pattern (LBP) features around them were extracted as an input of an ensemble classifier-RUSBoost. The positive predictions were considered as candidates and the hierarchical contour maps around them were extracted and fitted with Circular Hough Transform. The fitted circles were predicted as fruit targets if its radius were in a predetermined range. The algorithm was evaluated with a test set of 25 images, achieved 80.4% true positive rate and 82.3% precision rate, and F-measure was 81.3%. The good performance of occlusion tolerance of the proposed method was mainly coming from the robust LBP texture descriptor and hierarchical contour analysis (HCA) which used the pattern of light intensity distribution on fruit surface. This study proposed an innovative method to detect green fruit in images of trees only by using texture and intensity distribution.

Correction to: Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images

Correction to: Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images

Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images

The original version of this article unfortunately contained a mistake. The affiliation “China West Normal University” should be removed from the author Dr. Chenglin Wang. The correct affiliation details are given below.

Feature extraction of hyperspectral images for detecting immature green citrus fruit

Feature extraction of hyperspectral images for detecting immature green citrus fruit

Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove

Recognition and detection of green immature citrus fruit more accurately and efficiently in groves under natural illumination conditions provides a promising benefit for growers to plan application of nutrients during the fruit maturing stages and estimate their yield and profit prior to the harvesting period. The goal of this study was to develop a robust and fast algorithm to detect and count immature green citrus fruit in individual trees from colour images acquired with different fruit sizes and under various illumination conditions. Adaptive Red and Blue chromatic map (ARB) was created and combined with the Hue image extracted after histogram equalization (HEH). The sum of absolute transformed difference (SATD), a block-matching method, was applied to detect potential fruit pixels. After OR operation of the results obtained from colour and SATD analysis which kept as many fruit pixels as possible, a kernel support vector machine (SVM) classifier was built with same learning sets used for different classification stages to remove false positives based on five selected texture features. The algorithm was evaluated with a set of testing images, and achieved more than 83% recognition accuracy. The proposed method can provide a more efficient way for green citrus identification in a grove using colour images.

Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images

A fast normalized cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system. As a template matching method, FNCC was used to detect potential fruit areas in the image, which was the very basis for subsequent false positive removal. Multiple features, including colour, shape and texture features, were combined in this algorithm to remove false positives. Circular Hough transform (CHT) was used to detect circles from images after background removal based on colour components. After building disks centred in centroids resulted from both FNCC and CHT, the detection results were merged based on the size and Euclidian distance of the intersection areas of the disks from these two methods. Finally, the number of fruit was determined after false positive removal using texture features. For a validation dataset of 59 images, 84.4 % of the fruits were successfully detected, which indicated the potential of the proposed method toward the development of an early yield mapping system.

Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions

Yield mapping for tree crops by mechanical harvesting requires automatic detection and counting of fruits in tree canopy. However, partial occlusion, shape irregularity, varying illumination, multiple sizes and similarity with the background make fruit identification a very difficult task to achieve. Therefore, immature green citrus-fruit detection within a green canopy is a challenging task due to all the above-mentioned problems. A novel algorithmic technique was used to detect immature green citrus fruit in tree canopy under natural outdoor conditions. Shape analysis and texture classification were two integral parts of the algorithm. Shape analysis was conducted to detect as many fruits as possible. Texture classification by a support vector machine (SVM), Canny edge detection combined with a graph-based connected component algorithm and Hough line detection, were used to remove false positives. Next, keypoints were detected using a scale invariant feature transform (SIFT) algorithm and to further remove false positives. A majority voting scheme was implemented to make the algorithm more robust. The algorithm was able to accurately detect and count 80.4% of citrus fruit in a validation set of images acquired from a citrus grove under natural outdoor conditions. The algorithm could be further improved to provide growers early yield estimation so that growers can manage grove more efficiently on a site-specific basis to increase yield and profit.

Green citrus detection using fast Fourier transform (FFT) leakage

Detection of immature green citrus fruit is important during the early life cycle of citrus fruit. It allows growers to manage citrus groves more efficiently and maximize yields by identifying expected fruit yields well in advance before harvesting. It also helps the growers prepare harvesting equipment and pickers for the harvesting operation. A novel technique was developed for detecting immature green citrus fruit from an outdoor color image and counting number of fruits. This technique is unique in that it is the first known attempt towards exploring it on green citrus fruits. A set of 71 images containing immature green citrus fruit was acquired in an experimental citrus grove at the University of Florida, Gainesville, Florida, USA. An algorithm was developed using a set of 11 training images by calculating the fast Fourier transform leakage values for fruit and leaves. A threshold value was obtained by comparing the percent leakage of fruit and other objects. The algorithm was tested on a set of 60 validation images. The correct total fruit count for a validation set came out to be 120, whereas the actual number of fruit was 146. The overall correct detection rate was 82.2 %. The proposed algorithm can be further improved to help growers manage their grove more efficiently.

Essential oils of citrus fruit stimulate oviposition in the Mediterranean fruit fly Ceratitis capitata (Diptera: Tephritidae)

Female Mediterranean fruit flies (medfly) Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) perceive both qualitative and quantitative aspects of citrus fruit chemistry. However, the behavioural and biological adjustments of this response remain largely unknown. In the present study, the ovipositional responses of gravid female medflies to essential oils (i.e. the most critical resistance factor to medfly infestation) of various citrus fruits are investigated. In dual‐choice (treatment versus distilled water control) experiments, females lay significantly more eggs into pre‐punctured hollow oviposition hemispheres (domes) provided with 1 µL of citrus peel oil from sweet orange, satsuma mandarin, bitter orange, grapefruit and lemon compared with odourless domes. No‐choice tests show a weak effect of lemon essential oils in stimulating oviposition. The female ovipositional response to sweet orange oil (the most active in eliciting oviposition) is dose‐dependent. Additionally, limonene, the most abundant chemical in all citrus oils, stimulates oviposition, whereas linalool, a representative compound of immature citrus fruit associated with high toxicity against immature stages of fruit flies, has a significant deterrent effect. In further no‐choice tests, females lay approximately 23% fewer eggs in limonene (93%) (amount found in orange oil) and 60% fewer eggs in limonene 93% plus linalool 3% (approximately 10‐fold the amount found in orange oil) mixtures, relative to sweet orange oil. The results suggest that the limonene content accounts largely (but not completely) for the ovipositional responses observed in sweet orange oil, whereas high linalool proportions are capable of significantly masking and/or disrupting its stimulatory effects in citrus oils. The importance and practical implications of these findings with respect to understanding how citrus fruit chemistry influences the ovipositional responses of medfly is discussed.