Green Citrus Fruit Research Articles

Lightweight green citrus fruit detection method for practical environmental applications

Real-time detection of green citrus fruit is crucial for accurate fruit localization and early yield prediction in the citrus growing process. This detection involves three imaging steps: acquisition, transmission, and detection. Image quality during acquisition and transmission also impacts detection accuracy. To enhance accuracy, we propose a lightweight method for detecting green citrus fruit in practical environments. The method applies a random sequence of four operations (blurred kernel, noise, size reduction, and compression) to preprocess original citrus fruit images, simulating real-world blurring and constructing a new dataset. A knowledge model is trained to reconstruct blurry citrus fruit images and address accuracy issues caused by blurring in practical environments. The YOLOv5 backbone network combines a convolutional neural network (CNN) for local information and a transformer for global background features. This fusion encodes both local and global information, learning representations efficiently and improving the detection model. In the YOLOv5 neck network, feature weighting optimizes representation of citrus fruit in space and channels, enhancing accuracy by reducing background weight. The proposed method, evaluated using a constructed dataset, outperforms mainstream lightweight detection models with 93.6% accuracy, 6.3 M parameters, and a 12.4 M model size. Compared to YOLOv5-MobileNetv3-small and YOLOv5-ShuffleNetv2, accuracy improves by 8.5% and 10.98%, respectively. Compared to YOLOv5s, parameters decrease by 9.6%, model size decreases by 8.82%, and accuracy improves by 1.5%. The proposed method demonstrates robust and effective detection of green citrus fruit in natural environments, providing guidance for accurate localization and early yield prediction. Data and methods used in this paper can be found at: https://github.com/PingfuChen/Citrus_detection.git.

Citrus depressa Hayata peel ameliorates nonalcoholic fatty liver and modulates the activity of hepatic drug-metabolizing enzymes and transporters in rats fed a high-fat diet.

Citrus depressa Hayata is a small, green citrus fruit native to Taiwan and Japan. Citrus peel contains polymethoxylated flavones, including nobiletin and tangeretin, and may exhibit strong antioxidant and anti-inflammatory activities. A preliminary study revealed that Citrus depressa Hayata peel (CDHP) ethanolic extract reduced fat accumulation and the concentration of reactive oxygen species in human HepG2 cells exposed to oleic acid. The effects of CDHP on the activity of hepatic drug-metabolizing enzymes and membrane transporters in high-fat (HF) diet-induced fatty liver were investigated. Male rats were fed a low-fat diet, a HF diet, and a HF diet containing 4% CDHP for 11 weeks. The low-fat and HF diet respectively contained 13.5% and 38.1% of daily total calories from dietary fat. CDHP supplementation reduced the HF diet-induced accumulation of triglycerides in the liver and lowered hepatic fatty acid synthase activity. Higher faecal excretions of cholesterol, triglycerides, and total bile acids were observed after CDHP treatment. CDHP lowered the HF diet-induced increase in the mRNA expressions of nuclear factor erythroid 2-related factor 2, aryl hydrocarbon receptor, pregnane X receptor, and peroxisome proliferator-activated receptor-α and the activities of cytochrome P-450 (CYP)1A1, 1A2, 2B, and 2E1. However, increased hepatic CYP3A activity was observed in rats fed the HF diet containing CDHP. A higher hepatic multidrug resistance-associated protein 2 level was observed after CDHP treatment. After CDHP administration (1 g per kg body weight) for 1 h, nobiletin was found in plasma and various tissues and was abundant in the liver. An in vitro study revealed that the activity of various CYP enzymes in liver microsomes was inhibited by CDHP ethanolic extract and nobiletin, with IC50 values ranging from 18.5 to 54.4 μg ml-1 and from 13.0 to 33.2 μM, respectively. The results of this study suggest that CDHP might reduce hepatic steatosis and alter drug-metabolizing enzymes and transporters in HF diet-induced nonalcoholic fatty liver diseases.

A New Perspective on Detection of Green Citrus Fruit in the Grove Using Deep Learning Neural Networks

The entire citrus industry is under attack by terminal citrus diseases such as Huanglongbing (HLB) or citrus greening (Candidatus Liberibacter asiaticus), and citrus canker (Xanthomonas axonopodis pv. citri). There is a need to develop fruit quality assessment techniques that can assess the quality of green citrus fruits during the growing season when green fruits are still on the trees. Learning technique is one of the fundamental techniques for fruit quality evaluation.

Expression of Hexokinase in Stomata of Citrus Fruit Reduces Fruit Transpiration and Affects Seed Development.

The temporal formation and spatial distribution of stomata on the surface of citrus floral organs and, specifically, on the ovule from which the fruit develops, were analyzed using citrus plants that express green fluorescent protein (GFP) under the guard cell-specific KST1 promoter. Stomata are found on the style, sepal, and anther of the closed flower and on ovules from the stage of anthesis. It has previously been shown that hexokinase (HXK) mediates sugar-sensing in leaf guard cells and stimulates stomatal closure. The activity and response of citrus fruit stomata to sugar-sensing by HXK was examined using plants that express HXK under the KST1 promoter. Those plants are referred to as GCHXK plants. The transpiration of young green GCHXK citrus fruits was significantly reduced, indicating that their stomata respond to sugar similar to leaf stomata. Toward fruit maturation, fruit stomata are plugged and stop functioning, which explains why WT and GCHXK mature yellow fruits exhibited similar water loss. Seeds of the GCHXK plants were smaller and germinated more slowly than the WT seeds. We suggest that the stomata of young green citrus fruits, but not mature yellow fruits, respond to sugar levels via HXK and that fruit stomata are important for proper seed development.

Physical exercises and fat burners – the easy road to severe consequences?

The aim of the study is to identify the features of the regular aerobic exercise effect, dietary diet with the intake of dietary supplements with plant components, intended for weight loss, on the reduction of body weight, fat mass, and glycemic profile of overweight women. The article presents the results of two groups of women (22 people in each) in a three-month physical fitness program to reduce excess weight, including regular exercise and adherence to dietary nutrition. Women of the first group additionally took an active substance for weight loss, containing a mixture of carnitine, chromium, arginine and guarana, green tea, citrus fruits and willow extract, women of the second group without placebo. It was found that participation in the program led to approximately the same reduction in body weight, BMI, total body fat, visceral fat and waist circumference in women of both groups. The use of a dietary supplement (fat burner) had a negative effect on glucose metabolism and insulin resistance. We attribute this result to the presence of caffeine and an especially inappropriate combination of substances in the mixture. The negative influence of a fat burner on carbohydrate metabolism revealed by us in the process of physical culture and health-improving fitness must be taken into account when organizing sports nutrition with the use of biologically active additives.

Setting up a methodology to distinguish between green oranges and leaves using hyperspectral imaging

The estimation of green citrus fruit yield is a key parameter for growers and the industry. The early estimation of orange yield at the immature green stage could influence the future market price and allow producers to plan the harvest in advance, thus reducing costs. This research can be considered as a preliminary step for designing low-cost spectral cameras capable of being mounted on unmanned aerial vehicles (UAVs) to estimate orange yield and defects. Images were acquired from oranges and leaves from an orchard in Jeju Island (Jeju, Republic of Korea), using two hyperspectral reflectance imaging systems, one working in the range 400–1000 nm (visible/near infrared, Vis/NIR) and the other between 900 and 2500 nm (short-wave infrared, SWIR). The main objective of the research was to set up a methodology to select the relevant bands - from the two spectral ranges studied - to distinguish between green oranges and leaves and to detect defects, which will allow citrus yield to be estimated. Analysis of variance (ANOVA) and principal component analysis (PCA) were used to select the key wavelengths for this purpose; next, a band ratio coupled with a simple thresholding method was applied. This study showed that the Vis/NIR hyperspectral imaging correctly classified 96.97% and 92.93% of the pixels, respectively, to distinguish between green oranges and leaves and to detect defects, while with the SWIR system, the percentage of pixels correctly classified for these two objectives were 74.79% and 89.31%, respectively. These results confirm that it is possible to use a low number of wavelengths to estimate harvest yield in oranges, which could pave the way for the future development of low-cost and low-weight equipment for the detection of green and sound fruit.

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.

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 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.

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

Classification of imazalil resistance in an international collection of Penicillium digitatum isolates

A total of 213 Penicillium digitatum isolates with resistance to the fungicide imazalil (IMZ) originating from green moulded citrus fruit from several countries (South Africa, USA, Uruguay, Spain, Israel, Cyprus, Chile, Australia and Argentina) were tested for their Cyp51 IMZ-resistance group (R1 to R3) using a published multiplex PCR assay. As well, the level of resistance (measured as EC50 values) was determined. Isolates from the USA showed the highest resistance group diversity (isolates belonged to R1, R2 and R3 groups) and lowest EC50 values. The isolates from the other countries were all classified into the R3 group, except for Chile, which also contained R1 isolates. There was no correlation between the level of resistance of isolates and the resistance group genotype. In general, IMZ resistance was most commonly associated (88.3%) with the R3 group. This study is the first to investigate the distribution of IMZ resistance genotypes in P. digitatum populations from a broad international scale.

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.

Efficacy and safety of Citrus sudachi peel in obese adults: A randomized, double-blind, pilot study

Objective: This study was undertaken to explore the efficacy and safety of Citrus sudachi peel for metabolic risk factors in obese male and female adults.Background: Citrus sudachi Hort. ex Shirai (Rutaceae), called “sudachi”, is a small, round, green citrus fruit that is mainly cultivated in Tokushima Prefecture in Japan. Our group reported that Citrus sudachi peel powder improved glucose tolerance and dyslipidemia in Zucher-fatty rats and reduced hyperglycemia and hypertriglyceridemia in GK diabetic rats.Materials and Methods: We conducted a randomized, double-blind, placebo-controlled trial in 40 participants with abdominal obesity and metabolic risk factors including hypertension, impaired glucose tolerance and elevated triglyceride levels. Participants were randomized to receive either tablets that contained 1.3 g dried Citrus sudachi peel powder or placebo tablets for 12 weeks. The sudachi peel group included 14 males and 5 females with a mean age of 54.5 years, and the placebo group included 18 males and 2 females with a mean age of 51.9 years. Results: Physical status including body weight, waist circumference and blood pressure and laboratory markers including metabolic parameters were not different at any observation point between the two groups. However, among participants with serum triglyceride levels of more than 120 mg/dl, body weight, waist circumference and serum triglyceride levels were significantly decreased at several observation points after the start of treatment in the sudachi peel group but not in the placebo group. No serious adverse events were observed in the sudachi peel group. Conclusions: Citrus sudachi peel has the potential effect to safely improve abdominal obesity and lower serum levels of TG in obese individuals with hypertriglyceridemia. A large-scale randomized, double-blind clinical study targeting subjects with both abdominal obesity and high TG levels is needed to confirm the metabolic effects of Citrus sudachi peel.Trial registration: UMIN Clinical Trials Registry (UMIN-CTR) UMIN000002682. Accession number of the Ethics Committee for Clinical Trials of Food in Tokushima University Hospital is F5.Key words: health functional food, anti-obesity, triglyceride

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.

Green citrus detection using ‘eigenfruit’, color and circular Gabor texture features under natural outdoor conditions

A machine vision algorithm was developed to detect and count immature green citrus fruits in natural canopies using color images. A total of 96 images were acquired in October 2010 from an experimental citrus grove in the University of Florida, Gainesville, Florida. Thirty-two of the total 96 images were selected randomly and used for training the algorithm, and 64 images were used for validation. Color, circular Gabor texture analysis and a novel ‘eigenfruit’ approach (inspired by the ‘eigenface’ face detection and recognition method) were used for green citrus detection. A shifting sub-window at three different scales was used to scan the entire image for finding the green fruits. Each sub-window was classified three times by eigenfruit approach using intensity component, eigenfruit approach using saturation component, and circular Gabor texture. Majority voting was performed to determine the results of the sub-window classifiers. Blob analysis was performed to merge multiple detections for the same fruit. For the validation set, 75.3% of the actual fruits were successfully detected using the proposed algorithm.

Vegetable and fruit intake and non-Hodgkin lymphoma survival in Connecticut women

We investigated whether an increased intake of vegetables and fruits favors NHL survival. A cohort of 568 female cases of incident NHL diagnosed during 1996–2000 in Connecticut was followed up for a median of 7.7 years. Adjusted hazard ratios (HRs) were estimated by Cox proportional hazard models. Our results show that a pre-diagnostic high intake of vegetables appeared to favor overall survival (HR = 0.74, 95% CI 0.57–0.98) among patients with NHL who survived longer than 6 months. In particular, pre-diagnostic high intakes of green leafy vegetables and citrus fruits were associated with 29% (95% CI 0.51–0.98) and 27% (95% CI 0.54–0.99) reduced risk of death, respectively. When different types of vegetables and fruits were investigated separately, their impacts were found to vary in NHL subtypes. Our study suggests that increasing vegetable and citrus fruit consumption could be a useful strategy to improve survival in NHL patients.