FRUIT CLASSIFIER BASED ON MACHINE LEARNING ANALYSIS

There are variety of fruits around the world, different types of fruits contain different types of nutrients and vitamins which could benefits our health. In order to understand which fruit can provide specific type of nutrients, we need to identify the types of fruits. However, fruits grow in a different shape, colour and texture based on the country they were planted and the environment of the land. Implementing a machine vision-based recognition on the fruits can help people recognize them easily. In this paper, an edge detection method is applied using computer vision approach to recognize different types of fruits. The fruits are classified based on the features extracted from their images. In the experiment, a total of 450 images of three types of fruit are used, which are apples, lemons and mangoes. Pre-processing steps are applied on the captured image to improve the quality of fruit details and the edge features are extracted using Canny Edge Detection method. Classification of the fruits is accomplished using two different types of learning model, the deep leaning model, Convolution Neural Network (CNN) and machine learning model, Support Vector Machines (SVM). The performance of both classifiers is compared and the model with the best performance, SVM is chosen as the model for the system. The system can achieve 86% classification accuracy with the SVM model, which is good enough for fruit recognition.

Multiple studies investigating the relationship between intake of different types of fruit and colorectal cancer (CRC) risk have yielded inconsistent results. To perform a meta-analysis of existing studies to assess the association between the intake of different kinds of fruit and the incidence of CRC. We searched online literature databases including PubMed, Embase, WOS, and Cochrane Library for relevant articles available up to August 2022. With data extracted from observational studies, odds ratios (ORs) with 95% confidence intervals (CIs) were assessed using random-effects models. A funnel plot and Egger's test were used to determine publication bias. Furthermore, subgroup analysis and dose-response analysis were performed. All analyses were conducted using R (version 4.1.3). Twenty-four eligible studies involving 1068158 participants were included in this review. The meta-analysis showed that compared to a low intake, a higher intake of citrus, apples, watermelon, and kiwi reduced the risk of CRC by 9% [OR (95%CI) = 0.91 (0.85-0.97)], 25% [OR (95%CI) = 0.75 (0.66-0.85)], 26% [OR (95%CI) = 0.74 (0.58-0.94)], 13% [OR (95%CI) = 0.87 (0.78-0.96)], respectively. No significant association was observed between the intake of other types of fruit and the risk of CRC. In the dose-response analysis, a nonlinear association was found [R (95%CI) = -0.0031 (-0.0047 to -0.0014)] between citrus intake and CRC risk (P < 0.001), with the risk minimized around 120 g/d (OR = 0.85), while no significant dose-response correlation was observed after continued increase in intake. We found that a higher intake of citrus, apples, watermelon, and kiwi was negatively associated with the risk of CRC, while the intake of other types of fruits were not significantly associated with CRC. Citrus intake showed a non-linear dose-response relationship with the risk of CRC. This meta-analysis provides further evidence that a higher intake of specific types of fruit is effective in preventing the occurrence of CRC.

In an effort to increase the number of sales, the process of classifying the type of Gerga citrus fruit is very necessary. The problem that often occurs is the mixing of various types of fruit from the storage warehouse so that the quality of the fruit will be mixed and it will be difficult to determine the selling price because the quality of the fruit itself is not evenly distributed so that a sorting process is needed. There are still many sellers or growers of citrus fruits who sort the quality of the fruit manually so that it can take a very long time. Given these problems, it is necessary to classify the quality of Gerga oranges automatically with the Naïve Bayes Classifier algorithm with GLCM feature extraction and HSV color characteristics. as a method for classifying the quality of Gerga citrus fruit and as for the media used, there is digital image media. From the experiments that have been carried out the use of angles in the formation of co-occurrence matrices with the best accuracy values reaching 80% are found at angles of 0°, 45°, and 135°, while the lowest accuracy values are found at angles of 90°. It was concluded that the Gerga citrus fruit quality classification system using the Naïve Bayes method was categorized as good with an AUC value of 0.8.

Quality inspection of fruits and vegetables linked to food safety monitoring and quality control. Traditional chemical analysis and physical measurement techniques are reliable, they are also time-consuming, costly, and susceptible to environmental and sample changes. Hyperspectral imaging technology combined with deep learning methods can effectively overcome these problems. Compared with human evaluation, automated inspection improves inspection efficiency, reduces subjective error, and promotes the intelligent and precise fruit and vegetable quality inspection. This paper reviews reports on the application of hyperspectral imaging technology combined to deep learning methods in various aspects of fruits and vegetables quality assessment. In addition, the latest applications of these technologies in the fields of fruit and vegetable safety, internal quality, and external quality inspection are reviewed, and the challenges and future development directions of hyperspectral imaging technology combined with deep learning in this field are prospected. Hyperspectral imaging combined with deep learning has shown significant advantages in fruit and vegetable quality inspection, especially in improving inspection accuracy and efficiency. Future research should focus on reducing costs, optimizing equipment, personalizing feature extraction, and model generalizability. In addition, the development of lightweight models and the balance of accuracy, the enhancement of the database and the importance of quantitative research should also be brought to attention. These efforts will promote the wide application of hyperspectral imaging technology in fruit and vegetable inspection, improve its practicability in the actual production environment, and bring important progress for food safety and quality management.

A strategy for healthy eating habits of daily fruits revisited: A metabolomics study

Cassava Mosaic Disease (CMD) is a major trouble to cassava crops, causing significant losses in agrarian product worldwide. Beforehand and accurate discovery of CMD is pivotal to alleviate its impact. Traditional styles of complaint discovery, similar as homemade examination and lab- grounded diagnostics, are time- consuming, precious, and frequently unreliable In this exploration, we propose an innovative approach to CMD discovery using a Deep intermittent Neural Network (DRNN) fashion, using image data from cassava shops. The DRNN model is designed to dissect factory images and descry symptoms of CMD with high perfection, offering a more effective and scalable result than conventional styles. The primary ideal of this study is to enhance the discovery delicacy of CMD using deep literacy ways, specifically DRNN, which combines the power of convolutional neural networks (CNN) with intermittent layers for bettered point birth and pattern recognition in factory images. We use a large data set of cassava factory images, including both healthy shops and those affected by CMD, to train and validate the model. Data addition ways were applied to ameliorate the model’s conception and robustness. Our results show that the DRNN model achieves a discovery delicacy of over 90, outperforming traditional styles and furnishing real- time individual capabilities. This approach not only improves the effectiveness of complaint discovery but also empowers growers with a cost-effective tool for covering factory health. The proposed system can be further expanded to descry other factory conditions, offering a protean result for agrarian health operation. This exploration contributes to the field of agrarian technology by demonstrating the eventuality of DRNNs in factory complaint discovery and offers a promising direction for unborn advancements in automated crop operation systems. Highlights Preface of DRNN The paper explores the operation of Deep intermittent Neural Networks (DRNN) for effective complaint discovery in cassava shops. ⮚ CMD Impact Emphasizes the significant profitable and agrarian impact of Cassava Mosaic Disease (CMD) on cassava product encyclopaedically. ⮚ Early Discovery significance Highlights the need for beforehand, accurate discovery of CMD to alleviate crop losses and ameliorate yield. ⮚ Deep literacy operation Demonstrates how deep literacy models, especially DRNN, can be used to enhance factory complaint discovery from images. ⮚ Data Augmentation Utilizes data addition ways to ameliorate the conception capability of the DRNN model. ⮚ Cassava Image Dataset The study uses a large and different dataset of cassava factory images, including both healthy and CMD affected shops. ⮚ Model Architecture Details the DRNN armature, combining convolutional and intermittent layers to effectively capture temporal and spatial features in factory images. ⮚ Delicacy Achievement The proposed DRNN model achieves over 90 delicacies in detecting CMD, outperforming traditional styles. ⮚ Relative Analysis The paper compares the DRNN model’s performance with other common deep literacy models, demonstrating its superior performance. ⮚ Real- Time opinion DRNN provides real- time individual capabilities for CMD discovery, offering immediate perceptivity for growers. ⮚ Cost- Effectiveness The exploration shows that the proposed system is cost-effective, making it accessible for use in small- scale and large- scale husbandry. ⮚ Scalable result the system can be fluently gauged to descry other factory conditions, furnishing a protean tool for husbandry. ⮚ Model Robustness The model’s robustness is bettered by applying colourful data preprocessing ways similar as normalization and image resizing. ⮚ Impact on Agricultural Practices The study highlights how automated CMD discovery can transfigure agrarian practices and decision-timber. ⮚ Unborn exploration Directions The paper suggests farther exploration into integrating DRNN with other AI ways for indeed more accurate complaint discovery and vaticination.

Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate and curate microscopy image (such as scanning tunneling microscopy and scanning transmission electron microscopy) data sets and apply a variety of machine learning techniques. To demonstrate the applicability of this library, we (1) establish an atomistic image data set of about 10 000 materials with large structural and chemical diversity, (2) develop and compare convolutional and atomistic line graph neural network models to classify the Bravais lattices, (3) demonstrate the application of fully convolutional neural networks using U-Net architecture to pixelwise classify atom versus background, (4) use a generative adversarial network for super resolution, (5) curate an image data set on the basis of natural language processing using an open-access arXiv data set, and (6) integrate the computational framework with experimental microscopy images for Rh, Fe3O4, and SnS systems. The AtomVision library is available at https://github.com/usnistgov/atomvision.

Based on Global Cancer 2015 statistics, the lung cancer of all types constitutes 27% of overall cancers while 19.5% of cancer deaths are due to lung cancer. In lieu of this, an effective lung cancer screening test using Computed Tomography (CT) scan is crucial to detect cancer at the early stage. The interpretation of the CT images requires an advanced CAD system of high accuracy for instance, in classifying the lung nodules. Recently, Deep Learning method that is Convolution Neural Network (CNN) shows an outstanding success in lung nodules classification. However, the training of CNN requires a great number of images. Such a requirement is an issue in the case of medical images. Generative adversarial network (GAN) has been introduced to generate new image datasets for CNN training. Thus, the main objective of this study is to compare the performance of CNN architectures with and without the implementation of GAN for lung nodules classification in CT images. Here, the study used Conditional GAN (cGAN) to generate benign nodules images. The classification accuracy of the combined cGAN-CNN architecture was compared among CNN pretraining networks namely GoogleNet, ShuffleNet, DenseNet, and MobileNet based on classification accuracy, specificity, sensitivity, and AUC-ROC values. The experiment was tested on LIDC-IDRI database. The results showed cGAN-CNN architecture improves the overall classification accuracy as compared to CNN alone with the cGAN-ShuffleNet architecture performed the best, achieving 98.38% accuracy, 98.13% specificity, 100% sensitivity and AUC-ROC at 99.90%. Overall, the classification performance of CNN can be improved by integrating GAN architecture to mitigate the constraint of having a large medical image dataset, in this case, CT lung nodules images.

In recent years, the number of diseases caused by fungal pathogens has increased significantly. Many species of fungi are pathogenic for plants, causing a threat to food production and to humans, and are among the causes of chronic diseases. The aim of the study is to determine the enzyme profiles of fungi, depending on the different types of fruit with which they have contact, and to determine the differences in these profiles in relation to the substrate on which they are grown. Six strains of fungi identified as Cladosporium sphaerospermum, Fusarium poae, Alternaria alternata, Penicillium expansum, Penicillium verucosum and Acremonium strictum, isolated from fruits, were selected and analyzed for enzymatic profiles. The enzymatic activity was assessed using the API ZYM test (bioMerieux, France). In the majority of the 6 fungal strains isolated from fruits, enzymes belonging to glycol-hydrolases were the most active. The exception was Acremonium strictum, where phosphatases dominated. Among most fungal isolates, the enzymes β- glucosidase and N-acetyl-β-glucosaminidase showed the highest activity. The highest β-glucosidase activities were found in Cladosporium sphaerospermum and Penicillium expansum. On the other hand, lipase, α-fucosidase and α-chymotrypsin showed the least activity. The least activity of these enzymes or their complete absence was observed in Fusarium poae, Alternaria alternata, Penicillium expansum and Acremonium strictum. The activity of hydrolytic enzymes in the isolated fungi depended on the addition of fruit and the type of medium. Individual fruits can increase or decrease the activity of the enzymes. Fungi present in fruit have pathogenic properties and can be possible risk factors for fungal infections.

First report on organochlorine pesticides in water in a highly productive agro-industrial basin of the Central Valley, Chile

Fruit is a crucial component of daily diets, emphasizing the importance of ensuring its freshness. Our proposed method utilizes YOLOv8 and SE attention mechanism for detecting the freshness of fruits. Our method balances accuracy and real-time performance while detecting the freshness of different types of fruits. The proposed method achieves an average accuracy of 87.8% and a maximum accuracy of 95.0% for detecting the freshness level of a single fruit category. Experimental results confirm the effectiveness of our proposed method in accurately detecting and localizing the freshness level of fruits, demonstrating its potential for wide application and promotion in the fruit industry.

Health as a form of human capital is crucial in the production of market and non-market commodities. It is, therefore, imperative that individuals/households invest in health as it directly influences their capacity to be productive and generate wealth. Investment in health is achieved through use of market and non-market goods and individuals own time as inputs. This study aimed at establishing the ways through which households/individuals produce their health; effect of health knowledge on health and providing policy measures to improve the health of households. A 2SLS translog health production function was employed using primary data obtained from a sample of households in Kangundo and Matungulu Sub-Counties. The study found that, the residents had normal weight according to World Health Organization classification and hence good health status, though in every 100 residents three were obese, 31 overweight, and nine were underweight. According to the study, health status improved with the frequency of fruits and vegetables consumed by individuals/households. Moreover, moderate exercise was found to be good for improved health status though excessive exercising had deleterious effect on health capital. Health knowledge was negatively related to health status, whereas it was positively related with the quadratic term for health knowledge. The study recommends that households be encouraged to increase the intake of fruits and vegetables. This could be achieved by encouraging small-scale farmers to practice horticulture, by intercropping different types of fruits and green leafy vegetables. Further, households need to be empowered with more health knowledge, by integrating health education in school curriculum from early childhood to university levels, though; this should not be tested in the final national examinations. It would be appropriate to deploy couples in the same areas, not only to reduce HIV/AIDS incidence among married people, but also improve their health status as established by the study.

Can eating five fruit and veg a day really keep the doctor away?

Fruits as the vehicle of drug resistant pathogenic yeasts

This paper presents a comprehensive investigation into the application of an electromagnetic microwave technique combined with the dielectric anomaly approach algorithm for the non-destructive inspection of fruits. The proposed antenna configuration comprises eight elements arranged in a circular array, enabling the collection of signals scattered by objects, specifically fruits, placed in their path. The collected data undergoes a series of processing steps, including Fast Fourier Transform, covariance matrix estimation, eigenvalue and eigenvector computation, and spatial spectrum construction. The dielectric anomaly algorithm is then applied to detect defects in the fruits. The study covers five different types of fruits, both healthy and defective, and gathers essential dielectric properties. Furthermore, a novel hybrid IQR method is introduced for outlier detection in the dielectric data. The results demonstrate the effectiveness of the proposed methodology in providing valuable insights into the internal structures of fruits and detecting anomalies, contributing to the enhancement of quality assessment in fruit inspection processes.