Fruit Images Research Articles

Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network

The palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architecture, has succeeded in image classification tasks and offers a promising solution. This study explores the implementation of AlexNet to improve the efficiency and accuracy of palm oil fruit maturity classification, thereby reducing costs and production time. We employed a dataset of 1500 images of palm oil fruits, meticulously categorized into three classes: raw, ripe, and rotten. The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. Furthermore, AlexNet achieved superior precision, recall, and F-1 scores, each reaching 0.99, while the CNN scores were 0.98. These findings suggest that adopting AlexNet can enhance the palm oil industry's operational efficiency and product quality. The improved classification accuracy ensures that fruits are harvested at optimal ripeness, leading to better oil yield and quality. Reducing classification errors and manual labor can also lead to substantial cost savings and increased profitability. This study underscores the potential of advanced deep learning models like AlexNet in revolutionizing agricultural practices and improving industrial outcomes.

Classification of Fruits using Image Processing and Deep Neural Networks

Fruits have shown a crucial role in our daily life. Fruits provide us rich resources for our food, pharmacy and industrial materials. The automatic classification of fruits using artificial intelligence and image processing has attracted a large number of researches. However, there exists a large number of species of fruits, the accurate classification of fruits is a challenging task. The paper presents a method for the classification of fruits using the Alexnet, Resnet-50 and MobileNetV3. We collect and normalize various fruit images for the training and testing the deep learning models. The accuracy of 90% and 92% the methods using Alexnet and Resnet-50 on various dataset has shown the potential application of the method in reality. Moreover, the application of the MobileNetV3 improves the execution time of the classification of fruit images.

Ultrahigh-field animal MRI system with advanced technological update

Animal magnetic resonance imaging (MRI) systems typically deliver superior imaging performance over conventional human MRI systems, making them a prevailing instrument in preclinical research. It is challenging to achieve the high performance of these animal MRI systems, due to the multifaceted nature of the various system components and the complexity of integration debugging. This work described the design, fabrication, measurement and integration of a 7 T animal MRI system, which exhibits several performance highlights. Both the magnet and gradient assembly adopted an ultra-shielding strategy, facilitating ease of system installation, maintenance and debugging. The main magnetic field exhibits acceptable homogeneity and stability, and the gradient coil is mechanically reliable thanks to zero-force control. The animal MRI system underwent debugging using proprietary imaging software to generate images of phantoms, fruits and organisms. Further research investigation will be performed to promote more scientific outputs with enhanced functional capabilities.

Efficient citrus fruit image classification via a hybrid hierarchical CNN and transfer learning framework

Efficient citrus fruit image classification via a hybrid hierarchical CNN and transfer learning framework

Autonomous Yield Estimation System for Small Commercial Orchards Using UAV and AI

In the context of precision horticulture, decision support tools play a significant role in providing fruit growers with insights into orchard conditions, facilitating informed decisions regarding orchard management practices. This study presents the development of an autonomous yield estimation system designed to provide decision support to small commercial orchards. Autonomous yield estimation is based on the application of UAVs and AI. AI is used to identify and quantify fruitlets and fruits in photographs collected by UAV. In this article, we present our prototype of an autonomous yield estimation system. The adapted “4+1” architecture was applied to design a system with a holistic approach analyzing software, hardware, and ecosystem requirements. Six datasets are presented, which contain the images of fruitlets and fruits of apples, pears, and cherries. Three CNN models were trained: YOLOv8m, YOLOv9m, and YOLOv10m. The experiment showed that the most accurate was YOLOv9m, which achieved mean accuracies of 0.896 mAP@50 and 0.510 mAP@50:95 for all datasets.

Performance Analysis of Various Deep Transfer Learning Models for Bacterial Blight Disease Detection and Classification in Pomegranate Fruits

Background: Detecting and classifying diseases in pomegranate fruit using computer vision remains a challenging task due to the presence of numerous diseases. Recent research findings suggest that models based on Convolutional Neural Networks (CNNs) have shown significant enhancements in accuracy when it comes to classifying images of fruits and leaves. The fungal infection responsible for the disease swiftly propagates through the soil, infiltrating the roots of pomegranate plants. Presently, the sole method to halt its spread entails farmers conducting thorough inspections and promptly eliminating infected plants, an uphill task. Methods: The present study introduces a classification PomeNetV1 model for identifying pomegranate fruit diseases employing CNN and transfer learning techniques. For executing this system, the dataset is created by taking the images directly from the farms in Ballari, Bengaluru, Bagalakote, etc. The proposed pomegranate fruit disease dataset contains 5099 images of five categories: Alternaria, Anthracnose, Bacterial Blight, Cercospora and Healthy. The system under consideration utilizes TensorFlow as its framework for developing deep learning models. Result: This paper evaluates ten different pre-trained models using a transfer learning approach and a proposed novel PomeNetV1 model for classification. All pre-trained models achieved over 99% classification accuracy. However, the PomeNetV1 model stands out with an impressive 99.80% accuracy, making it the most efficient for detecting healthy and bacterial blight diseases compared to others. Surpassing existing models like Vasumathi et al., 2021 deep CNN model with at least 1.63% accuracy, the PomeNetV1 model offers a feasible solution for detecting bacterial blight in pomegranate crops.

A deep learning approach to detect diseases in pomegranate fruits via hybrid optimal attention capsule network

In 2022, the production rate of pomegranate is estimated at approximately 4.8 million metric tons. Unfortunately, these fruits are susceptible to many different kinds of diseases caused by bacterial, viral, and fungal infections. Such diseases can have a major negative impact on fruit quality, production, and the profitability of pomegranate cultivation. Nowadays, several machine learning and deep learning methods are used to identify pomegranate fruit diseases automatically and effectively. In post-harvest pomegranate fruit disease detection, deep learning has great potential to extract complex patterns and features from large datasets. This can improve disease identification accuracy, enabling more efficient disease control, lower crop losses, and better resource management. The proposed work introduces an intelligent deep learning-based approach for accurately detecting pomegranate diseases, begins with Improved Guided Image Filtering (Improved GIF) and resizing to pre-process fruit images, followed by feature extraction (shape, color, texture) using GLCM and GLRLM to streamline classification. Extracted features are then fed into a novel Hybrid Optimal Attention Capsule Network (Hybrid OACapsNet), which classifies the images as normal or diseased, conditions such as bacterial blight, heart rot, and scab. Our analysis indicates that the proposed classifier has a classification accuracy of 99.19 %, precision of 98.45 %, recall of 98.41 %, F1-score of 98.43 %, and specificity of 99.45 % compared to other techniques. So this approach offers a framework, which is a feasible solution for automated detection of diseases in fruits, thereby benefiting farmers and supporting their farming operations.

Advancing mango leaf variant identification with a robust multi-layer perceptron model

Mango, often regarded as the “king of fruits,” holds a significant position in Bangladesh’s agricultural landscape due to its popularity among the general population. However, identifying different types of mangoes, especially from mango leaves, poses a challenge for most people. While some studies have focused on mango type identification using fruit images, limited work has been done on classifying mango types based on leaf images. Early identification of mango types through leaf analysis is crucial for taking proactive steps in the cultivation process. This research introduces a novel multi-layer perceptron model called WaveVisionNet, designed to address this challenge using mango leaf datasets collected from five regions in Bangladesh. The MangoFolioBD dataset, comprising 16,646 annotated high-resolution images of mango leaves, has been curated and augmented to enhance robustness in real-world conditions. To validate the model, WaveVisionNet is evaluated on both the publicly available dataset and the MangoFolioBD dataset, achieving accuracy rates of 96.11% and 95.21%, respectively, outperforming state-of-the-art models such as Vision Transformer and transfer learning models. The model effectively combines the strengths of lightweight Convolutional Neural Networks and noise-resistant techniques, allowing for accurate analysis of mango leaf images while minimizing the impact of noise and environmental factors. The application of the WaveVisionNet model for automated mango leaf identification offers significant benefits to farmers, agricultural experts, agri-tech companies, government agencies, and consumers by enabling precise diagnosis of plant health, enhancing agricultural practices, and ultimately improving crop yields and quality.

Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms

Manual selection of fresh fruit has been identified as a significant challenge for the agricultural sector due to its time-consuming nature and potential for inconsistent categorization. This process requires human labour to visually inspect and sort fruits, leading to variability and inefficiencies in the sorting process. This research proposes a low-cost alternative using intelligent fruit selection systems based on computer vision techniques to address these issues. These systems aim to automate the process of fruit selection, improving efficiency and consistency in categorizing fruits such as apples, bananas, and oranges. A critical step in developing such intelligent systems is the feature extraction process. Feature extraction is essential in classification, especially for data sources in the form of images. It involves identifying and isolating relevant information from the images that classification algorithms can use to distinguish between different fruit categories. If the feature extraction process fails to capture the correct information, the performance or accuracy of the classification algorithm will be negatively impacted. This research compares three different methods for extracting features from fruit images to determine which method yields the highest accuracy for fruit classification. The feature extraction methods evaluated were Grayscale Pixel Values, Mean Pixel Value of Channels, and Extracting Edge Features. The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. CNNs are well-suited for image classification tasks due to their ability to learn hierarchical feature representations from the input images automatically. By comparing the performance of the CNN classifier using the three different feature extraction methods, this research aims to identify the method that provides the highest level of accuracy in classifying fruit images. By conducting this comparative analysis, the research provides insights into the most effective feature extraction techniques for improving the performance of computer vision-based fruit selection systems, ultimately contributing to more efficient and consistent fruit categorization in the agricultural sector. The result revealed that the Grayscale achieved the highest validation accuracy (99.94%) and the lowest validation loss (0.44%).

Klasifikasi Tingkat Kemanisan Buah Kersen Berdasarkan Fitur Warna NTSC Menggunakan Jaringan Syaraf Tiruan Berbasis Pengolahan Citra Digital

<em><span lang="EN-US">The fruit of the calabura tree (Muntingia calabura) is a small red fruit originating from the Prunus genus, often found along roadsides. This fruit contains numerous nutrients beneficial for bodily health, serving as a highly potential source of nutrition. Presently, a challenge exists in determining the sweetness level of calabura fruit, relying heavily on manual human assessment. The development of classification utilizing technology is considered a crucial step. Previous research has concentrated on classifying various objects using RGB, HSV, YCbCr color feature extraction. However, it was observed that RGB, HSV, YCbCr color features are not universally suitable, particularly for calabura fruits. Hence, this study employs a method of classifying the sweetness level of calabura fruit based on NTSC color features using a Digital Image Processing-based Artificial Neural Network (ANN). This approach leverages color-based image processing features. The research involves several stages, starting from acquiring 300 calabura fruit images with 3 levels of classification to the classification process utilizing Backpropagation in the ANN. Multiple training and testing scenarios were conducted to select feature combinations with the highest accuracy and fastest computational time. Results revealed that the most effective feature used was the NTSC color feature as a skin characteristic parameter. Based on training outcomes using 210 training images, the accuracy reached 100% with a computational time of 1.66 seconds per image. Meanwhile, testing with 90 sample images showed an accuracy of 94% with a computational time of 4.23 seconds per image. Thus, it can be concluded that the employed method successfully classifies the quality of calabura fruit images based on color features and skin characteristics.</span></em>

Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars.

The paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were pictured and images segmented to extract the Red (R), Green (G), and Blue (B) mean pixel values which were re-arranged in 35 RGB-derived colorimetric indexes (CIs). After imaging, the samples were crushed and oil concentration was determined (NIR). The analysis of the correlation between oil and CIs revealed a differential hysteretic behavior depending on the covariates (CI and cultivar). The hysteresis area (Hyst) was then quantified and used to rank the CIs under the hypothesis that CIs with the maximum or minimum Hyst had the highest correlation coefficient and were the most suitable predictors within a general linear model. The results show that the predictors selected according to Hyst-based criteria had high accuracy as determined using a Global Performance Indicator (GPI) accounting for various performance metrics (R 2, RSME, MAE). The use of a general linear model here presented is a new computational option integrating current methods mostly based on artificial neural networks. RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the transition of the olive sector towards a digital agriculture domain.

Effect of packaging with versus without candy-oriented marketing themes on the appeal and sensory attributes of flavoured e-cigarettes

SignificanceE-cigarettes with candy-themed marketing are implicated in decisions to first try e-cigarettes but have unknown effects on the experience of vaping. We compared adults’ perceived appeal and sensory attributes after...

A Multi-Fruit Recognition Method for a Fruit-Harvesting Robot Using MSA-Net and Hough Transform Elliptical Detection Compensation

In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of crops they plant flexibly. However, the differences in size, shape, and color among different types of fruits make fruit recognition quite challenging. If each type of fruit requires a separate visual model, it becomes time-consuming and labor intensive to train and deploy these models, as well as increasing system complexity and maintenance costs. Therefore, developing a general visual model capable of recognizing multiple types of fruits has great application potential. Existing multi-fruit recognition methods mainly include traditional image processing techniques and deep learning models. Traditional methods perform poorly in dealing with complex backgrounds and diverse fruit morphologies, while current deep learning models may struggle to effectively capture and recognize targets of different scales. To address these challenges, this paper proposes a general fruit recognition model based on the Multi-Scale Attention Network (MSA-Net) and a Hough Transform localization compensation mechanism. By generating multi-scale feature maps through a multi-scale attention mechanism, the model enhances feature learning for fruits of different sizes. In addition, the Hough Transform ellipse detection compensation mechanism uses the shape features of fruits and combines them with MSA-Net recognition results to correct the initial positioning of spherical fruits and improve positioning accuracy. Experimental results show that the MSA-Net model achieves a precision of 97.56, a recall of 92.21, and an mAP@0.5 of 94.81 on a comprehensive dataset containing blueberries, lychees, strawberries, and tomatoes, demonstrating the ability to accurately recognize multiple types of fruits. Moreover, the introduction of the Hough Transform mechanism reduces the average localization error by 8.8 pixels and 3.5 pixels for fruit images at different distances, effectively improving the accuracy of fruit localization.

Apple fruits categorizing based on deep convolutional neural network techniques

<p>For a variety of reasons, including the high degree of similarity between varieties of the same type of fruit, the requirement to train the technique on a large amount of data, and the type and number of features suitable for application, the use of computer vision techniques in the classification of fruits still faces many challenges. Additionally, the technique's effectiveness and speed both need to be improved. Deep conventional neural network (DCNN) approaches were required for all of these reasons. A proposed CNN model is described in this work. The suggested methodology is intended to quickly and accurately categorize thirteen groups of apple fruits. The proposed technique was based on training and testing the model on a maximum number of images of apple fruits, by increasing the number of database images tenfold, after augmentation was performed on the images. The technology also relied on good tuning of the hyperparameters. To further ensure the efficiency of training, validation was performed on 20% of the database. All results that demonstrate the high efficiency of the proposed model were reviewed. The results of the proposal were compared with the results of four related techniques. The results showed the great advantage of the proposed technology at all levels.</p>

Genomic structure and marker-trait association for plant and fruit traits in Capsicum chinense and Capsicum baccatum germplasm

ObjectivesCapsicum baccatum and C. chinense are domesticated pepper species originating from Latin America recognized for their unique flavor and taste and widely diffused as spicy food for fresh uses or for processing. Owing to their capacity for adaptation to diverse habitats in tropical regions, these species serve as a valuable resource for agronomic traits and tolerance to both biotic and abiotic challenges in breeding projects. This study aims to dissect the genetic diversity of C. baccatum and C. chinense germplasm and to detect candidate genes underlying the variation of plant morphological and fruit size and shape traits. To that goal, SNP data from genotyping by sequencing have been used to investigate the genetic diversity and population structure of 103 accessions belonging to the two species. Further, plants have been assessed with main plant descriptors and fruit imaging analysis and association between markers and traits has been performed.ResultsThe population structure based on 29,820 SNPs revealed 4 subclusters separating C. chinense and C. baccatum individuals. A deeper analysis within each species highlighted three subpopulations in C. chinense and two in C. baccatum. Phenotypic characterization of 54 traits provided approximately 125 thousand datapoints highlighting main differences between species for flower and fruit traits rather than plant architecture. Marker-traits association, performed with the CMLM model, revealed a total of 6 robust SNPs responsible for change in flower traits and fruit shape. This is the first attempt for mapping morphological traits and fruit features in the two domesticated species, paving the way for further genomic assisted breeding.

Utilizing RT-DETR Model for Fruit Calorie Estimation from Digital Images

Estimating the calorie content of fruits is critical for weight management and maintaining overall health as well as aiding individuals in making informed dietary choices. Accurate knowledge of fruit calorie content assists in crafting personalized nutrition plans and preventing obesity and associated health issues. In this paper, we investigate the application of deep learning models for estimating the calorie content in fruits from digital images, aiming to provide a more efficient and accurate method for nutritional analysis. We create a dataset comprising images of various fruits and employ random data augmentation techniques during training to enhance model robustness. We utilize the RT-DETR model integrated into the ultralytics framework for implementation and conduct comparative experiments with YOLOv10 on the dataset. Our results show that the RT-DETR model achieved a precision rate of 99.01% and mAP50-95 of 94.45% in fruit detection from digital images, outperforming YOLOv10 in terms of F1- Confidence Curves, P-R curves, precision, and mAP. Conclusively, in this paper, we utilize a transformer architecture to detect fruits and estimate their calorie and nutritional content. The results of the experiments provide a technical reference for more accurately monitoring an individual’s dietary intake by estimating the calorie content of fruits.

DeepDate: A deep fusion model based on whale optimization and artificial neural network for Arabian date classification.

As agricultural technology continues to develop, the scale of planting and production of date fruit is increasing, which brings higher yields. However, the increasing yields also put a lot of pressure on the classification step afterward. Image recognition based on deep learning algorithms can help to identify and classify the date fruit species, even in natural light. In this paper, a deep fusion model based on whale optimization and an artificial neural network for Arabian date classification is proposed. The dataset used in this study includes five classes of date fruit images (Barhi, Khalas, Meneifi, Naboot Saif, Sullaj). The process of designing each model can be divided into three phases. The first phase is feature extraction. The second phase is feature selection. The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50). The experimental results show that, after trying different combinations of optimization algorithms and classifiers, the highest test accuracy achieved by DeepDate was 95.9%. It takes less time to achieve a balance between classification accuracy and time consumption. In addition, the performance of DeepDate is better than that of many deep transfer learning models such as Alexnet, Squeezenet, Googlenet, VGG-19, NasNet, and Inception-V3. The proposed DeepDate improves the accuracy and efficiency of classifying date fruits and achieves better results in classification metrics such as accuracy and F1. DeepDate provides a promising classification solution for date fruit classification with higher accuracy. To further advance the industry, it is recommended that stakeholders invest in technology transfer programs to bring advanced image recognition and AI tools to smaller producers, enhancing sustainability and productivity across the sector. Collaborations between agricultural technologists and growers could also foster more tailored solutions that address specific regional challenges in date fruit production.

Using Machine Learning to Identify Diseases and Perform Sorting in Apple Fruit

Fruit diseases play a major role in global agriculture, leading to substantial crop losses and influencing food production and economic stability. In this age of Industry 4.0 the fruit sorting is an important part in the food processing wherein this work plays a vital role. In this study, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. Deep learning models offer promise for automating disease identification using fruit images, but encounter obstacles such as therequirement for extensive training data, computational complexity, and the risk of overfitting. This study introduces an innovative convolutional neural network (CNN) architecture aimed at addressing these challenges by incorporating a reduced number of layers, thus alleviating computational burdens while maintaining performance. Additionally, augmentation techniques such as shift, shear, scaling, zoom, and flipping are employed to diversify the training set without additional image acquisition. Our CNN model is specifically trained to identify common apple crop diseases like Scab, Rot, and Blotch. Rigorous experimental evaluation demonstrates the effectiveness ofour model, achieving a remarkable classification accuracy of 95.37%. Significantly, our model demonstrates reduced storage requirements and faster execution times compared to existing deep CNN architectures, enabling deployment on handheld devices and resource-limited environments. While other CNN models may offer similar accuracy levels, our approach emphasizes efficiency and resource optimization, rendering it practical for real-world applications in agriculture. Furthermore, our CNN model exhibits resilience to environmental variations and imaging parameters, enhancing its applicability across diverse agricultural settings. By leveraging advanced machine learning techniques, the approach developed in this experimental work contributes to modernizing fruits and vegetables sorting operations in food processing, crop management practices thus promoting agricultural sustainability. The scalability and portability of our model make it suitable for deployment in both small-scale farms and large-scale agricultural operations.

Near-infrared optical properties of Cs2KScF6:Cr3+ phosphor for high-power light-emitting diodes

The recent years have witnessed a great leap for near-infrared (NIR) spectroscopy technology in multifunctional application fields. In this study, a double-perovskite structured Cr3+-doped Cs2KScF6 fluoride NIR phosphor with peak location at 778 nm and full-width at half maximum (FWHM) of 95 nm was synthesized by a co-precipitation method. The integral emission intensity at 423 K retains 70 % of that at room temperature (RT), showing good luminescent thermal stability. Combining the obtained phosphor in an NIR phosphor converted light-emitting diode (NIR pc-LED) device, the output power and photoelectric conversion efficiency can reach 72.28 mW and 6.83 % at 320 mA. By virtue of the good optical performance, clear structural images of different fruits were visualized by using the NIR pc-LED as light source, suggesting the potential of Cs2KScF6:Cr3+ phosphor to be used in high-power LED devices.

Associations Between Noticing E-Cigarette Advertising Features and E-Cigarette Appeal and Switching Interest Among Young Adult Dual Users

Background E-cigarette advertising, which often includes various features, may prompt e-cigarette use and product switching. This study examined the associations between noticing e-cigarette ad features and perceived product appeal and interest in completely switching from cigarettes to advertised e-cigarettes among young adult dual users of both products. Methods We analyzed data from an online heatmap experiment among young adult dual users defined as established cigarette smokers who currently used e-cigarettes (ages 18-34 years; n = 1,821). Participants viewed 12 e-cigarette ads, clicked on ad features (e.g., fruit flavors, nicotine warnings, price promotions, smoker-targeted claims) that attracted their attention (defined as “noticing”), and answered questions about e-cigarette product appeal and interest in completely switching from cigarettes to the e-cigarettes shown. We examined within-person associations between noticing specific ad features and outcomes, controlling for demographic and tobacco use-related characteristics. Results Noticing fruit flavors (AOR = 1.67 and 1.28) and fruit images (AOR = 1.53 and 1.21) was positively associated with having any e-cigarette product appeal and switching interest. Noticing price promotions (AOR = 1.23) was positively associated with product appeal. In contrast, noticing nicotine warnings (AOR = 0.74 and 0.86), smoker-targeted claims (AOR = 0.78 and 0.89), and tobacco flavors (AOR = 0.92 and 0.90) was negatively associated with product appeal and switching interest. Conclusions Noticing certain e-cigarette ad features (e.g., fruit flavors and nicotine warnings) may be associated with product appeal and/or switching interest among young adult dual users. More research is needed to assess the influence of e-cigarette ad features that promote product switching interests among cigarette smokers while discourage interests among tobacco-naïve individuals.