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Artykuły w czasopismach na temat „Fruits recognition”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Deepali, M. Bongulwar, and N. Talbar S. "Robust Convolutional Neural Network Model For Recognition of Fruits." Indian Journal of Science and Technology 14, no. 45 (2021): 3318–34. https://doi.org/10.17485/IJST/v14i45.1493.

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<strong>Objectives:</strong> To develop a model for the automatic recognition of fruits utilizing deep learning techniques. <strong>Methods:</strong> We have designed a fruit classification and recognition Model using Convolutional Neural Networks (CNN). We have used excellent quality ImageNet dataset of fruit images for evaluation purpose. It contains 9,130 images of 11 different categories. The classification is challenging as the images comprise different fruits of the same color and shape, overlapped fruits, the background is not homogenous, and with different light effects

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Tan, Sean Huey, Chee Kiang Lam, Kamarulzaman Kamarudin, et al. "Vision-Based Edge Detection System for Fruit Recognition." Journal of Physics: Conference Series 2107, no. 1 (2021): 012066. http://dx.doi.org/10.1088/1742-6596/2107/1/012066.

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Abstract 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

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Zhou, Yunhe, Yunchao Tang, Xiangjun Zou, et al. "Adaptive Active Positioning of Camellia oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm." Applied Sciences 12, no. 24 (2022): 12959. http://dx.doi.org/10.3390/app122412959.

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Camellia oleifera fruits are randomly distributed in an orchard, and the fruits are easily blocked or covered by leaves. In addition, the colors of leaves and fruits are alike, and flowers and fruits grow at the same time, presenting many ambiguities. The large shock force will cause flowers to fall and affect the yield. As a result, accurate positioning becomes a difficult problem for robot picking. Therefore, studying target recognition and localization of Camellia oleifera fruits in complex environments has many difficulties. In this paper, a fusion method of deep learning based on visual p

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Azida Muhammad, Nur, Amelina Ab Nasir, Zaidah Ibrahim, and Nurbaity Sabri. "Evaluation of CNN, Alexnet and GoogleNet for Fruit Recognition." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 468. http://dx.doi.org/10.11591/ijeecs.v12.i2.pp468-475.

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Fruit recognition is useful for automatic fruit harvesting. Fruit recognition application can reduce or minimize human intervention during fruit harvesting operation. However, in computer vision, fruit recognition is very challenging because of similar shapes, colors and textures among various fruits. Illuminations changes due to weather condition also leads to a challenging task for fruit recognition. Thus, this paper tends to investigate the performance of basic Convolutional Neural Network (CNN), Alexnet and Googlenet in recognizing nine different types of fruits from a publicly available d

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Nur, Azida Muhammad, Ab Nasir Amelina, Ibrahim Zaidah, and Sabri Nurbaity. "Evaluation of CNN, Alexnet and GoogleNet for Fruit Recognition." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 468–75. https://doi.org/10.11591/ijeecs.v12.i2.pp468-475.

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Fruit recognition is useful for automatic fruit harvesting. Fruit recognition application can reduce or minimize human intervention during fruit harvesting operation. However, in computer vision, fruit recognition is very challenging because of similar shapes, colors and textures among various fruits. Illuminations changes due to weather condition also lead to a challenging task for fruit recognition. Thus, this paper tends to investigate the performance of basic Convolutional Neural Network (CNN), Alexnet and Googlenet in recognizing nine different types of fruits from a publicly available da

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Behnam Israel, Nohadra, Adnan Ismail Al-Sulaifanie, and Ahmed Khorsheed Al-Sulaifanie. "A Recognition and Classification of Fruit Images Using Texture Feature Extraction and Machine Learning Algorithms." Academic Journal of Nawroz University 13, no. 1 (2024): 92–104. http://dx.doi.org/10.25007/ajnu.v13n1a1514.

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Fruits classification is demanded in some fields, such as industrial agriculture. Automatic fruit classification from their digital image plays a vital role in those fields. The classification encounters several challenges due to capturing fruits’ images from different viewing angle, rotation, and illumination pose. In this paper a framework for recognition and classification of fruits from their images have been proposed depending on texture features, the proposed system rely on three phases; firstly, pre-processing, as images need to be resized, filtered, color convert, and threshold in orde

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Li, Xiuhua, Xiang Wang, Pauline Ong, Zeren Yi, Lu Ding, and Chao Han. "Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream." Sensors 23, no. 20 (2023): 8444. http://dx.doi.org/10.3390/s23208444.

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Dragon fruit (Hylocereus undatus) is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient. Deep learning techniques have proven effective for object recognition tasks but limited research has been conducted on dragon fruit due to its unique stem morphology and the coexistence of flowers and fruits. Ad

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Wang, Shengxue, and Tianhong Luo. "A Multi-Fruit Recognition Method for a Fruit-Harvesting Robot Using MSA-Net and Hough Transform Elliptical Detection Compensation." Horticulturae 10, no. 10 (2024): 1024. http://dx.doi.org/10.3390/horticulturae10101024.

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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 a

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Salim, Farsana, Faisal Saeed, Shadi Basurra, Sultan Noman Qasem, and Tawfik Al-Hadhrami. "DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition." Electronics 12, no. 14 (2023): 3132. http://dx.doi.org/10.3390/electronics12143132.

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With the dramatic increase of the global population and with food insecurity increasing, it has become a major concern for both individuals and governments to fulfill the need for foods such as vegetables and fruits. Moreover, the desire for the consumption of healthy food, including fruit, has increased the need for applications in the field of agriculture that help to achieve better methods for fruit sorting and fruit disease prediction and classification. Automated fruit recognition is a potential solution to reduce the time and labor required to identify different fruits in situations such

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Izzani Kamal Ariffin, Nur, Mas Rina Mustaffa, Lili Nurliyana Abdullah, Nurul Amelina Nasharuddin, and . "Fruits Recognition based on Texture Features and K-Nearest Neighbor." International Journal of Engineering & Technology 7, no. 4.31 (2018): 452–58. http://dx.doi.org/10.14419/ijet.v7i4.31.23728.

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Malaysia is well-known for its variety of fruits available in the country such as pineapple, guava, durian, apple, and watermelon. Therefore, it is important for us to get to know more about fruits so that we can take advantage of all the benefits that each fruit can offer. However, problems may arise where a person may know nothing about a particular fruit apart from only having an image of it. Most of the fruit encyclopedias nowadays still rely on text as search input. Furthermore, various features are commonly utilised for representation which can lead to high computational complexity. Ther

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Bagus Hardika, Mahesa Dzikri Kurniawan, Muhammad Adzka, et al. "Penerapan Klasifikasi Gambar Buah dalam Aplikasi FruityLens Menggunakan Metode CNN." Jurnal Sistem Informasi dan Ilmu Komputer 2, no. 4 (2024): 132–42. http://dx.doi.org/10.59581/jusiik-widyakarya.v2i4.4275.

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This research develops a fruit classification system using Convolutional Neural Network (CNN) in the educational application FruityLens, which helps children recognize different types of fruits through image recognition. The application can identify four types of fruits: apple, banana, orange, and watermelon, utilizing an image dataset from open sources. The research methods include dataset collection, image pre-processing, CNN model training, and classification accuracy evaluation. The results indicate that the developed CNN model achieves high accuracy, supporting children's learning about f

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Lei, Xiangming, Mingliang Wu, Yajun Li, et al. "Detection and Positioning of Camellia oleifera Fruit Based on LBP Image Texture Matching and Binocular Stereo Vision." Agronomy 13, no. 8 (2023): 2153. http://dx.doi.org/10.3390/agronomy13082153.

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To achieve the rapid recognition and accurate picking of Camellia oleifera fruits, a binocular vision system composed of two industrial cameras was used to collect images of Camellia oleifera fruits in natural environments. The YOLOv7 convolutional neural network model was used for iterative training, and the optimal weight model was selected to recognize the images and obtain the anchor frame region of the Camellia oleifera fruits. The local binary pattern (LBP) maps of the anchor frame region were extracted and matched by using the normalized correlation coefficient template matching algorit

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13

Abdul Hamid, Nik Noor Akmal, Rabiatul Adawiya Razali, and Zaidah Ibrahim. "Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 333. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp333-339.

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This paper presents a comparative study between Bag of Features (BoF), Conventional Convolutional Neural Network (CNN) and Alexnet for fruit recognition. Automatic fruit recognition can minimize human intervention in their fruit harvesting operations, operation time and harvesting cost. On the other hand, this task is very challenging because of the similarities in shapes, colours and textures among various types of fruits. Thus, a robust technique that can produce good result is necessary. Due to the outstanding performance of deep learning like CNN and its pre-trained models like AlexNet in

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Yang, Yi, Lijun Su, Aying Zong, et al. "A New Kiwi Fruit Detection Algorithm Based on an Improved Lightweight Network." Agriculture 14, no. 10 (2024): 1823. http://dx.doi.org/10.3390/agriculture14101823.

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To address the challenges associated with kiwi fruit detection methods, such as low average accuracy, inaccurate recognition of fruits, and long recognition time, this study proposes a novel kiwi fruit recognition method based on an improved lightweight network S-YOLOv4-tiny detection algorithm. Firstly, the YOLOv4-tiny algorithm utilizes the CSPdarknet53-tiny network as a backbone feature extraction network, replacing the CSPdarknet53 network in the YOLOv4 algorithm to enhance the speed of kiwi fruit recognition. Additionally, a squeeze-and-excitation network has been incorporated into the S-

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15

Wu, Ruoyu. "Crop Recognition Method Based On End-effector." Highlights in Science, Engineering and Technology 97 (May 28, 2024): 237–43. http://dx.doi.org/10.54097/5cka6v19.

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With the increasing global population, the demand for agriculture is also on the rise. The crucial stages of agricultural production, namely fruit identification and picking, play a vital role in enhancing product quality and minimizing losses. Traditional manual processing methods, although time-tested, are not only inefficient but also challenging to maintain consistency, making them inadequate to meet the large-scale requirements of modern agricultural production. Consequently, the integration of automation technology has become a necessity. For agricultural robot the machine vision system

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Lin, Miaorun. "Fruit Classification based on ResNet and Attention Mechanism." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 163–67. http://dx.doi.org/10.54097/hset.v34i.5441.

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Numerous types of fruits are discovered, which can provide humans with nutrients and trace elements. There are 59 families and 694 species of fruits in China, of which more than 300 kinds of cultivated fruit trees are in abundance, including more than 10000 varieties. Therefore, the classification of fruit detection is very necessary. The traditional method of fruit classification mainly relies on manual methods, which has greatly reduced the cost effectiveness in recent years due to the increase of labor cost. This paper introduces the idea of transfer learning by comparing various automated

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Hussain, Dostdar, Israr Hussain, Muhammad Ismail, Amerah Alabrah, Syed Sajid Ullah, and Hayat Mansoor Alaghbari. "A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition." Computational Intelligence and Neuroscience 2022 (February 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/6538117.

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Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and background variations. Therefore, developing and exploring an expert system for automatic fruits’ recognition is getting more and more important after many successful approaches; however, this technology is still far from being mature. The deep learning-based models have emerged as state-of-the-art techniques for image segmentation and c

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Chakravarthy Malineni, Sai, Kaja Mytheen Basari Kodi, Jeevitha Sakkarai, Gomathinayagam Nallasivan, Mani Geetha, and Bhuvanesh Ananthan. "Enhancing fruit recognition with robotic automation and salp swarm optimization for random forest classification." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2120–30. https://doi.org/10.11591/eei.v14i3.8917.

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In response to the growing demand for automation and labor-saving solutions in agriculture, there has been a noticeable lack of advancements in mechanization and robotics specifically tailored for fruit cultivation. To address this gap, this work introduces a novel method for fruit recognition and automating the harvesting process using robotic arms. This work employs a highly efficient and accurate model utilizing a single shot multibox detector (SSD) for detecting the precise fruit position. Once the fruit's position is identified, the angles of the robot arm's joints are calculated using in

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Christian, Josua, and Said Iskandar Al Idrus. "Introduction to Citrus Fruit Ripens Using the Deep Learning Convolutional Neural Network (CNN) Learning Method." Asian Journal of Applied Education (AJAE) 2, no. 3 (2023): 459–70. http://dx.doi.org/10.55927/ajae.v2i3.5003.

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The export value of Indonesian fruits in 2023 will increase compared to 2021. For this reason, a program is needed to introduce fruit maturity, in this case, citrus fruits. Currently, the fruit maturity recognition system is still done manually which takes a long time and requires a lot of human resources. Thus, the purpose of this research is to use Machine Learning and the Convolution Neural Network (CNN) model in the classification of citrus fruit maturity. The computer image recognition method used is CNN, which has advantages in computer vision applications, face recognition, object detec

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Fan, Youchen, Shuya Zhang, Kai Feng, Kechang Qian, Yitong Wang, and Shangzhi Qin. "Strawberry Maturity Recognition Algorithm Combining Dark Channel Enhancement and YOLOv5." Sensors 22, no. 2 (2022): 419. http://dx.doi.org/10.3390/s22020419.

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Aiming at the problems of low accuracy of strawberry fruit picking and large rate of mispicking or missed picking, YOLOv5 combined with dark channel enhancement is proposed. In “Fengxiang” strawberry, the criterion of “bad fruit” is added to the conventional three criteria of ripeness, near-ripeness, and immaturity, because some of the bad fruits are close to the color of ripe fruits, but the fruits are small and dry. The training accuracy of the four kinds of strawberries with different ripeness is above 85%, and the testing accuracy is above 90%. Then, to meet the demand of all-day picking a

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Li, Xinning, Hu Wu, Xianhai Yang, Peng Xue, and Shuai Tan. "Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network." Journal of Robotics 2021 (July 7, 2021): 1–13. http://dx.doi.org/10.1155/2021/3584422.

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In order to better realize the orchard intelligent mechanization and reduce the labour intensity of workers, the study of intelligent fruit boxes handling robot is necessary. The first condition to realize intelligence is the fruit boxes recognition, which is the research content of this paper. The method of multiview two-dimensional (2D) recognition was adopted. A multiview dataset for fruits boxes was built. For the sake of the structure of the original image, the model of binary multiview 2D kernel principal component analysis network (BM2DKPCANet) was established to reduce the data redunda

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Kutyrev, Alexey Igorevich, and Igor Gennadievich Smirnov. "Neural network for apple fruit recognition and classification." Agrarian Scientific Journal, no. 8 (August 31, 2023): 123–33. http://dx.doi.org/10.28983/asj.y2023i8pp123-133.

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The article proposes a method for monitoring industrial gardens based on artificial intelligence and machine learning. To identify apple fruits on the crown of a tree using a robotic platform moving in the garden areas with a camera attached to it, a neural network was developed, the VGG-16 model and SSD architecture were used, which detect the output space and generate bounding rectangles in images with different aspect ratios. To count the number of fruits relative to each row of plantings, a method is proposed for stitching a series of photographs of fruit trees in a row into a cylindrical

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O. M.Salim, Nareen, and Ahmed Khorsheed Mohammed. "Fruit recognition using Statistical and Features extraction by PCA." Academic Journal of Nawroz University 12, no. 3 (2023): 566–74. http://dx.doi.org/10.25007/ajnu.v12n3a1687.

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Fruits are an integral part of human diet since they are a vital source of minerals, vitamins, fiber, and phytonutrients. Fruits are rich in potassium, fiber, and vitamin C yet low in fat, sodium, and calories. A diet high in fruit can help us avoid diseases including cancer, diabetes, heart disease, and others. Without professional dietitian guidance, a method that quickly reveals how many calories or fruit they are consuming can be helpful in maintaining health. The use of image processing methods is expanding across all academic fields, including food science and agriculture. The identifica

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Baryła, Mateusz. "What is this fruit? Neural network application for Vietnamese fruit recognition." ITM Web of Conferences 20 (2018): 02009. http://dx.doi.org/10.1051/itmconf/20182002009.

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We use deep learning for problems in computer vision, image recognition and classification. Deep learning methods for fruit recognition are built with methods where features (in our case fruits key features) are processed and sent through multiple layers where transformations and computations are done sequentially to form a prediction model. Deep learning algorithms draws inspiration from many fields especially applied maths fundamentals like linear algebra, probability, information theory and numerical optimization. To the best of our knowledge this is the first web application for fruit reco

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Li, Hongli. "A Visual Recognition and Path Planning Method for Intelligent Fruit-Picking Robots." Scientific Programming 2022 (April 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/1297274.

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With the rapid development of economy and the increasing improvement of agricultural production level, people’s demand for fruits is also increasing year by year. China is the largest fruit production and consumption country in the world. According to relevant statistics reported for China, by the end of 2019, the total amount of various fruits sold had reached about 270 million tons, with apples accounting for 48% of the global output and pears accounting for 69% of the national total output. However, China’s fruit picking is still dominated by manual picking process, which takes a lot of man

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Abdel-raouf, Amal, Alaa Sheta, AbdelKarim Baareh, and Peter Rausch. "Barcode-less Fruits Classification Using Deep Learning." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3211. http://dx.doi.org/10.11591/ijai.v13.i3.pp3211-3217.

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<p>Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advan

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Alaa, Sheta, Karim Baareh Abdel, Abdel-Raouf Amal, and Rausch Peter. "Barcode-less fruits classification using deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3211–17. https://doi.org/10.11591/ijai.v13.i3.pp3211-3217.

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Barcode-less fruit recognition technology has revolutionized checkout by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcodes to fruits requires adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced

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Narwal, Pulkit, and Ipsita Pattnaik. "Smart Fruit Basket." International Journal of Creative Interfaces and Computer Graphics 13, no. 1 (2022): 1–14. http://dx.doi.org/10.4018/ijcicg.311427.

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This paper discusses smart retailing solutions, self-checkout stores in particular. Since RFID tag-based product identification accounts for various limitations, the authors propose a smart basket to facilitate self-checkout mechanism for fruits and vegetables, based on multi-view image recognition and weight sensor. The system works on a multi-view model and recognizes and counts the fruit/vegetables from four camera views to handle the occlusions. The user places fruits inside the basket. Multiple cameras installed provide different views inside the basket and captures this fruit placing act

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Bongulwar, Deepali M., and S. N. Talbar. "Robust Convolutional Neural Network Model For Recognition of Fruits." Indian Journal of Science and Technology 14, no. 45 (2021): 3318–34. http://dx.doi.org/10.17485/ijst/v14i45.1493.

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Kazakevich, P. P., A. N. Yurin, and G. А. Prokopovich. "Technical vision system for apple defects recognition: justification, development, testing." Proceedings of the National Academy of Sciences of Belarus. Agrarian Series 59, no. 4 (2021): 488–500. http://dx.doi.org/10.29235/1817-7204-2021-59-4-488-500.

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The most rational method for identifying the quality of fruits is the optical method using PPE, which has the accuracy and stability of measurement, as well as distance and high productivity. The paper presents classification of fruit quality recognition systems and substantiates the design and technological scheme of the vision system for sorting them, consisting of an optical module with installed structural illumination and a video camera, an electronic control unit with an interface and actuators for the sorter and conveyor for fruits. In the course of the study, a single-stream type of fr

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de Castro, Francisco, and Angelin Gladston. "FPN-Based Small Orange Fruit Detection From Farm Images With Occlusion." International Journal of Knowledge-Based Organizations 12, no. 1 (2022): 1–12. http://dx.doi.org/10.4018/ijkbo.296394.

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Fruit detection using deep learning is yielding very good performance, the goal of this work is to detect small fruits in images under these occlusion and overlapping conditions. The overlap among fruits and their occlusion can lead to false and missing detection, which decreases the accuracy and generalization ability of the model. Therefore, a small orange fruit recognition method based on improved Feature Pyramid Network was developed. To begin with, multi-scale feature fusion was used to fuse the detailed bottom features and high-level semantic features to detect small-sized orange to impr

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Lin, Yuhan, Wenxin Hu, Zhenhui Zheng, and Juntao Xiong. "Citrus Identification and Counting Algorithm Based on Improved YOLOv5s and DeepSort." Agronomy 13, no. 7 (2023): 1674. http://dx.doi.org/10.3390/agronomy13071674.

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A method for counting the number of citrus fruits based on the improved YOLOv5s algorithm combined with the DeepSort tracking algorithm is proposed to address the problem of the low accuracy of counting citrus fruits due to shading and lighting factors in videos taken in orchards. In order to improve the recognition of citrus fruits, the attention module CBAM is fused with the backbone part of the YOLOv5s network, and the Contextual Transformer self-attention module is incorporated into the backbone network; meanwhile, SIoU is used as the new loss function instead of GIoU to further improve th

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Lincy, Vincent Rakesh V. S. "DETECTION AND ANALYSIS OF ANTHRACNOSE DISEASE USING C++ PROGRAMMING WITH OPENCV LIBRARIES AND MATLAB." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [AIVESC-18] (April 26, 2018): 12–16. https://doi.org/10.5281/zenodo.1230356.

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— Physical recognition of defected fruits is very difficult. These days, the existing system has the drawback of low speed, low efficiency, high cost and complexity. The identification of diseases on fruits is the major factor for reduces the diseases on fruits and thereby increasing the productivity. The symptoms can be observed as spots or lesions on fruits and Its effect will diminish the quantity and quality of fruit, as it reduces the photosynthesis process. The system uses openCV to monitor the diseases on fruits and the steps for the resulting system are image acquisition, Image p

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Gatica, Gabriel, Stanley Best, José Ceroni, and Gastón Lefranc. "Olive Fruits Recognition Using Neural Networks." Procedia Computer Science 17 (2013): 412–19. http://dx.doi.org/10.1016/j.procs.2013.05.053.

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Xiao, Xu, Yaonan Wang, Bing Zhou, and Yiming Jiang. "Flexible Hand Claw Picking Method for Citrus-Picking Robot Based on Target Fruit Recognition." Agriculture 14, no. 8 (2024): 1227. http://dx.doi.org/10.3390/agriculture14081227.

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In order to meet the demand of the intelligent and efficient picking of fresh citrus fruit in a natural environment, a flexible and independent picking method of fresh citrus fruit based on picking pattern recognition was proposed. The convolutional attention (CA) mechanism was added in the YOLOv7 network model. This makes the model pay more attention to the citrus fruit region, reduces the interference of some redundant information in the background and feature maps, effectively improves the recognition accuracy of the YOLOv7 network model, and reduces the detection error of the hand region.

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Qiao, Jianlei, Guoqiang Su, Chang Liu, et al. "Study on the Application of Electronic Nose Technology in the Detection for the Artificial Ripening of Crab Apples." Horticulturae 8, no. 5 (2022): 386. http://dx.doi.org/10.3390/horticulturae8050386.

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Ripening agents can accelerate the ripening of fruits and maintain a similar appearance to naturally ripe fruits, but the fruit flavor and quality will be changed compared to naturally ripe fruits. To find an efficient detection method to distinguish whether crab apples were artificial ripened, the naturally ripe and artificially ripe fruits were detected and analyzed using the electronic nose (e-nose) technique in this study. The fruit quality indexes of samples were determined by the traditional method as a reference. Significant differences were found between naturally ripe and artificially

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Mureşan, Horea, and Mihai Oltean. "Fruit recognition from images using deep learning." Acta Universitatis Sapientiae, Informatica 10, no. 1 (2018): 26–42. http://dx.doi.org/10.2478/ausi-2018-0002.

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Abstract In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use such classifier.

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Jing, Juanli, Menglin Zhai, Shiqing Dou, et al. "Optimizing the YOLOv7-Tiny Model with Multiple Strategies for Citrus Fruit Yield Estimation in Complex Scenarios." Agriculture 14, no. 2 (2024): 303. http://dx.doi.org/10.3390/agriculture14020303.

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The accurate identification of citrus fruits is important for fruit yield estimation in complex citrus orchards. In this study, the YOLOv7-tiny-BVP network is constructed based on the YOLOv7-tiny network, with citrus fruits as the research object. This network introduces a BiFormer bilevel routing attention mechanism, which replaces regular convolution with GSConv, adds the VoVGSCSP module to the neck network, and replaces the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv) in the backbone network. The improved model significantly reduces the number of mo

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Chen, Dan, Jiali Tang, Haixu Xi, and Xiaorong Zhao. "Image Recognition of Modern Agricultural Fruit Maturity Based on Internet of Things." Traitement du Signal 38, no. 4 (2021): 1237–44. http://dx.doi.org/10.18280/ts.380435.

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The accurate judgement of fruit maturity is significant for modern agriculture. At present, few scholars have monitored and recognized fruit maturity based on the Internet of things (IoT) and image recognition technology. Therefore, this paper explores the image recognition of fruit maturity in the context of agricultural Internet of things (IoT). Firstly, the single shot multi-box detection (SSD) algorithm was improved for fruit recognition and positioning, and used to determine the size and position the fruits to be recognized. Next, an image fusion algorithm was designed based on improved L

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Akshat, Bhutiani. "Real-Time Fruit Sorting Using Color and Shape Recognition on Embedded Systems for Automated Agriculture." Journal of Scientific and Engineering Research 5, no. 3 (2018): 14–19. https://doi.org/10.5281/zenodo.13950645.

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This paper presents a real time fruit sorting system that utilizes color and shape recognition on embedded systems for automated agriculture. The proposed system is implemented on an ARM Cortex – M microcontroller and uses color thresholding and shape feature extraction techniques to classify different fruits. A low-cost CMOS Camera captures images of the fruits as they move along a conveyer belt. The system uses real time image processing to sort the fruits in different categories. By fusing color and shape information, the system achieves a classification accuracy of 95%. With an empha

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Jiang, Renjie, Chengyun Wei, Yong Fan, Shuqin Wu, and Wu Xie. "Design and experimental research on intelligent harvesting device for citrus fruits." Journal of Physics: Conference Series 3032, no. 1 (2025): 012004. https://doi.org/10.1088/1742-6596/3032/1/012004.

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Abstract Aiming at the problems of low efficiency and high labor intensity in traditional manual picking of citrus fruits, a set of intelligent picking devices integrating a five-degree-of-freedom mechanical arm, a shear gripper, and an omnidirectional mobile chassis was designed. Using the Raspberry Pi 4B main control and binocular vision camera detection, combined with the YOLOv5 deep learning model, the threedimensional coordinate recognition and positioning of citrus fruits and the control of the mechanical arm picking were achieved, completing the precise shearing of the target fruit stal

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Carrington, Mary E., J. Jeffrey Mullahey, Gerard Krewer, Bob Boland, and James Affolter. "Saw Palmetto (Serenoa repens): An Emerging Forest Resource in the Southeastern United States." Southern Journal of Applied Forestry 24, no. 3 (2000): 129–34. http://dx.doi.org/10.1093/sjaf/24.3.129.

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Abstract Saw palmetto fruits collected from the wild are becoming a significant economic resource in Florida and south Georgia. The fruits are used to produce a drug for the treatment of benign prostatic hyperplasia (BPH). Here we introduce saw palmetto as an emerging resource for foresters and land managers, evaluate potential management practices, and discuss harvesting, processing and marketing aspects. Fruit production can be variable, affected by fruit disease, insect damage to flowers, depletion of plant carbohydrate reserves and drought. Controlled burning can enhance flowering and frui

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Maurya, Karan Kumar, Adarsh Verma, Danish Gaur, and Ankit Patel. "A Hybrid Classification Model (Fruits or Vegetable) Using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 2312–20. http://dx.doi.org/10.22214/ijraset.2024.63483.

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Abstract: In modern vision and pattern recognition, complex tasks such as picture analysis, facial recognition, fingerprint identification, and DNA sequencing necessitate a nuanced approach, often requiring the integration of multiple feature descriptors. This research proposes a multi-model identification and classification strategy leveraging multi- feature fusion techniques to address these intricate challenges. Specifically, the focus is on fruit and vegetable recognition and classification, a burgeoning field in computer and machine vision. By employing an identification system tailored t

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Kumar C, Udhaya, Miruthula R C, Pavithra G, Revathi R, and Suganya M. "FPGA-based Hardware Acceleration for Fruit Recognition Using SVM." Irish Interdisciplinary Journal of Science & Research 06, no. 02 (2022): 22–29. http://dx.doi.org/10.46759/iijsr.2022.6204.

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Selection classification for Fruit recognition could be an absolute zone of inspection. Fruit Recognition mistreatment FPGA-based Hardware Acceleration by SVM is helpful for the observance and indexing of the fruits consistent with their kind with the peace of mind of a quick production chain. During this test, we have processed to initial replacement prime quality data-set of pictures grouped in the 5 preferred varieties of oval-shaped fruits. Honor to the fast image process techniques for the development, image resolution, quality of the algorithms leads to carry-out image process and comput

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Kumar C, Udhaya, Miruthula R C, Pavithra G, Revathi R, and Suganya M. "FPGA-based Hardware Acceleration for Fruit Recognition Using SVM." Irish Interdisciplinary Journal of Science & Research 06, no. 02 (2022): 22–29. http://dx.doi.org/10.46759/iijsr.2022.6204.

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Selection classification for Fruit recognition could be an absolute zone of inspection. Fruit Recognition mistreatment FPGA-based Hardware Acceleration by SVM is helpful for the observance and indexing of the fruits consistent with their kind with the peace of mind of a quick production chain. During this test, we have processed to initial replacement prime quality data-set of pictures grouped in the 5 preferred varieties of oval-shaped fruits. Honor to the fast image process techniques for the development, image resolution, quality of the algorithms leads to carry-out image process and comput

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Hua, Xuehui, Haoxin Li, Jinbin Zeng, et al. "A Review of Target Recognition Technology for Fruit Picking Robots: From Digital Image Processing to Deep Learning." Applied Sciences 13, no. 7 (2023): 4160. http://dx.doi.org/10.3390/app13074160.

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Machine vision technology has dramatically improved the efficiency, speed, and quality of fruit-picking robots in complex environments. Target recognition technology for fruit is an integral part of the recognition systems of picking robots. The traditional digital image processing technology is a recognition method based on hand-designed features, which makes it difficult to achieve better recognition as it results in dealing with the complex and changing orchard environment. Numerous pieces of literature have shown that extracting special features by training data with deep learning has sign

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Yu, Jianyong, and Dejin Zhao. "Design of vision recognition system for picking robots." Journal of Physics: Conference Series 2383, no. 1 (2022): 012086. http://dx.doi.org/10.1088/1742-6596/2383/1/012086.

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Based on yolov4-tiny deep learning neural network, an improved yolov4-tiny network model is proposed in order to achieve the reduction of the network model for overlapping fruits and branch-obscured fruits in natural environment and to realize the accurate and fast recognition of apple-pear fruits, the main improvement measures include: firstly, the CSPBlock residual network module of the backbone network is introduced in the module of the backbone network to replace the 3×3 convolution kernel in it, which improves the perceptual field of the feature layer in the network and enhances the extra

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Krishna Kant Agarwal. "IOT Based Monitoring Model to Identify and Classify the Grading of Fruits and Vegetables." Journal of Electrical Systems 20, no. 3 (2024): 491–98. http://dx.doi.org/10.52783/jes.2976.

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Thirty to forty percent of the fruits and vegetables harvested are discarded at this time due to the large number of employees with no training. A growing problem throughout reaping is fruit and vegetable waste because human judgment is subjective when it comes to crop recognition, categorization, and assessment. The fruit and vegetable business is in serious require of the launch and deployment of a robotic system in order to classify and grade fruits and vegetables based on their level of ripeness. Machine learning techniques can produce a sophisticated machine learning framework that can di

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Guerra, Marcos, Rosa María Gómez, Miguel Ángel Sanz, Álvaro Rodríguez-González, and Pedro Antonio Casquero. "Effect of Fruit Weight and Fruit Locule Number in Bell Pepper on Industrial Waste and Quality of Roasted Pepper." Horticulturae 8, no. 5 (2022): 455. http://dx.doi.org/10.3390/horticulturae8050455.

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Bell pepper (Capsicum annuum L.), one of the most consumed vegetables worldwide, shows great differences between its diverse varieties. These differences affect the fruit type, size and shape. Food preservation techniques prolong the availability of sweet pepper. Roasted pepper is a product marketed with the European recognition of Protected Geographical Indication ‘Pimiento Asado del Bierzo’. The objective of this work was to analyse the effect of the fruit weight and fruit locule number of the industrial fresh pepper on quality and roasted pepper yield. Large trilocular fruits and large tetr

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Kanakaprabha, S., Dr Gaddam Venu Gopal, D. Kaleeswaran, D. Hemamalini, and Dr G. Ganeshkumar. "Fruits and Vegetables Detection using YOLO Algorithm." International Journal of Advanced Engineering Research and Science 10, no. 7 (2023): 053–60. http://dx.doi.org/10.22161/ijaers.107.8.

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The robotic harvesting platform's fruit and vegetable detection system is crucial. Due to uneven environmental factors such branch and leaf shifting sunshine, fruit and vegetable clusters, shadow, and so on, the fruit recognition has become more difficult in nowadays. The current method in this work is used to detect different types of fruits and vegetables in different size and shape. This method makes the use of OpenCV, Dark Flow, a TensorFlow variant of the YOLO technique. To train the necessary of network, a range of fruits and vegetable pictures were input into the network. The photos wer

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