Relevant bibliographies by topics / Fruits recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fruits recognition'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Fruits recognition"

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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>&nbsp;To develop a model for the automatic recognition of fruits utilizing deep learning techniques.&nbsp;<strong>Methods:</strong>&nbsp;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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Dissertations / Theses on the topic "Fruits recognition"

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Hameed, Khurram. "Computer vision based classification of fruits and vegetables for self-checkout at supermarkets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2519.

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The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide r
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Henry, Karen Jane. "An investigation of a pattern recognition system to analyse and classify dried fruit." Master's thesis, University of Cape Town, 1996. http://hdl.handle.net/11427/9249.

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Includes bibliographical references.<br>Both the declining cost and increasing capabilities of specialised computer hardware for image processing have enabled computer vision systems to become a viable alternative to human visual inspection in industrial applications. In this thesis a vision system that will analyse and classify dried fruit is investigated. In human visual inspection of dried fruit, the colour of the fruit is often the main determinant of its grade; in specific cases the presence of blemishes and geometrical fault are also incorporated in order to determine the fruit grade. A
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Cheng, Xuemei. "Hyperspectral imaging and pattern recognition technologies for real time fruit safety and quality inspection." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2154.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Biological Resources Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Björk, Gabriella. "Evaluation of system design strategies and supervised classification methods for fruit recognition in harvesting robots." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217859.

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This master thesis project is carried out by one student at the Royal Institute of Technology in collaboration with Cybercom Group. The aim was to evaluate and compare system design strategies for fruit recognition in harvesting robots and the performance of supervised machine learning classification methods when applied to this specific task. The thesis covers the basics of these systems; to which parameters, constraints, requirements, and design decisions have been investigated. The framework is used as a foundation for the implementation of both sensing system, and processing and classifica
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Aykas, Didem P. "Verification of Ingredient Labels in High-Risk Oils and Fruit Juices by Using Vibrational Spectroscopy Combined with Pattern Recognition Analysis." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555644726731438.

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CHENG, YEH-HSUN, and 鄭業勳. "Recognition and Calorie Intake Measurement of Fruits and Vegetables." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/55804059206025910222.

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碩士<br>國立中正大學<br>電機工程研究所<br>104<br>With the popularity of health awareness, there are more and more software and hardware that can help people record diet and measure calorie. Traditional written records, electronic barcode and measureing calorie with look-up table became incovenient. With the popularity of smartphone, the way using image recognition to record personal diet and measure calorie become a more convenient and high accuracy choice. Vegetable and fruit recognition is a image recognition method using vegetable and fruit images. Because we use hand to measure food weight, we do not hav
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Sun, Tzu-Chun, and 孫梓鈞. "Fruit Recognition Using Deep Convolution Neural Network." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/76315586416634324332.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>102<br>This thesis focuses on developing a fruit recognition method. It can be used to improve life convenience by shortening the supermarket checkout time. Existing methods for fruit recognition use handcrafted image features, such as the texture, the color, and the shape of a fruit, for fruit recognition. However, image features extracted with a set of specific algorithms do not necessarily provide enough information for pattern recognition. In this work, we use deep convolution neural network (DCNN) to learn discriminative fruit features automatically. In order
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Wu, Yi-Han, and 巫易翰. "An Implementation of a Real-Time Fruit Recognition System." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/07607500782175492421.

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碩士<br>南台科技大學<br>資訊工程系<br>97<br>In recent years, the field of robot applications such as Sony’s Dancing Robot QRIO and Honda’s ASIMO Robot is rapid developed. In the near future, robots will be integrated into human’s life. In order to bring us more convenient. How to make the robot to recognize the objects is a very interesting issue. For example, the housekeeper robot is requested to take the apple which is on the table and then the robot must find the apple out from the table in a very short time. Based on this idea, this study proposed a method which were applying color and texture features
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Li, Hsiu-chin, and 李秀勤. "Purchase Incentives and Value Recognition about the Fruit and Vegetable Juices: A Survey in the Eastern Region of Taiwan." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/j8sjj5.

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碩士<br>國立東華大學<br>管理學院高階經營管理碩士在職專班<br>103<br>This study explored the influence of consumer health concept, self-rated health, and product attributes, on the perceived value and purchase intention in the eastern part of Taiwan. General convenient sampling is adopted for questionnaires. Totally 470 questionnaires were sent out; 453 valid questionnaires were received; the effective rate is 96.38%. SPSS20.0 software package is used for statistical analysis about descriptive factors, ANOVA, T test, Pearson product moment correlation, validity and reliability analysis, and linear regression analysis.
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Ho, Cheng-Mao, and 何澄茂. "Employee Recognition on Uncertainty and Organizational Promise of Possible Move- Case Study of Taichung Fruit and Vegetable Wholesale Market." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/87439133409385900124.

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碩士<br>國立中興大學<br>農業企業經營管理碩士在職專班<br>103<br>Abstract The topic of organizational change is discussed due to the government’s city plan and region development. However, few studies have been done about the topic of organizational change of terminal market relocation. The studies is the case study of Taichung Food and Vegetables Terminal Market, and the objects of study are the employees at Taichung Food and Vegetables Terminal Market. The purpose of the studies is to explore the employees’ uncertainty about relocation and their cognition about organizational commitment. Above seventy percent of
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Books on the topic "Fruits recognition"

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Gall, Gregor. The fruits of our labour?: Outcomes of the new union recognition agreements. TUC, 2003.

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coloring fruits coloring fruits and vegetables. Coloring Fruits and Vegetables for Kids: Coloring Fruits and Vegetables with Recognition of Their Names for Kids . size 8,5x11 Inches. Independently Published, 2021.

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Dowe, John Leslie. Australian Palms. CSIRO Publishing, 2010. http://dx.doi.org/10.1071/9780643098022.

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Australian Palms offers an updated and thorough systematic and taxonomic treatment of the Australian palm flora, covering 60 species in 21 genera. Of these, 54 species occur in continental Australia and six species on the off-shore territories&#x0D; of Lord Howe Island, Norfolk Island and Christmas Island.&#x0D; Incorporating recent advances in biogeographic and phylogenetic research, Australian Palms provides a comprehensive introduction to the palm family Arecaceae, with reviews of botanical history, biogeography, phylogeny, ecology and conservation. Thorough descriptions of genera and speci
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Space, Emin. Fruit ABC Writing Practice Learn the English Alphabet from a to Z: These Essential Letter Drills Will Help with Letter Recognition Fun for Kids. Independently Published, 2019.

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Press, Possum. Big Kids Favorite Yummy Foods Coloring Book: Easy and Fun Pictures of Delicious Snacks and Treats Like Pizza, Candy, Burgers, Fries, Cake, Fruit, Ice Cream and More - Each Page Is Labelled to Encourage Reading Practice and Word Recognition. Independently Published, 2021.

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Press, Possum. Little Kids Favorite Yummy Foods Coloring Book: Super Easy Pictures of Delicious Snacks and Treats Like Pizza, Candy, Burgers, Fries, Cake, Fruit, Ice Cream and More for Toddlers to Color - Each Page Is Labelled to Encourage Early Word Recognition. Independently Published, 2021.

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Book chapters on the topic "Fruits recognition"

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Mandal, Olivia Saha, Aniruddha Dey, Subhrapratim Nath, Rabindra Nath Shaw, and Ankush Ghosh. "Fruit-Net: Fruits Recognition System Using Convolutional Neural Network." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25088-0_10.

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Gatica, C. Gabriel, S. Stanley Best, José Ceroni, and Gaston Lefranc. "A New Method for Olive Fruits Recognition." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25085-9_77.

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Aguiar, Henrique Tavares, and Raimundo C. S. Vasconcelos. "Identification of External Defects on Fruits Using Deep Learning." In Pattern Recognition and Image Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04881-4_45.

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Drew, Richard A. I., and Meredith C. Romig. "Species and speciation." In The fruit fly fauna (Diptera: Tephritideae: Dacinae) of Papua New Guinea, Indonesian Papua, Associated Islands and Bougainville. CABI, 2022. http://dx.doi.org/10.1079/9781789249514.0004.

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Abstract This chapter discusses two species models, which are diametrically opposed. The first, often called the 'biological species concept', defines species in terms of 'reproductive isolation', convinced that species arise when subsets of a population are split off and remain geographically isolated over evolutionary time. If and when such new species are reunited with their founder population, interbreeding does not occur, or if it does, infertile progeny result. Hence, from the biological species concept, natural selection is a primary agent of change and directly selects for new species.
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García Portilla, Jason. "Summary Overview of the Four Case Studies." In “Ye Shall Know Them by Their Fruits”. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78498-0_22.

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AbstractThis chapter presents a brief comparative summary overview of the four cases.In Switzerland, most conservative Catholics escaped modernisation and centralism by relocating to the mountains, while Liberals and Protestants mostly remained in flat areas that became industrialised (Obinger, 2009). The federal government has been mainly liberal (anti-clerical) and close to Protestantism. Likewise, the Protestant population was in the majority until the 1970s. Currently, the Protestant cantons are the most competitive, while the mountainous Roman Catholic cantons are the least competitive.Ur
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Yumoto, Yuya, and Ikuo Mizuuchi. "Recognition of Three-Dimensional Branch Structure and Fruits Identification in a Tree Based on It." In Intelligent Autonomous Systems 13. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08338-4_63.

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Pinsent, Andrew. "Hope as a Virtue in the Middle Ages." In Historical and Multidisciplinary Perspectives on Hope. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46489-9_3.

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Abstract As a theological disposition revealed in Scripture, the recognition of hope as an important virtue coincided with the radical transformation in virtue ethics in the early Middle Ages. As the ideals of pagan antiquity gave way to the Christian aspirations for the Kingdom of Heaven, early work on hope was strongly influenced by writers with a monastic background, such as Pope St Gregory the Great. The rise of scholasticism in the twelfth and thirteenth centuries, however, gave an impetus to finding a coherent account of virtue ethics that would incorporate hope along with the other theo
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Balakesava Reddy, P., Somula Ramasubbareddy, D. Saidulu, and K. Govinda. "Fruit Recognition Using Deep Learning." In Innovations in Computer Science and Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4543-0_7.

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Ali, Syed Sumera Ershad, and Sayyad Ajij Dildar. "MLP-WOA Neural Network-Based Automated Grading of Fruits and Vegetable Quality Detection for Food Industry Using Artificial Intelligence Techniques (Computer Vision—Image Recognition)." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4604-8_43.

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Jana, Susovan, and Ranjan Parekh. "Shape-based Fruit Recognition and Classification." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6430-2_15.

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Conference papers on the topic "Fruits recognition"

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Xu, Zijian, Juncheng Chen, Xintong Wu, and Chengming Tian. "Intelligent Recognition of Packed Fruits Based on Deep Learning." In 2024 9th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2024. https://doi.org/10.1109/icivc61627.2024.10837532.

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Singh, Aryan, Priyanshu Gusain, Daksh Rawat, Harshit Narang, Satvik Vats, and Vikrant Sharma. "Deep Learning-Based Fruits And Vegetables Recognition Using Convolutional Neural Network." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10730946.

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Rivera, José Antonio Menjivar, and Alicia María Reyes-Duke. "Convolutional Neural Network for the Detection of Cocoa Maturity with an Approach for the Analysis of Images Captured at Different Distances." In I Conferencia Internacional de Ciencia, Tecnología e Innovación. Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-jm3tgd.

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This study presents an approach to improve cocoa harvesting using image recognition technology. Convolutional neural networks (CNN) along with the help of the Roboflow platform were used to analyze images of cocoa fruits and determine their maturity stage in an uncontrolled environment. The study focused mainly on the effectiveness of taking pictures at different distances, dividing the images into three categories (0.10m-0.30m, 0.30m-1.00m and 1.00m-3.00m) each trained with 400 images in order to evaluate the performance of each one in terms of its mAP, precision and recall. Subsequently, a f
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Chuquimarca, Luis, Boris Vintimilla, and Sergio Velastin. "Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models." In 2024 14th International Conference on Pattern Recognition Systems (ICPRS). IEEE, 2024. http://dx.doi.org/10.1109/icprs62101.2024.10677833.

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Wang, Yizong, Zhengrong Xiao, Yue Cui, and Dengxun Sun. "A study on rapid recognition of ripe tomato fruits in complex environment based on YOLOv5s." In International Conference on Mechatronics and Intelligent Control (ICMIC 2024), edited by Kun Zhang and Pascal Lorenz. SPIE, 2025. https://doi.org/10.1117/12.3047485.

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Jiang, Houkang, Jizhan Liu, Xiaojie Lei, Baocheng Xu, and Jie Wang. "Research on target recognition, segmentation, and accurate positioning methods for obscured apple fruits under dense canopies." In International Conference on Optical and Photonic Engineering (icOPEN 2024), edited by Jianglei Di, Kemao Qian, Shijie Feng, et al. SPIE, 2025. https://doi.org/10.1117/12.3057765.

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Xia, Bing, Chaokui Ning, Zan Zhang, and Zhiqing Zhou. "Design of Fruit Quality Recognition System." In 2024 IEEE 16th International Conference on Advanced Infocomm Technology (ICAIT). IEEE, 2024. https://doi.org/10.1109/icait62580.2024.10808014.

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Isaac, Ting-Feng Low, Jit-Yan Lim, Yong-Xuan Tan, Kian-Ming Lim, Chin-Poo Lee, and Pa-Pa-Min. "Transfer Learning-based Fruit Image Recognition." In 2024 IEEE 12th Conference on Systems, Process & Control (ICSPC). IEEE, 2024. https://doi.org/10.1109/icspc63060.2024.10862682.

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Bhendarkar, Deep, Gajendra Bhople, Sahil Wani, Sarika Khope, and Sandeep D. Hanwate. "Autonomous System for Fruit Freshness Recognition." In 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2025. https://doi.org/10.1109/ispcc66872.2025.11039604.

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Wang, Chenxi. "Fruit classification and recognition based on CNN." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3070884.

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Reports on the topic "Fruits recognition"

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Droby, Samir, Tim R. Gottwald, Richard Stange, Efraim Lewinsohn, and T. Gregory McCollum. Characterization of the biochemical basis of host specificity of Penicillium digitatum and Penicillium italicum on citrus fruit. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7587726.bard.

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l. This research demonstrates that citrus fruit volatiles play an important role in host recognition by P. digitatum and P. italicum. 2. Volatiles derived from non-host fruits and vegetables (apple, pear, tomato, pepper, strawberry and avocado) had no effect on promotion of spore germination and growth of citrus pathogens. 3. Citrus volatiles have a specific stimulatory effect solely on P. digitatum and P. italicum. Non-citrus pathogens such as P. expansum and B. cinerea not affected orinhibited by the volatile materials. The specific stimulatory effect of fruit peelvolatiles on citrus pathoge
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Shapovalov, Viktor B., Yevhenii B. Shapovalov, Zhanna I. Bilyk, Anna P. Megalinska, and Ivan O. Muzyka. The Google Lens analyzing quality: an analysis of the possibility to use in the educational process. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3754.

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Biology is a fairly complicated initial subject because it involves knowledge of biodiversity. Google Lens is a unique, mobile software that allows you to recognition species and genus of the plant student looking for. The article devoted to the analysis of the efficiency of the functioning of the Google Lens related to botanical objects. In order to perform the analysis, botanical objects were classified by type of the plant (grass, tree, bush) and by part of the plant (stem, flower, fruit) which is represented on the analyzed photo. It was shown that Google Lens correctly identified plant sp
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Davis, Cristina, Amots Hetzroni, Alexander Aksenov, Michael J. Delwiche, Victoria Soroker, and Victor Alchanatis. Development of a universal volatile compound detection technology for early recognition of pests and diseases in fruit trees. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7600016.bard.

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Sessa, Guido, and Gregory Martin. A functional genomics approach to dissect resistance of tomato to bacterial spot disease. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7695876.bard.

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The research problem. Bacterial spot disease in tomato is of great economic importance worldwide and it is particularly severe in warm and moist areas affecting yield and quality of tomato fruits. Causal agent of spot disease is the Gram-negative bacterium Xanthomonas campestris pv. vesicatoria (Xcv), which can be a contaminant on tomato seeds, or survive in plant debris and in association with certain weeds. Despite the economic significance of spot disease, plant protection against Xcvby cultural practices and chemical control have so far proven unsuccessful. In addition, breeding for resist
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Gordon, Dalia, Ke Dong, and Michael Gurevitz. Unexpected Specificity of a Sea Anemone Small Toxin for Insect Na-channels and its Synergic Effects with Various Insecticidal Ligands: A New Model to Mimic. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7697114.bard.

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Motivated by the high risks to the environment and human health imposed by the current overuse of chemical insecticides we offer an alternative approach for the design of highly active insect-selective compounds that will be based on the ability of natural toxins to differentiate between insect and mammalian targets. We wish to unravel the interacting surfaces of insect selective toxins with their receptor sites on voltage-gated sodium channels. In this proposal we put forward two recent observations that may expedite the development of a new generation of insect killers that mimic the highly
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