Статті в журналах: "Intelligenza vegetale"

1

Yang, Linlin, Yingluo Song, Zilin Hu, Aili Wang, Haibin Wu, and Yuji Iwahori. "Design and Implementation of Intelligent Vegetable Recognition System based on MobileNet." Embedded Selforganising Systems 9, no. 3 (December 21, 2022): 82–86. http://dx.doi.org/10.14464/ess.v9i3.579.

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With the rise of food safety traceability, unmanned supermarkets and autonomous shopping, the automatic identification technology of agricultural products such as vegetables in circulation and sales has become an urgent problem. This paper designs an intelligent vegetable identification system based on MobileNet to solve intelligent identification problem of vegetable sales in supermarkets. The system includes main control core, visual processing module, pressure sensor, voice broadcasting module and display module. When the system detects that there are vegetables to be weighed, the visual processing module completes the classification of vegetables, broadcasts the name, unit price and total price of vegetables by voice, and displays the weight, unit price and total price by OLED. The machine vision processing module is constructed by deep separable convolution (DSC). It realizes the separation of channels and regions, so it has high computing efficiency and is more suitable for embedded devices with low memory space. The experimental results show that the overall recognition rate of five vegetables reaches 97.33% under three kinds of illumination. The system has the advantages of stability, intelligence and convenience.

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Zheng, Bowen, Guiling Sun, Zhaonan Meng, and Ruili Nan. "Vegetable Size Measurement Based on Stereo Camera and Keypoints Detection." Sensors 22, no. 4 (February 18, 2022): 1617. http://dx.doi.org/10.3390/s22041617.

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This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable’s non-contact measurement problems and can promote the application of computer vision in agricultural automation.

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3

Van Volkenburgh, Elizabeth. "Brilliant Green: The Surprising History and Science of Plant Intelligence. By Stefano Mancuso and Alessandra Viola; Foreword by Michael Pollan; translated by Joan Benham. Washington (DC): Island Press. $20.00. xiii + 173 p.; ill.; no index. ISBN: 978-1-61091-603-5. [Original title: Verde brillante: Sensibilità e intelligenza del mondo vegetale, 2013.] 2015." Quarterly Review of Biology 91, no. 1 (March 2016): 100–101. http://dx.doi.org/10.1086/685354.

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4

Wang, Zhi Gang, Xiao Guang Chen, and Wen Fu Wu. "Artificial Intelligence for the Control of Northeast Region Greenhouse Base on Biomass." Applied Mechanics and Materials 48-49 (February 2011): 270–73. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.270.

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Greenhouse is used to produce vegetables in winter, the parameter of environment which suit vegetables grow is a complex and ill-defined problem. Furthermore, burning coal to heat greenhouse is expensive than carry vegetables from south region enormously, so at northeast, most greenhouse cannot produce. On the other hand, there are more than several hundred million ton biomass be burned in field. Not only waste natural resources greatly, but also caused pollution seriously. This paper presents an artificial intelligence control system of northeast region greenhouse base on biomass, peasants can use this system produce vegetables automatically by simple intervening. This system also can learn new vegetables growth data and representation it to control vegetable producing.

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5

Tata, Jagannadha Swamy, Naga Karthik Varma Kalidindi, Hitesh Katherapaka, Sharath Kumar Julakal, and Mohan Banothu. "Real-Time Quality Assurance of Fruits and Vegetables with Artificial Intelligence." Journal of Physics: Conference Series 2325, no. 1 (August 1, 2022): 012055. http://dx.doi.org/10.1088/1742-6596/2325/1/012055.

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Abstract Vegetables and fruits make up a major part of the human diet and finding a good grade of its quality is now a major issue in the market. To find a grade of the vegetable or fruit will be based on some parameters like size, shape, appearance, etc. The appearance is now a deciding factor for the market and affects the consumer’s choice. So, we have designed an application that will classify fruits and grade them according to their quality with appearance as a parameter. This paper will describe the process which is involved in the application. This proposed system will use image processing to classify and grade the quality of fruits and vegetables by extracting features such as color, shape, and HOG (Histogram of Gradient) to classify the given fruit or vegetable. Image pre-processing techniques like data-augmentation and normalization along with Principle- Component Analysis (PCA), and also Deep learning (CNN) are used for getting good accuracy and for dimensional reduction. To speed up the identification and increase the usability, compared to current manual systems like a person checking every fruit and vegetable to grade which takes more time and energy, or by using embedded systems (sensors), we opted for a high-performance android application for quicker deployment.

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ANUSHA, V. V. S. S., and S. R. PADMA. "RELATIONSHIP ANALYSIS BETWEEN MARKETING BEHAVIOUR AND PROFILE OF VEGETABLE GROWERS OF RANGA REDDY DISTRICT." JOURNAL OF RESEARCH ANGRAU 50, no. 4 (December 31, 2022): 124–34. http://dx.doi.org/10.58537/jorangrau.2022.50.4.13.

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Ranga Reddy district of Telangana state tops in area and production of vegetables and was selected to study the marketing behaviour in year 2021. The profile variables were finalized after the judge’s opinion with the dependent variable was drafted as the interview schedule to collect responses from 150 farmers. The results displayed half of the respondents were middleaged, mostly (30.67%) with high school education taking up only agriculture (50.67%) as the occupation. The respondents (68.67%) were having medium experience in vegetable production with 84.67 per cent of them allocated a medium share of their land for vegetable production. A lion’s share (90.00%) of respondents had a medium annual income and no one recorded a low annual income. Respondents had only a medium to low marketing behaviour of vegetable growers that can be improved by increasing education, the area under vegetable production, market orientation, market intelligence, information-seeking behaviour, and decision-making ability of respondents that had a significant positive relationship with the marketing behaviour of vegetable growers at a 1% level of significance. The variables market orientation (0.378) and market intelligence (0.331) had positive direct impact, whereas experience in vegetable farming (-0.251) had negative direct effect. Information-seeking behaviour (0.332), decision-making ability (0.290) and area under vegetable production (0.288) were the top three variables causing a higher indirect effect on dependent variable. Now, these findings will help in further development of marketing behaviour.

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7

Liu, Jun. "Tomato Yield Estimation Based on Object Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 7 (November 20, 2018): 1120–25. http://dx.doi.org/10.20965/jaciii.2018.p1120.

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At present, the vegetable yield estimation in China is performed by manual sampling and visual observation of vegetable counts. This is not only time-consuming and labor-intensive, but it also has low precision. In this study, we capture video surveillance images of the tomatoes during plant maturation, and use neural networks to identify pictures, extract growing features, identify the number of vegetables hanging from the plants, and establish an estimation model for tomato yield. We then take a sample of the vegetables to be measured. Strains are image-analyzed and processed to predict yield per plant and yield per unit area to obtain an accurate prediction of tomato yield.

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8

Changdar, Chiranjit, Pravash Kumar Giri, Rajat Kumar Pal, Alok Haldar, Samiran Acharyya, Debasis Dhal, Moumita Khowas, and Sudip Kumar Sahana. "Solving a Mathematical Model for Small Vegetable Sellers in India by a Stochastic Knapsack Problem: An Advanced Genetic Algorithm Based Approach." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 05 (October 2022): 897–921. http://dx.doi.org/10.1142/s0218488522500271.

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In this paper, we have proposed a stochastic Knapsack Problem (KP) based mathematical model for small-scale vegetable sellers in India and solved it by an advanced Genetic Algorithm. The knapsack problem considered here is a bounded one, where vegetables are the objects. In this model, we have assumed that different available vegetables (objects) have different weights (that are available), purchase costs, and profits. The maximum weight of vegetables that can be transported by a seller is limited by the carrying capacity of the vegetable carrier and the business capital of the seller is also limited. The aim of the proposed mathematical model is to maximize the total profit of the loaded/traded items, with a set of predefined constraints on the part of the vegetable seller or retailer. This problem has been solved in a Type-2 fuzzy environment and the Critical Value (CV) reduction method is utilized to defuzzify the objective value. We have projected an improved genetic algorithm based approach, where we have incorporated two features, namely refinement and immigration. We have initially considered benchmark instances and subsequently some redefined cases for experimentation. Moreover, we have solved some randomly generated proposed KP instances in Type-2 fuzzy environment.

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9

Fedosov, A. Yu, and A. M. Menshikh. "Precision farming technologies in vegetable growing." Vegetable crops of Russia, no. 6 (December 7, 2022): 40–45. http://dx.doi.org/10.18619/2072-9146-2022-6-40-45.

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Precision farming technologies can help mitigate the environmental impact of agriculture by reducing the use of fertilizers and irrigation while reducing costs. Vegetable precision farming technology uses geographic positioning system (GPS), geographic information system (GIS), artificial intelligence (IoT), robotics, sensor technology, data-based genome editing, etc. to improve the production and quality of vegetables. Digital genome sequencing, developed over the past decade, has greatly reduced the cost and time required to map the DNA of plants and other organisms. Digital genome sequencing methods generate vast amounts of genome sequence data, which in turn aid in plant breeding for specific field conditions or desired traits. This maintains excellent prospects for growing vegetables in the current farming scenario, when climate change is forcing a rethink of all agricultural practices. This article provides useful information about precision farming technologies for vegetable growers, enthusiasts, farmers and researchers. Economic factors are important drivers and barriers to technology adoption. The practical significance of new technologies provided through communication and education has additional potential in terms of their promotion.

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10

Dou, Dandan. "Sensor Vegetable Greenhouse and Agricultural Product Supply Chain Management Based on Improved Neural Network." Mobile Information Systems 2022 (August 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/4139784.

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In recent years, people have begun to collect environmental data in vegetable greenhouses. Therefore, this article studies the current internal control technology of vegetable greenhouses, and improves the sensor application in vegetable greenhouses, in a targeted manner by combining with the improved neural network algorithm. The model has more accurate prediction accuracy than traditional BP. By using the Android client and ZigBee artificial intelligence control technology it creates the most suitable living conditions for the vegetables, fruits, and other crops in the vegetable greenhouse by controlling the various valves, sun visors, and light supplementary switches inside the vegetable greenhouse. The simulation results show that the model proposed in this article has higher convergence accuracy. In addition, this article applies the improved neural network to the management of the agricultural product supply chain, starting from the agricultural product supply chain and agricultural product supply chain management. Based on this, the analytic hierarchy process is used to explore the influencing factors of agricultural product supply chain management, and the analysis shows that consumers are targeting different consumer demand raised by the agricultural product supply chain. The improved neural network provides new ideas for the research of sensor vegetable greenhouses and agricultural product supply chain management.

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11

Li, Xiao Di, Wei Ping Hu, and Xiao Ping Yang. "Thinking on the Water System Design Method of Intelligent Civil Ecological Agriculture Residential Buildings." Applied Mechanics and Materials 357-360 (August 2013): 487–91. http://dx.doi.org/10.4028/www.scientific.net/amm.357-360.487.

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The intelligent civil ecological agriculture residential building is the authors creative idea. The design of its water system, as one of the important contents, means providing water according to the demand of the vegetables and flowers growth intelligently, instead of watering by labors. In this way, humans planting experience and intelligence will be endowed with building facilities, thus constructing a small automatic family farming system with the symbiosis of man, machine and plant. Based on new achievements of modern science and technology, a reasonable energy-conservative and artistic water supply system is studied with logical method and application. In future, intelligent water supply in agriculture residential building is the trend, which could be achieved by intelligent application of modern electronic information technology.

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12

Dubey, Shiv Ram, and Anand Singh Jalal. "Application of Image Processing in Fruit and Vegetable Analysis: A Review." Journal of Intelligent Systems 24, no. 4 (December 1, 2015): 405–24. http://dx.doi.org/10.1515/jisys-2014-0079.

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AbstractImages are an important source of data and information in the agricultural sciences. The use of image-processing techniques has outstanding implications for the analysis of agricultural operations. Fruit and vegetable classification is one of the major applications that can be utilized in supermarkets to automatically detect the kinds of fruits or vegetables purchased by customers and to determine the appropriate price for the produce. Training on-site is the underlying prerequisite for this type of arrangement, which is generally caused by the users having little or no expert knowledge. We explored various methods used in addressing fruit and vegetable classification and in recognizing fruit disease problems. We surveyed image-processing approaches used for fruit disease detection, segmentation and classification. We also compared the performance of state-of-the-art methods under two scenarios, i.e., fruit and vegetable classification and fruit disease classification. The methods surveyed in this paper are able to distinguish among different kinds of fruits and their diseases that are very alike in color and texture.

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13

Huang, Yi, Junze Xu, and Yida An. "Intelligent integrated multifunctional vegetable cutter system." Journal of Physics: Conference Series 1865, no. 3 (April 1, 2021): 032073. http://dx.doi.org/10.1088/1742-6596/1865/3/032073.

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14

Xuehan, Gao, Xu Jianjun, and Yan Limei. "Vegetable Greenhouses Intelligent Temperature Control System." Advance Journal of Food Science and Technology 10, no. 1 (January 5, 2016): 43–48. http://dx.doi.org/10.19026/ajfst.10.1750.

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15

Dziallas, Kristina. "Gender stereotyping." Metaphor and the Social World 9, no. 2 (November 5, 2019): 199–220. http://dx.doi.org/10.1075/msw.18007.dzi.

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Abstract Across languages, the head and sexualized body parts (i.e., vagina, breasts, penis, testicles) are conceptualized in a number of ways, for example as fruits and vegetables: heads are conceptualized as cabbages, vaginas as figs, breasts as melons, penises as carrots, and testicles as olives, to only name a few. The present study draws on the theories of conceptual metaphor and metonymy by Lakoff & Johnson (1980) to analyze the conceptualizations of the five body parts as fruits and vegetables in English, Spanish and French. For this purpose, a slang dictionary-based database of 184 conceptualizations was compiled. Research on the head and sexualized body parts is particularly interesting as they represent the core of intellect and sexuality respectively, which makes them prone to being conceptualized in a variety of expressive and euphemistic ways. The results of the present study show that female body parts are primarily conceptualized as sweet fruits, while the penis as well as the head are mostly understood of as savory vegetables. This finding suggests a case of gender stereotyping, whereby sweet-natured women are denied intelligence as the head is stereotypically seen as a male body part (i.e., as a savory vegetable).

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Farooq, Omer, and Jasmeen Gill. "Vegetable Grading and Sorting using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 13–21. http://dx.doi.org/10.22214/ijraset.2022.40407.

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Abstract: Agriculture and food industry are the backbone of any country. Food industry is the prime contributor in agricultural sector. Thus, automation of vegetable grading and sorting is the need of the hour. Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this research. Back propagation is the most important algorithm for training neural networks. But, it easily gets trapped in local minima leading to inaccurate solutions. Therefore, some global search and optimization techniques were required to hybridize with artificial neural networks. One such technique is Genetic algorithms that imitate the principle of natural evolution. So, in this article, a hybrid intelligent system is proposed for vegetable grading and sorting in which artificial neural networks are merged with genetic algorithms. Results show that proposed hybrid model outperformed the existing back propagation based system. Keywords: Vegetable grading and sorting; artificial neural networks; Particle Swarm Optimization; Hybrid intelligent system; Pattern recognition

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17

Volison-Klimentiev, Anastasiya, Lucas Cullari, Gal Shachar-Michaely, Snir Dor, Sivan Peretz-Damari, Noa Afik, and Oren Regev. "Vegetable-Oil-Based Intelligent Ink for Oxygen Sensing." ACS Sensors 5, no. 10 (September 30, 2020): 3274–80. http://dx.doi.org/10.1021/acssensors.0c01777.

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18

Lin, Nan, and He Long Yu. "Vegetable Intelligent Monitoring System Database Design Based on the Internet of Things." Advanced Materials Research 655-657 (January 2013): 1423–26. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.1423.

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Based on the internet of things (IOT), the application of the intelligent monitoring system in agriculture is studied. Tomato cultivation is chosen as the researching object. Various domain experts’ knowledge and experience on internet of things, intelligent control technology and tomato cultivation are integrated. Vegetable cultivation intelligent monitoring system is studied. Detailed design of database, which is the basic part of intelligent monitoring system, is carried out. Design includes conceptual design, logic design and physical design. Further design of the system is also discussed.

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Yan, Guoping, Maoshuo Feng, Weiguo Lin, Yuan Huang, Ruizheng Tong, and Yan Cheng. "Review and Prospect for Vegetable Grafting Robot and Relevant Key Technologies." Agriculture 12, no. 10 (September 30, 2022): 1578. http://dx.doi.org/10.3390/agriculture12101578.

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Grafting is an effective way to overcome the obstacles of continuous soil cropping and improve the tolerance of plants to abiotic and biotic stresses. An automatic grafting robot can effectively improve the grafting efficiency and survival rate of grafted seedlings, which is an important demand for the commercialization and promotion of vegetable planting. Based on the six main grafting technologies, this paper deeply summarized and analyzed the research status, technical characteristics, and development trends of vegetable grafting robots developed by various countries in the world. At the same time, it focused on the design methods and characteristics of key components such as seedling picking device, clamping device, and cutting device of vegetable grafting robots in detail. Then, the application of machine vision in the grafting robot was compared from the aspects of seed information feature recognition, automatic seedling classification, seedling state detection, and auxiliary grafting. It also was pointed out that machine vision technology was the only way to realize the fully automated grafting of vegetable grafting robots. Finally, several constraints, such as the limited grafting speed of vegetable grafting robots were pointed out, and the future development direction of grafting robots was predicted. As a result, it is believed that the intelligence degree of vegetable grafting robots needs to be improved, and its research and development fail to integrate with the seedling biotechnology, which leads to its poor universality. In the future, improving machine vision, artificial intelligence, and automation technology will help the development of high-performance universal grafting robots.

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20

Xin, Jin, Li Mingyong, Zhao Kaixuan, Ji Jiangtao, Ma Hao, and Qiu Zhaomei. "Development of vegetable intelligent farming device based on mobile APP." Cluster Computing 22, S4 (March 2, 2018): 8847–57. http://dx.doi.org/10.1007/s10586-018-1979-4.

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21

Zhang, Yin-ping, Yin-ping Zhang, Shuang-jie Zhu, Huan Wang, Yan Xu, Shuang-jie Zhu, Huan Wang, and Yan Xu. "Research on Intelligent Grading System of Tremella Fuciformis Based on Machine Vision." Applied Engineering in Agriculture 38, no. 6 (2022): 961–73. http://dx.doi.org/10.13031/aea.14824.

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Highlights The experimental results showed that the grading accuracy of the intelligent grading system of Tremella fuciformis designed in the study was 97.07%. The grading speed designed in the study was five times that of manual grading. The grading system designed in the study can also be extended to classification fields of other edible fungi, fruits, and vegetables. It was proved by experiments that the system involved has high work efficiency and market value. Abstract. An intelligent grading system of dried Tremella fuciformis was designed to solve the problem of low intelligence in the course of production and processing of Tremella fuciformis in this study. The overall structure and working process of the grading system were described, and different grading standards were set according to the color, shape, size, and integrity of dried Tremella fuciformis. The image acquisition and image preprocessing of dried Tremella fuciformis were completed. The RGB model was used for the color feature extraction and recognition of dried Tremella fuciformis, and the diameter and integrity of dried Tremella fuciformis were judged by edge detection. A set of intelligent grading system of dried Tremella fuciformis was developed based on Microsoft Visual Studio 2017 platform. The experimental results showed that the grading accuracy of the grading system designed in the study was 97.07%, and the average grading speed was five times that of manual grading. It was better in reliability, speed, work efficiency, and robustness than the traditional manual grading. Keywords: Intelligent classification, Image recognition, Machine vision, Tremella fuciformis.

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22

Wang, Xinfa, Vladislav Zubko, Viktor Onychko, Zhenwei Wu, and Mingfu Zhao. "Research on intelligent building greenhouse plant factory and “3-Positions and 1-Entity” development mode." IOP Conference Series: Earth and Environmental Science 1087, no. 1 (October 1, 2022): 012062. http://dx.doi.org/10.1088/1755-1315/1087/1/012062.

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Abstract After continuous development and evolution, the plant growth greenhouse has developed from the original heat preservation and moisture film cover to the present multi-cross intelligent solar greenhouse, which has played an important social value in agricultural production. However, in terms of the form of the greenhouse, it has not kept pace with the pace of social development, far from meeting the needs of environmental protection, energy saving, stable, intelligent, long-term use. With the wide application of big data, artificial intelligence, block chain, Internet of Things, cloud computing and other intelligent technologies in agriculture, as well as the rapid development of soilless culture and artificial light of plant lighting technology, the intelligent building greenhouse based on modern building technology can not only completely get rid of geographical location and regional climate conditions, but also durable to achieve long-term use standards and facilitate the deployment of intelligent operation equipment, which will become a more advanced and high-end form of intelligent plant greenhouse. Therefore, this paper defines the concept of “building greenhouse” for the first time, preliminarily discusses the construction idea of “intelligent building greenhouse plant factory”, and puts forward the suggestion of “trinity” new development mode, in order to play a pioneer role. With the proposal of “intelligent building greenhouse plant factory”, we firmly believe that the intelligent building greenhouse plant factory will gradually gain social recognition and get rapid development under the promotion of the “trinity” development mode. It can be predicted that skyscrapers style “Intelligent building greenhouse plant factory” will be everywhere in the foreseeable future, no matter in the heart of the city and other extreme weather conditions desert, ruin. In the future, people living near plant factories will be able to breathe oxygen-rich air, eat clean vegetables from nearby plant factories, and enjoy a high-quality and healthy life brought by abundant fresh plant food. What’s more, the intelligent building greenhouse plant factory can better the environment of our earth to a certain extent, improve its carrying capacity, alleviate the food panic caused by outbreaks or disasters, local unrest to consolidate food security, intensive and efficient use of cultivated land resources, and rich people’s pursuit of healthy plant food raw materials.

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Rapela, Miguel A. "Mejoramiento vegetal moderno, inteligencia artificial y derechos de propiedad intelectual." Revista Jurídica Austral 1, no. 2 (December 12, 2020): 839–66. http://dx.doi.org/10.26422/rja.2020.0102.rap.

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The modern plant breeding to obtain new plant varieties is based on genomic and phenomic selection generated through big data with millions of information points. In the face of such a quantity of data, it is necessary to use artificial intelligence to combine a complete vision and analysis of the problem through a human-computer interaction never addressed.The use of artificial intelligence has already created interpretive challenges in patents and copyrights. To a greater extent, modern plant breeding with the assistance of artificial inte-lligence is exposing major disarticulations and anachronisms in the Plant Breeder’s Rights and patent systems for biotechnological inventions. The challenges may even extend to the question of who would be entitled to the right in the case of products obtained without human intervention.The analysis of the situation indicates, on the one hand, that it would be necessary a review of the international framework of intellectual property rights in plant living matter which is based on independent treaties and conventions that apply to an indivisible organism as is a new plant variety. A more logical proposal would be to have a single, modern, and up-to-date compre-hensive sui generis protection system for all types of plant germplasm. On the other hand, it is proposed that, even in the case of products obtained through complete artificial intelligence processes, there must always be a human person legally responsible of the consequences of their actions, whether positive or negative

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Singh, Abha, Gayatri Vaidya, Vishal Jagota, Daniel Amoako Darko, Ravindra Kumar Agarwal, Sandip Debnath, and Erich Potrich. "Recent Advancement in Postharvest Loss Mitigation and Quality Management of Fruits and Vegetables Using Machine Learning Frameworks." Journal of Food Quality 2022 (June 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/6447282.

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Agriculture is an important component of the concept of sustainable development. Given the projected population growth, sustainable agriculture must accomplish food security while also being economically viable, socially responsible, and having the least possible impact on biodiversity and natural ecosystems. Deep learning has shown to be a sophisticated approach for big data analysis, with several successful cases in image processing, object identification, and other domains. It has lately been applied in food science and engineering. Among the issues and concerns addressed by these systems were food recognition; quality detection of fruits, vegetables, meat, and aquatic items; food supply chain; and food contamination. In precision agriculture, Artificial Intelligence (AI) is a commonly used technology for estimating food quality. It is especially important when evaluating crops at different phases of harvest and postharvest. Crop disease and damage detection is a high-priority activity because some postharvest diseases or damages, such as decay, can destroy crops and produce poisons that are toxic to humans. In this paper, we use Convolutional Neural Networks (CNNs)-based U-Net, DeepLab, and Mask R-CNN models to detect and predict postharvest deterioration zones in stored apple fruits. Our approach is unique in that it segmented and predicted postharvest decay and nondecay zones in fruits separately. This review will focus on postharvest physiology and management of fruits and vegetables, including harvesting, handling, packing, storage, and hygiene, to reduce postharvest loss (PHL) and improve crop quality. It will also cover postharvest handling under extreme weather conditions and potential impacts of climate change on vegetable postharvest and postharvest biotechnology on PHL.

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Singh, Abha, Gayatri Vaidya, Vishal Jagota, Daniel Amoako Darko, Ravindra Kumar Agarwal, Sandip Debnath, and Erich Potrich. "Recent Advancement in Postharvest Loss Mitigation and Quality Management of Fruits and Vegetables Using Machine Learning Frameworks." Journal of Food Quality 2022 (June 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/6447282.

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Анотація:

Agriculture is an important component of the concept of sustainable development. Given the projected population growth, sustainable agriculture must accomplish food security while also being economically viable, socially responsible, and having the least possible impact on biodiversity and natural ecosystems. Deep learning has shown to be a sophisticated approach for big data analysis, with several successful cases in image processing, object identification, and other domains. It has lately been applied in food science and engineering. Among the issues and concerns addressed by these systems were food recognition; quality detection of fruits, vegetables, meat, and aquatic items; food supply chain; and food contamination. In precision agriculture, Artificial Intelligence (AI) is a commonly used technology for estimating food quality. It is especially important when evaluating crops at different phases of harvest and postharvest. Crop disease and damage detection is a high-priority activity because some postharvest diseases or damages, such as decay, can destroy crops and produce poisons that are toxic to humans. In this paper, we use Convolutional Neural Networks (CNNs)-based U-Net, DeepLab, and Mask R-CNN models to detect and predict postharvest deterioration zones in stored apple fruits. Our approach is unique in that it segmented and predicted postharvest decay and nondecay zones in fruits separately. This review will focus on postharvest physiology and management of fruits and vegetables, including harvesting, handling, packing, storage, and hygiene, to reduce postharvest loss (PHL) and improve crop quality. It will also cover postharvest handling under extreme weather conditions and potential impacts of climate change on vegetable postharvest and postharvest biotechnology on PHL.

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Li, Feng Xia, Guang Cheng Jiang, Zheng Ku Wang, Mao Rong Cui, and Wen Hua Li. "Development of the Natural Vegetable Gum Drilling and Completion Fluids System for Industrial Intelligent Application." Advanced Materials Research 443-444 (January 2012): 241–45. http://dx.doi.org/10.4028/www.scientific.net/amr.443-444.241.

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To ensure the fluids selected to drill and complete the well would simplify the operation for the oil and gas development in the petroleum industry, a natural vegetable gum drilling and completion fluids system is developed for the industrial intelligent application. As the system combines the advantage of the drilling fluid and completion fluid, it need not change the two different fluids during the operation, which is beneficial to the intelligent operation. In addition, the formulation of the proposed system has mainly taken the environment factor into consideration as the environmental protection has become main concern before the implementation of the oil and gas exploration. An extensive laboratory work of the natural vegetable gum drilling and completion fluids system is carried out, including the formulation study of the detailed system and the corresponding performance evaluation. In the system, the vegetable gum is chosen as raw material and TLJ-1 is optimally selected as the major treatment agent in the natural vegetable gum drilling and completion fluid system. The LV-CMC, polyglycol and QS-2 are taken as the auxiliary treatment agents for the system. And the three formulations, i. e. the solids-free fluid system, the low-solids fluid system and the weighting fluid system have been presented in this paper. The laboratory analysis has demonstrated that the prosperities of the system are proper for the industrial intellectual application, with the temperature resistance capability of 315 ℉.

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Beloff, Laura. "The Hearing Test: Evidence of a Vegetal Entity." Leonardo Music Journal 30 (December 2020): 85–89. http://dx.doi.org/10.1162/lmj_a_01097.

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The author's artistic experiment The Hearing Test focuses on detection of high frequency clicking sounds that are emitted by the tips of plants' roots. Scientists have claimed that plants' roots produce high frequency clicks between 20 and 300 kHz by bursting air bubbles. But while the phenomenon has been described, its cause remains unexplained. This lack of knowledge opens up possibilities for multiple interpretations and invites experimental approaches as well as speculation concerning plant intelligence, the role of species-specific hearing and sound as evidence. The article is an extended reflection on the experiment.

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Jiao, Xiaolong, Wen Xu, and Lintong Duan. "Study on Cold Chain Transportation Model of Fruit and Vegetable Fresh-Keeping in Low-Temperature Cold Storage Environment." Discrete Dynamics in Nature and Society 2021 (December 18, 2021): 1–9. http://dx.doi.org/10.1155/2021/8445028.

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Анотація:

Due to the limitation of later stage intelligent algorithms, the fruit and vegetable fresh-keeping cold chain transportation scheme did not meet the expectation and could not achieve the dual objectives of the shortest time and the lowest consumption at the same time. In order to solve the above problems, a cold chain transportation model of fruit and vegetable fresh-keeping in a low-temperature cold storage environment is proposed. The model is based on the topology of the cold chain transportation network. By setting the assumptions of the fruit and vegetable fresh-keeping cold chain transportation model, the objective model is composed of three parts: vehicle power fuel consumption cost, cold chain transportation refrigeration cost, and total fruit and vegetable loss cost. Under six constraints, the improved ant colony algorithm is used to find the optimal fruit and vegetable fresh-keeping cold chain transportation route. The experimental results show that compared with the methods based on ALNS, genetic algorithm, and quantum bacterial foraging optimization algorithm, the research method can bring the best comprehensive benefit by accomplishing the fruit and vegetable transportation task in the shortest time at the lowest cost, and the research goal is thus achieved.

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Kennedy, HannahJoy, Steven A. Fennimore, David C. Slaughter, Thuy T. Nguyen, Vivian L. Vuong, Rekha Raja, and Richard F. Smith. "Crop signal markers facilitate crop detection and weed removal from lettuce and tomato by an intelligent cultivator." Weed Technology 34, no. 3 (November 14, 2019): 342–50. http://dx.doi.org/10.1017/wet.2019.120.

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Анотація:

AbstractIncreasing weed control costs and limited herbicide options threaten vegetable crop profitability. Traditional interrow mechanical cultivation is very effective at removing weeds between crop rows. However, weed control within the crop rows is necessary to establish the crop and prevent yield loss. Currently, many vegetable crops require hand weeding to remove weeds within the row that remain after traditional cultivation and herbicide use. Intelligent cultivators have come into commercial use to remove intrarow weeds and reduce cost of hand weeding. Intelligent cultivators currently on the market such as the Robovator, use pattern recognition to detect the crop row. These cultivators do not differentiate crops and weeds and do not work well among high weed populations. One approach to differentiate weeds is to place a machine-detectable mark or signal on the crop (i.e., the crop has the mark and the weed does not), thereby facilitating weed/crop differentiation. Lettuce and tomato plants were marked with labels and topical markers, then cultivated with an intelligent cultivator programmed to identify the markers. Results from field trials in marked tomato and lettuce found that the intelligent cultivator removed 90% more weeds from tomato and 66% more weeds from lettuce than standard cultivators without reducing yields. Accurate crop and weed differentiation described here resulted in a 45% to 48% reduction in hand-weeding time per hectare.

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Trajer, Jędrzej, Radosław Winiczenko, and Bogdan Dróżdż. "Analysis of Water Consumption in Fruit and Vegetable Processing Plants with the Use of Artificial Intelligence." Applied Sciences 11, no. 21 (October 29, 2021): 10167. http://dx.doi.org/10.3390/app112110167.

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Fruit and vegetable processing has a significant impact on the environment due to its consumption of a significant amount of water. Water consumption mainly depends on the type of production and the technology used. Water in fruit and vegetable processing plants is used as a raw material, an energy carrier, and in hydro transport, as well as for washing raw materials and maintaining production hygiene. The variety of technological operations carried out in the production process and the seasonality of production make it difficult to objectively assess the use of water in fruit and vegetable processing plants. Few available publications in this field provide numerical values of water unit consumption indices, with none entering into the cause-and-effect relationships of water use in plants in this industry. The aim of this study was to analyze the research to date and to verify the following research hypothesis: the structure of processing and the relationship between the weights of individual products have an impact on water consumption in fruit and vegetable processing plants. For this purpose, neural models of water consumption were developed for the largest agri-food processing plants in Poland that use similar technology. Water consumption was then optimized using genetic algorithms for the processing structure. The results confirmed the hypothesis that production structure has a significant impact on the rationalization of water consumption. The optimization results show that the production of concentrates, juices, and drinks has the greatest impact on water consumption. The lowest water consumption will be achieved when the production of concentrates is at a 2 to 1 ratio to the production of juices and drinks.

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Zhou, Mo, and Li Zhang. "Design of automatic spraying machine based on internet of things technology." ITM Web of Conferences 47 (2022): 01032. http://dx.doi.org/10.1051/itmconf/20224701032.

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In order to realize large-area automatic spraying of fruits and vegetables and improve the ability of intelligent spraying device to select the target of key diseases and pests, a new automatic target spraying device for fruits and vegetables is designed based on the Internet of things and intelligent monitoring technology. The new automatic spraying machine can be combined with the real-time monitoring data of diseases and pests, and the infrared scanning technology can be used to accurately spray the target and selectively spray the fruits and vegetables automatically. The single chip microcomputer is used to realize the automatic control of the whole system, and the real-time control can be combined with the mobile terminal software of the mobile phone. The device meets the needs of modern agriculture for pesticide spraying, has a wide range of applications in real life, and has significant practical significance.

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32

Carter, Sara, and Susan A. Shaw. "The British Vegetable Industry: Changing Marketing and the Role of Market Intelligence." British Food Journal 95, no. 10 (October 1993): 29–35. http://dx.doi.org/10.1108/00070709310048452.

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Jin, Xin, Kaixuan Zhao, Jiangtao Ji, Zhaomei Qiu, Zhitao He, and Hao Ma. "Design and experiment of intelligent monitoring system for vegetable fertilizing and sowing." Journal of Supercomputing 76, no. 5 (September 1, 2018): 3338–54. http://dx.doi.org/10.1007/s11227-018-2576-2.

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34

Shakya, Dr Subarna. "Analysis of Artificial Intelligence based Image Classification Techniques." Journal of Innovative Image Processing 2, no. 1 (March 26, 2020): 44–54. http://dx.doi.org/10.36548/jiip.2020.1.005.

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Анотація:

Time is an essential resource for everyone wants to save in their life. The development of technology inventions made this possible up to certain limit. Due to life style changes people are purchasing most of their needs on a single shop called super market. As the purchasing item numbers are huge, it consumes lot of time for billing process. The existing billing systems made with bar code reading were able to read the details of certain manufacturing items only. The vegetables and fruits are not coming with a bar code most of the time. Sometimes the seller has to weight the items for fixing barcode before the billing process or the biller has to type the item name manually for billing process. This makes the work double and consumes lot of time. The proposed artificial intelligence based image classification system identifies the vegetables and fruits by seeing through a camera for fast billing process. The proposed system is validated with its accuracy over the existing classifiers Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) and Discriminant Analysis (DA).

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35

Zhang, Yue Wang. "Research on Fuzzy Control in the Greenhouse for Humidity Monitoring System." Applied Mechanics and Materials 416-417 (September 2013): 904–8. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.904.

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Design a smart vegetable greenhouse humidity control system based on automatic computer-controlled, elaborated on the humidity of the system acquisition, humidity control system, heater control circuits and other system hardware design ideas to improve the system's control algorithm, using serial of computer to record. The simulation curve of the system has better control and tracking performance, high precision humidity control, also composed of two computer-controlled system and the host computer, to facilitate the centralized management of the production. Practice shows that the study design, vegetable greenhouses intelligent humidity control system for man-machine interface, easy operation, high degree of automation, low cost, with a good prospect and promotional value.

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36

Luo, Xiao Li. "Research on Color Vegetable Diseases in Greenhouse Image Pretreatment and Extraction Method." Advanced Materials Research 989-994 (July 2014): 3730–33. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3730.

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In order to realize the accurate identification of the image color vegetable diseases in Greenhouse, In this paper, by using computer image processing technology in the intelligent recognition of vegetable diseases in greenhouse leaf, First, the moving average method can effectively remove the noise by the image of R,G, B three channel gray level, Then separate three channel image using Sobel-operator and the 4 template based on the direction of gradient magnitude ,and then integrate the three channel edge ,it can overcome the shortcoming of traditional gray image information, and make up the deficiency of the traditional Sobel operator edge thinning geometric features.

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Tao, Yongting, Jun Zhou, Mingjun Wang, Na Zhang, and Yimeng Meng. "An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation." International Journal of Advanced Robotic Systems 14, no. 4 (July 1, 2017): 172988141772419. http://dx.doi.org/10.1177/1729881417724190.

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It is a challenging task to achieve rapid and stable grasping of fruit and vegetable without damages for the agricultural robot. From the point of view of which most of fruits and vegetables are viscoelastic material, the viscoelastic characteristic of tomato was analyzed based on Burgers model in this article to provide a reference for the robotic grasping. First, the real-time viscoelastic parameters estimation model based on back-propagation neural network was established. The 3-11-4 network structure was applied, where the grasping force, displacement, and time were input to the model to estimate four viscoelastic parameters. The relative error was less than 15% at the 0.2-s estimation and correlation coefficient of fitting could reach to 0.99. Then, the expression of plastic deformation was derived by analyzing the dynamic characteristic of tomato based on Burgers model and Gripper’s model during grasping. The minimum plastic deformation was taken as the condition to optimize the grasping speed and operation time. Finally, the result of simulation and experiment showed the feasibility of the method proposed in this article. This research can achieve the goal of reducing the grasping time of robots without damaging the fruit and provide a reference for robots grasping process optimization.

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Wang, Xiao Yan, Zhi Wen Zhou, and Tao Wu. "The Design of Greenhouse Temperature Control System." Applied Mechanics and Materials 599-601 (August 2014): 1111–14. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1111.

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Анотація:

A vegetable greenhouse temperature detection and control system is described in this paper. The system is based STC89C52 microcontroller, using DS18B20 temperature sensor to detect real-time temperature, and using fuzzy control algorithm to control greenhouse temperature. Experimental results show that the system is cost-effective, long life and high degree of intelligence, has some practical value.

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39

Morrow, C., P. Heinemann, H. Sommer, R. Crassweller, R. Cole, Y. Tao, Z. Varghese, and S. Deck. "AUTOMATED INSPECTION OF FRUITS AND VEGETABLES." HortScience 26, no. 6 (June 1991): 712B—712. http://dx.doi.org/10.21273/hortsci.26.6.712b.

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Research is described on the development of an automated inspection system which uses digital images and artificial intelligence techniques. Procedures have been developed for evaluating size, shape, and color of apples, potatoes, and mushrooms. Current emphasis is being placed on developing algorithms for detection of surface defects. A major effort will also be expended toward the development of an overall “quality” score for automated inspection of fruit and vegetables. The automated results are compared with those obtained using conventional manual inspection methods. Apples, potatoes, and mushrooms are the primary crops being inspected although the algorithms and techniques are applicable to many different fruits and vegetables. Color and monochromatic image processing components in “MS-DOS” and “Macintosh” computers are being used in this study.

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40

Li, Mingyong, Xin Jin, Jiangtao Ji, Pengge Li, and Xinwu Du. "Design and experiment of intelligent sorting and transplanting system for healthy vegetable seedlings." International Journal of Agricultural and Biological Engineering 14, no. 3 (2021): 208–16. http://dx.doi.org/10.25165/j.ijabe.20211404.6169.

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41

Wei, Qing Feng, Chang Shou Luo, Cheng Zhong Cao, and Qiang Guo. "The Intelligent Diagnostic System of Vegetable Diseases Based on a Fuzzy Neural Network." Applied Mechanics and Materials 321-324 (June 2013): 1907–11. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1907.

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To overcome the disadvantages that image analysis of agricultural disease diagnosis was not practical in the field, and the expert diagnosis system had an unsatisfied correct rate, a diagnostic model based on fuzzy rule and BP neural network (back propagation neural network) was constructed. The input vector in the model was formed by a unified description of symptoms using plant protection terms and combined with the membership. The intelligent diagnostic system of vegetable diseases based on the diagnostic model was developed by the mixed programming of Visual C # and Matlab. The test shows that the diagnostic correct rate of the system is 88.95%, and it has better fault tolerance and practical value.

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42

Ji, Jiang-Tao, Lin-Hui Yang, Xin Jin, Hao Ma, Jing Pang, Rong-Biao Huang, and Meng-Meng Du. "Design of intelligent transplanting system for vegetable pot seedling based on PLC control." Journal of Intelligent & Fuzzy Systems 37, no. 4 (October 25, 2019): 4847–57. http://dx.doi.org/10.3233/jifs-179322.

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43

Dewidar, A., F. Mohammad, H. Al-Ghobari, I. Sayed-Ahmed, and M. Metwally. "INTELLIGENT IRRIGATION IN VEGETABLE CROP (TOMATO): NOVEL APPROACH FOR WATER RESOURCE USE OPTIMIZATION." Journal of Soil Sciences and Agricultural Engineering 6, no. 12 (December 1, 2015): 1455–66. http://dx.doi.org/10.21608/jssae.2015.43934.

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44

Li, Hongwei, and Peng Pan. "Food Waste in Developed Countries and Cold Chain Logistics." E3S Web of Conferences 251 (2021): 03001. http://dx.doi.org/10.1051/e3sconf/202125103001.

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Food waste is a tough and profound question in the world. Although the development of agricultural technology has effectively promoted the increase of vegetable and fruit production, one-third of global vegetable and fruit production are still wasted. This issue is caused not only by food overproduction or overstock, but also by customers’ requirements for fresh products. This paper aims to thoroughly explore the reasons for food waste and provide some solutions to solve this problem, especially from the “Agri-fresh produce supply chain management” perspective. Solutions include improving the cold-chain logistics system and intelligent methods. To clearly analyze reasons for food wastey, this paper interprets the issue from three dimensions (customer, food supply chain, and farm) and then explores solutions.

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45

Costa, Fiammetta, Alessandra Amati, Manuela Antonelli, Giacomo Cocetta, Michele Di Mauro, Antonio Ferrante, Klaudia Krasojevic, et al. "Designing the Future: An Intelligent System for Zero-Mile Food Production by Upcycling Wastewater." Proceedings 2, no. 22 (November 21, 2018): 1367. http://dx.doi.org/10.3390/proceedings2221367.

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The project deals with the environmental problem of water consumption. The aim of this work is to experiment the recycling of dishwasher wastewater through its reuse in growing edible vegetables or ornamental plants; this can also accomplish the valorization of nutrients present in the wastewater. This new process allows to ensure washing functions coupled with vegetables production and to affect users’ environmental awareness and habits, following a user-centered system design approach to understand the users and involve them actively in the system development. The presented work is also aimed to experiment a multidisciplinary approach in order to face environmental problems.

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46

Harrington, Katharine, Shannon N. Zenk, Linda Van Horn, Lauren Giurini, Nithya Mahakala, and Kiarri N. Kershaw. "The Use of Food Images and Crowdsourcing to Capture Real-time Eating Behaviors: Acceptability and Usability Study." JMIR Formative Research 5, no. 12 (December 2, 2021): e27512. http://dx.doi.org/10.2196/27512.

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Background As poor diet quality is a significant risk factor for multiple noncommunicable diseases prevalent in the United States, it is important that methods be developed to accurately capture eating behavior data. There is growing interest in the use of ecological momentary assessments to collect data on health behaviors and their predictors on a micro timescale (at different points within or across days); however, documenting eating behaviors remains a challenge. Objective This pilot study (N=48) aims to examine the feasibility—usability and acceptability—of using smartphone-captured and crowdsource-labeled images to document eating behaviors in real time. Methods Participants completed the Block Fat/Sugar/Fruit/Vegetable Screener to provide a measure of their typical eating behavior, then took pictures of their meals and snacks and answered brief survey questions for 7 consecutive days using a commercially available smartphone app. Participant acceptability was determined through a questionnaire regarding their experiences administered at the end of the study. The images of meals and snacks were uploaded to Amazon Mechanical Turk (MTurk), a crowdsourcing distributed human intelligence platform, where 2 Workers assigned a count of food categories to the images (fruits, vegetables, salty snacks, and sweet snacks). The agreement among MTurk Workers was assessed, and weekly food counts were calculated and compared with the Screener responses. Results Participants reported little difficulty in uploading photographs and remembered to take photographs most of the time. Crowdsource-labeled images (n=1014) showed moderate agreement between the MTurk Worker responses for vegetables (688/1014, 67.85%) and high agreement for all other food categories (871/1014, 85.89% for fruits; 847/1014, 83.53% for salty snacks, and 833/1014, 81.15% for sweet snacks). There were no significant differences in weekly food consumption between the food images and the Block Screener, suggesting that this approach may measure typical eating behaviors as accurately as traditional methods, with lesser burden on participants. Conclusions Our approach offers a potentially time-efficient and cost-effective strategy for capturing eating events in real time.

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Przybył, Krzysztof, and Krzysztof Koszela. "Applications MLP and Other Methods in Artificial Intelligence of Fruit and Vegetable in Convective and Spray Drying." Applied Sciences 13, no. 5 (February 25, 2023): 2965. http://dx.doi.org/10.3390/app13052965.

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The seasonal nature of fruits and vegetables has an immense impact on the process of seeking methods that allow extending the shelf life in this category of food. It is observed that through continuous technological changes, it is also possible to notice changes in the methods used to examine and study food and its microbiological aspects. It should be added that a new trend of bioactive ingredient consumption is also on the increase, which translates into numerous attempts that are made to keep the high quality of those products for a longer time. New and modern methods are being sought in this area, where the main aim is to support drying processes and quality control during food processing. This review provides deep insight into the application of artificial intelligence (AI) using a multi-layer perceptron network (MLPN) and other machine learning algorithms to evaluate the effective prediction and classification of the obtained vegetables and fruits during convection as well as spray drying. AI in food drying, especially for entrepreneurs and researchers, can be a huge chance to speed up development, lower production costs, effective quality control and higher production efficiency. Current scientific findings confirm that the selection of appropriate parameters, among others, such as color, shape, texture, sound, initial volume, drying time, air temperature, airflow velocity, area difference, moisture content and final thickness, have an influence on the yield as well as the quality of the obtained dried vegetables and fruits. Moreover, scientific discoveries prove that the technology of drying fruits and vegetables supported by artificial intelligence offers an alternative in process optimization and quality control and, even in an indirect way, can prolong the freshness of food rich in various nutrients. In the future, the main challenge will be the application of artificial intelligence in most production lines in real time in order to control the parameters of the process or control the quality of raw materials obtained in the process of drying.

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Zhao, Xing Chen, Jin Qiang Li, Jian Liu, Quan Shi, and Ling Sun. "Design and Implementation of Large Computer Room Environment Intelligent Monitoring System Based on Zigbee." Applied Mechanics and Materials 397-400 (September 2013): 1673–76. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1673.

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For large network communication equipment are in need of real time computer maintenance and management, the traditional manual management can't meet this need, so this paper designs a set of real-time data acquisition, intelligent alarm and remote control, large computer room environment monitoring system based on Zigbee. The system is composed of terminal monitoring subsystem, control subsystem and management terminal. The terminal monitoring system consists of ZigBee node through the network, and its data acquisition include temperature, humidity and smoke environmental data; MSP430F1611 and Ethernet module monitoring subsystem are responsible for packet analysis, Ethernet connection, and Web intelligent remote switch control when monitoring data reaches a threshold. The system also can be used in street lamp remote intelligent control, intelligent irrigation control system of greenhouse vegetables and so on. Therefore, it has good application value.

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Zhao, Junling. "Robot Structural Optimization Based on Computer Intelligent Image." Wireless Communications and Mobile Computing 2022 (October 12, 2022): 1–6. http://dx.doi.org/10.1155/2022/3328986.

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In order to solve the problems of low degree of automation, difficult identification of picking objects, and large picking damage in traditional fruit and vegetable picking operations, the author proposes a robot structure optimization method based on computer intelligent images. This method introduces the computer imaging technology, examines the principle of imaging the computer imaging technology as the in-depth study of the computer imaging technology, completes the mechanical design of the selected robot, and optimizes all hardware models of choice robot. At the same time, the computer image acquisition system, image acquisition module, and execution module are designed; finally, the computer image information processing flow design of the picking robot is completed, and the simulation experiment of the picking robot is carried out. Experimental results show that in the experiments with 166, 142, and 165 tomato identification numbers, the identification accuracy rates were all over 96%. Conclusion. The picking robot based on computer images has a simple structure, high recognition accuracy of picking targets, less damage to the picking targets, high safety and stability, and great promotion value.

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Beshai, Heba, Gursimran Sarabha, Pranali Rathi, Arif Alam, and M. Deen. "Freshness Monitoring of Packaged Vegetables." Applied Sciences 10, no. 21 (November 9, 2020): 7937. http://dx.doi.org/10.3390/app10217937.

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Smart packaging is an emerging technology that has a great potential in solving conventional food packaging problems and in meeting the evolving packaged vegetables market needs. The advantages of using such a system lies in extending the shelf life of products, ensuring the safety and the compliance of these packages while reducing the food waste; hence, lessening the negative environmental impacts. Many new concepts were developed to serve this purpose, especially in the meat and fish industry with less focus on fruits and vegetables. However, making use of these evolving technologies in packaging of vegetables will yield in many positive outcomes. In this review, we discuss the new technologies and approaches used, or have the potential to be used, in smart packaging of vegetables. We describe the technical aspects and the commercial applications of the techniques used to monitor the quality and the freshness of vegetables. Factors affecting the freshness and the spoilage of vegetables are summarized. Then, some of the technologies used in smart packaging such as sensors, indicators, and data carriers that are integrated with sensors, to monitor and provide a dynamic output about the quality and safety of the packaged produce are discussed. Comparison between various intelligent systems is provided followed by a brief review of active packaging systems. Finally, challenges, legal aspects, and limitations facing this smart packaging industry are discussed together with outlook and future improvements.

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