Bibliografías temáticas / Intelligenza vegetale

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Literatura académica sobre el tema "Intelligenza vegetale"

Autor: Grafiati
Publicado: 10 de marzo de 2023

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Artículos de revistas sobre el tema "Intelligenza vegetale"

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Yang, Linlin, Yingluo Song, Zilin Hu, Aili Wang, Haibin Wu y Yuji Iwahori. "Design and Implementation of Intelligent Vegetable Recognition System based on MobileNet". Embedded Selforganising Systems 9, n.º 3 (21 de diciembre de 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 y Ruili Nan. "Vegetable Size Measurement Based on Stereo Camera and Keypoints Detection". Sensors 22, n.º 4 (18 de febrero de 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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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, n.º 1 (marzo de 2016): 100–101. http://dx.doi.org/10.1086/685354.

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Wang, Zhi Gang, Xiao Guang Chen y Wen Fu Wu. "Artificial Intelligence for the Control of Northeast Region Greenhouse Base on Biomass". Applied Mechanics and Materials 48-49 (febrero de 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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Tata, Jagannadha Swamy, Naga Karthik Varma Kalidindi, Hitesh Katherapaka, Sharath Kumar Julakal y Mohan Banothu. "Real-Time Quality Assurance of Fruits and Vegetables with Artificial Intelligence". Journal of Physics: Conference Series 2325, n.º 1 (1 de agosto de 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. y S. R. PADMA. "RELATIONSHIP ANALYSIS BETWEEN MARKETING BEHAVIOUR AND PROFILE OF VEGETABLE GROWERS OF RANGA REDDY DISTRICT". JOURNAL OF RESEARCH ANGRAU 50, n.º 4 (31 de diciembre de 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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Liu, Jun. "Tomato Yield Estimation Based on Object Detection". Journal of Advanced Computational Intelligence and Intelligent Informatics 22, n.º 7 (20 de noviembre de 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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Changdar, Chiranjit, Pravash Kumar Giri, Rajat Kumar Pal, Alok Haldar, Samiran Acharyya, Debasis Dhal, Moumita Khowas y 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, n.º 05 (octubre de 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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Fedosov, A. Yu y A. M. Menshikh. "Precision farming technologies in vegetable growing". Vegetable crops of Russia, n.º 6 (7 de diciembre de 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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Dou, Dandan. "Sensor Vegetable Greenhouse and Agricultural Product Supply Chain Management Based on Improved Neural Network". Mobile Information Systems 2022 (18 de agosto de 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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Tesis sobre el tema "Intelligenza vegetale"

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Luwes, Nicolaas Johannes. "Artificial intelligence machine vision grading system". Thesis, Bloemfontein : Central University of Technology, Free State, 2014. http://hdl.handle.net/11462/35.

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VIOLA, ALESSANDRA. "Vegeto ergo sum. Metodo e strumenti per la divulgazione scientifica delle ricerche sull’intelligenza vegetale". Doctoral thesis, 2014. http://hdl.handle.net/2158/916730.

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In che modo è possibile comunicare al grande pubblico le ricerche sull'intelligenza vegetale? E come si può, più in generale, diffondere l'idea che le piante sono intelligenti? Questa tesi propone uno studio teorico-pratico per individuare le più efficaci tecniche di comunicazione e metterle alla prova utilizzando i media nazionali, un approfondimento del tema con ricerche storiche e scientifiche, l'analisi del contesto comunicativo italiano e degli strumenti disponibili ed infine la sperimentazione di alcune ipotesi comunicative insieme al vaglio dei risultati con esse ottenuti. Il tutto per tracciare la strada a possibili ulteriori approfondimenti e all'applicazione del metodo ad altri campi e fornire un'ipotesi metodologica valida non solo per la divulgazione scientifica, ma per la collaborazione tra saperi diversi in ambito comunicativo. Tra gli esiti più interessanti di questo lavoro, un insieme di proposte e di strumenti per la comunicazione che rimarranno in dotazione all'università.
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Chen, Chia-Tseng y 陳加增. "Analysis of Nitrogen Content in Vegetables Using Intelligent Spectral Information". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/51886541067671038570.

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博士
臺灣大學
生物產業機電工程學研究所
95
Using spectral remote sensing to monitor the physiological status during growth has been attempted in the recent studies. In the work, the near infrared spectrophotometer (NIRS 6500, FOSS NIRSystems Inc.) and the hyper-spectral imaging system developed in this study were used to measure and analyze the reflectance spectra of vegetables in order to provide the basis for the future development of the on-line non-destructive remote sensing system for monitoring the nitrogen content of vegetable crops. The typical calibration models, including step-wise multilinear regression (SMLR) and modified partial least square regression (MPLSR), were adopted to examine the prediction performance of plant nitrogen content by using the spectral data firstly. Furthermore, the machine learning algorithms, including artificial neural network (ANN), real genetic algorithm (RGA), and information entropy (IE), were adopted to develop the intelligence-based calibration models to improve the prediction accuracy of calibration models. In the first part of this dissertation, 113 samples of Chinese mustard (Brassica rapa L. var. chinensis (Rupr.) Olsson) were cultured by three different nitrogen fertilization treatments, and the reflectance spectra of leaves in terms of powder form were used to develop the calibration models. The results show that derivative treatments can reduce the noises of spectral shift caused by the particle sizes, and the significant wavelengths with high correlation coefficient ( |r| > 0.9 ) appear in the selected significant spectral band (1400-2450 nm). Regarding the nitrogen prediction accuracy, the SMLR model with smooth and first derivative pre-treatments and four significant wavelengths (2124, 2240, 1666, and 1632 nm) gives the best results (SEC = 2.059 mg/g, rc = 0.991, SEV = 2.131 mg/g, rv = 0.990). The results point out the SMLR model with a few wavelengths as inputs can be better than MPLSR model when spectral information is without water absorbance interference. Moreover, the SMLR model could be used to replace the time-consuming wet chemical method, such as Kjeldahl method, to analyze the nitrogen content in vegetable leaves. The results also indicate that a hyper-spectral imaging system, constructed of silicon CCD cameras and liquid crystal tunable filters (LCTF) using MPLSR method with the smooth and second derivative spectral information in range of 450 to1000 nm, could be used as the aids for nitrogen fertilization management of vegetable growth in the field. In the second part, fresh leaves of cabbage seedlings (Brassica oleracea L.) after fertilizations with 5 different concentrations are used to measure the reflectance absorbance spectra. To develop a multi-spectral imaging system for remote sensing of the nitrogen content of crops, the significant wavelengths and calibration models were carefully evaluated in this study. The significant wavelengths in full band (400-2500 nm) and a selected band (450-950 nm), which is suitable for silicon CCD cameras, were investigated. Significant wavelengths for estimating nitrogen content of cabbage seedling leaves were first determined by SMLR analysis. A proposed ANN model with cross-learning scheme (ANN-CL) was further developed to increase the prediction accuracy. To comply with the design of a practical multi-spectral imaging system using silicon CCD cameras and commercially available bandpass filters, an ANN-CL model with four inputs of spectral absorbance at 490, 570, 600, and 680 nm was developed. The calibration results (rc = 0.93, SEC = 0.873%, and SEV = 0.960%) reduce the SEV about 15% when compared with the SMLR method with four wavelengths (SEV = 1.099%). In addition, the results are comparable to that of SMLR with seven wavelengths (rc = 0.94, SEC = 0.806%, and SEV = 0.993%) in the full band. These results indicate that the ANN model with cross-learning using spectral information at 490, 570, 600, and 680 nm could be used to develop a practical remote sensing system to predict nitrogen content of cabbage seedlings. In the third part, the self-developed hyper-spectral imaging system, constructed from two sets of CCD cameras and liquid crystal tunable filters (LCTF, VIS and VNIR), were used to grab the spectral images of cabbage seedlings in the wavelength band of 410-1090 nm. In the analysis of hyper-spectral images, the region of seedling canopy was precisely extracted by image segmentation, which was dealt with a simply binary procedure, due to the fine spatial resolution of images. To calibrate and transfer the gray values of seedling canopy to the reflectance absorption, the six standard gray-blocks were used. The first and second significant wavelengths, analyzed by the information entropy (IE) index, are 650 nm and 690 nm, which are mutual different to the linear correlation (LC) analysis between nitrogen content and spectral data. The third significant wavelength of IE analysis is 530 nm, which is similar to 520 nm of LC. However, the fourth significant wavelength of LC is 470 nm, whose index value of IE is less than the wavelengths of 760 nm and 900 nm. The significant wavelengths of IE analysis are including 650, 690, 520, 760, and 900 nm. In the results of hyper-spectral calibration model analysis by using raw spectral data, MPLSR with six factors reduces the values of SEC and SECV to 6.20 mg/g and 7.64 mg/g respectively. Besides, the SMLR with three significant wavelengths (470, 1080, and 710 nm) gives the best results (SEC=7.55 mg/g, SEC=8.13 mg/g) by using simply linear equation. The different significant wavelengths sets of LC, IE and SMLR are used as input data of the intelligence-based calibration models of RGA and ANN-CL to improve the prediction accuracy of nitrogen content analysis. Regarding the RGA analysis, the genetic population was generated randomly and the best fitness genetic population was kept to generate the next generation by crossover and mutation, and the global minimum of error was achieved. Therefore, the RGA calibration model with five significant wavelengths set (650, 690, 520, 760, and 900 nm) of IE is obtained with the good prediction results (SEV=7.79 mg/g). Moreover, the intelligence-based calibration model of ANN-CL with 3/4 sample selection ratio of the calibration set, using the same significant wavelengths set of RGA model, reduces the SEC to 6.47 mg/g and SEV to 5.76 mg/g effectively. As a conclusion, the study has successfully developed nitrogen content prediction models using multi-spectra data of vegetable crops by integrating the near infrared, spectral images technology and artificial intelligence algorithms. With these research results, the remote sensing system with a multi-spectral imager could be developed for monitoring the nitrogen status of greenhouse crops in the future. The information of crops nitrogen status is useful for the precision management of nitrogen fertilization.
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4

CHEN, YU-MING y 陳玉明. "Applying ARIMA and Computational Intelligence Approaches to the Predictions for the Price of Three Major Vegetable Oils". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/76g4ux.

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碩士
輔仁大學
統計資訊學系應用統計碩士在職專班
107
Vegetable oil is one of the indispensable substances in our lives and becomes much more important commodities in the international market. In recent years, the price of vegetable oil has been attracting worldwide attention due to the reason that many foods are mixed with inferior oil and result in food safety problems. With the economic development, the varieties and quantities of imported and exported vegetable oils are increasing year by year. Therefore, accurate prediction of the price of the three major vegetable oils is extremely important in dealing with risks and uncertainties to which farmers, enterprises, government organizations and investors have to face. The most widely used predictive model of vegetable oil price is the autoregressive integrated moving average model (ARIMA). The ARIMA model presents the result of simplicity and the generation of linear time series data. However, the disadvantage of ARIMA modeling is that the construction of the model is based only on a linear structure. In order to cover the construction of nonlinear models, this study was collected the data of the past prices of three major vegetable oils from 2005 to 2018 in the Department of Agriculture in USA, applying three kinds of computational intelligence methods: artificial neural network (ANN), support vector regression (SVR) and extreme learning machines (ELM). Establish predictive models and comparing the performance of each predictive model. The study found that using computational intelligence to predict the price of the three major vegetable oil futures is better than using the time series ARIMA method itself.
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5

Lee, Yi-Chi y 李宜錡. "Integration of Game and Multi-Agent Theory on Intelligent Recommendation System of Organic Vegetables Planting". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/90514166662004915888.

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碩士
育達商業技術學院
資訊管理所
96
Because the people are fastidious more and more regarding the diet healthy demand, therefore has accomplished the organic agriculture starting. But the majority farmers when are engaged in the organic planter, does the regular session faced with - what need to plant to a question to be only then good. Therefore, how effectiveness, and chooses the appropriate planter crops precisely, is direction which is worth discussing. In addition, in all appropriate planter's crops, considered that its relative economic value, takes in various seasons should basis of the priority selection, achieves goal of the entire year planter most greatly economic profit, is also another ponder topic. Taking organic vegetables farming as an example, this research uses knowledge-based and rule-based methods, while applying the game theory and multi-agent theory, this study develops a set of graphic intellectual suggestion mechanism with ASP.NET and MS-SQL. In the first stage of the study, we apply the knowledge base and the rule base composed for this study, we filter the suitable crops for each season, and order the list of crops in the order of suitability before we propose the planting suggestion for the entire year. Next, we design a realistic game theory and multi-agent theory to operate a negotiation process for a more effective system, which considers the organic plantations’ affect between each crop and the limitation of the system, as well as the crop shifting cost. In the end, we construct a multi-agent game theory of negotiation in order to analyze the maximum profit and propose a one year with a maximum profit. In this system, a merge of game theory and multi-agent system has been tested and verified to give suggestions that are 84.25% as effective, compared to the suggestions provided by human professionals. Other than this, the system’s greatest contribution is that, the mechanism may act as a front system of e-learning application, thus increase the level of the organic farming techniques. This research because of knowledge library and pattern union breaks original carries on the appraisal, the decision scheme recommendation system model purely. Besides domain knowledge knowledge library and model let the user in face several possibilities in the choices, provides is more objective a more effective suggestion. In the furture, it is expected to be applied to other crops planting suggestion.
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Shen, Zhenliang. "Colour differentiation in digitial images". Thesis, 2003. https://vuir.vu.edu.au/15529/.

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To measure the quality of green vegetables in digital images, the colour appearance of the vegetable is one of the main factors. In general, green colour represents good quality and yellow colour represents poor quality empirically for green-vegetable. The colour appearance is mainly determined by its hue, however, the value of brightness and saturation affects the colour appearance under certain conditions. To measure the colour difference between green and yellow, a series of experiments have been designed to measure the colour difference under varying conditions. Five people were asked to measure the colour differences in different experiments. First, colour differences are measured as two of the values hue, brightness, and saturation are kept constant. Then, the previous results are applied to measure the colour difference as one of the values hue, brightness, and saturation is kept constant. Lastly, we develop a colour difference model from the different values of hue, brightness, and saturation. Such a colour difference model classifies the colours between green and yellow. A windows application is designed to measure the quality of leafy vegetables by using the colour difference model. The colours of such vegetables are classified to represent different qualities. The measurement by computer analysis conforms to that produced by human inspection.
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Libros sobre el tema "Intelligenza vegetale"

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L, Wilson Charles, ed. Intelligent and active packaging for fruits and vegetables. Boca Raton: Taylor & Francis, 2007.

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Ryan, John C., Monica Gagliano y Patrícia Vieira. Mind of Plants: Narratives of Vegetal Intelligence. Synergetic Press, 2021.

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Ryan, John C., Monica Gagliano y Patrícia Vieira. Mind of Plants: Narratives of Vegetal Intelligence. Synergetic Press, 2021.

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Charles L. Wilson Ph.D. Intelligent and Active Packaging for Fruits and Vegetables. CRC, 2007.

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Wilson, Charles L. Intelligent and Active Packaging for Fruits and Vegetables. Taylor & Francis Group, 2010.

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Wilson, Charles L. Intelligent and Active Packaging for Fruits and Vegetables. Taylor & Francis Group, 2007.

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Wilson, Charles L. Intelligent and Active Packaging for Fruits and Vegetables. Taylor & Francis Group, 2007.

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Griffith, Teresa. Intelligence is Everywhere - Looking at Animals, Vegetables, and Minerals. Teresa Griffith, 2017.

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Smith, Chris. Security and Stability in South Asia: A Survey and Analysis for the Intelligence and Investment Sectors (Jane's Special Reports). Jane's Information Group, 1996.

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Market intelligence: Lifestyle trends, canned vegetables, fortified wine, contraceptives, overcoats and raincoats, packaging. London: Mintel, 1985.

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Capítulos de libros sobre el tema "Intelligenza vegetale"

1

Ahrary, Alireza y D. A. R. Ludena. "A Cloud-Based Vegetable Production and Distribution System". En Intelligent Decision Technologies, 11–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19857-6_2.

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Habib, Md Tarek, Md Ariful Islam Arif, Sumaita Binte Shorif, Mohammad Shorif Uddin y Farruk Ahmed. "Machine Vision-Based Fruit and Vegetable Disease Recognition: A Review". En Algorithms for Intelligent Systems, 143–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6424-0_10.

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Jana, Susovan, Ranjan Parekh y Bijan Sarkar. "Detection of Rotten Fruits and Vegetables Using Deep Learning". En Algorithms for Intelligent Systems, 31–49. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6424-0_3.

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Voronin, Viktor, Oksana Larsen, Dmitry Zamelin y Nikolay Mikhailov. "Structure and Properties of Decorative Concrete Impregnated with Vegetable Oil". En Advances in Intelligent Systems and Computing, 761–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19756-8_72.

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Wei, Qingfeng, Changshou Luo, Jun Yu, Xuezhong Chen y Sufen Shun. "Vegetable Technology Information Visual Service System Based on Knowledge Map". En Advances in Intelligent, Interactive Systems and Applications, 566–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02804-6_74.

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6

Ikeda, Makoto, Yuki Sakai, Tetsuya Oda y Leonard Barolli. "Performance Evaluation of a Vegetable Recognition System Using Caffe and Chainer". En Advances in Intelligent Systems and Computing, 24–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61566-0_3.

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7

Suddapalli, Sai Raghunandan y Perugu Shyam. "Using Mask-RCNN to Identify Defective Parts of Fruits and Vegetables". En Intelligent Human Computer Interaction, 637–46. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98404-5_58.

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8

Yang, Xiu-Hong, Ji-Xiang Du, Hong-Bo Zhang y Wen-Tao Fan. "Fine-Grained Recognition of Vegetable Images Based on Multi-scale Convolution Neural Network". En Intelligent Computing Theories and Application, 67–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_9.

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9

Maqrot, Sara, Simon de Givry, Gauthier Quesnel y Marc Tchamitchian. "A Mixed Integer Programming Reformulation of the Mixed Fruit-Vegetable Crop Allocation Problem". En Advances in Artificial Intelligence: From Theory to Practice, 237–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60045-1_26.

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Shinde, Ranjeet, Victor Rodov, Shanthanu Krishnakumar y Jayasankar Subramanian. "Active and Intelligent Packaging for Reducing Postharvest Losses of Fruits and Vegetables". En Postharvest Biology and Nanotechnology, 171–89. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119289470.ch7.

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Actas de conferencias sobre el tema "Intelligenza vegetale"

1

Sun, Gengchen, Yueyang Li, Xiaofei Cheng, Haoxiang Liu, Quanzhuang Zhou y Wenyuan Song. "Intelligent family balcony vegetable garden based on embedded system". En International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), editado por Zhisheng Zhang y Jianxiong Zhu. SPIE, 2022. http://dx.doi.org/10.1117/12.2652078.

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Agrawal, Yash, Bhavesh Jain, Saurabh Kumar Mishra y S. Indu. "Android Application for Vegetable and Fruit Classification". En 2021 International Conference on Intelligent Technologies (CONIT). IEEE, 2021. http://dx.doi.org/10.1109/conit51480.2021.9498517.

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Bai, Qiuchan y Chunxia Jin. "The Remote Monitoring System of Vegetable Greenhouse". En 2017 10th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2017. http://dx.doi.org/10.1109/iscid.2017.13.

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4

Zhu, Changfeng y Qing-rong Wang. "Technical Measures of Fruit and Vegetable Transportation". En 2010 International Conference on Logistics Engineering and Intelligent Transportation Systems (LEITS). IEEE, 2010. http://dx.doi.org/10.1109/leits.2010.5664997.

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5

Krishna, S. Sai, K. Sai Chandra Manoj, N. Sri Sarada y T. Satyanarayana. "Generation of biological energy using vegetable waste". En 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2015. http://dx.doi.org/10.1109/isco.2015.7282348.

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6

Yu, Yongqing, Yishan Zou y Yu Sun. "An Intelligent Mobile Application to Automate the Analysis of Food Calorie using Artificial Intelligence and Deep Learning". En 2nd International Conference on Machine Learning Techniques and NLP (MLNLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111428.

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As obesity becomes increasingly common worldwide [9], more and more people want to lose weight – for both their health and their image. According to the Centers for Disease Control and Prevention (CDC), long-term changes in daily eating habits (such as regarding food/nutrition type, calorie intake) are successful at keeping weights off [10]. Therefore, it would be helpful to have an AI mobile program that identifies the types of food the user consumes and automatically calculates the total calories. This paper examines the development and optimization of an 11-categorical food classification model based on the MobileNet neural network using Python. Specifically, it classifies any food image as one of bread, dairy, dessert, egg product, fried food, meat, noodles, rice, seafood, soup, or fruit/vegetables. Methods of optimization include data preprocessing and learning rate and batch size adjustments. Experimental results show that scaling image inputs to standard size (Python Numpy resize() function), 300 training epochs, dynamic learning rate (start with 0.001 and *0.1 for every 30 epochs), and a batch size of 16 yields our best model of 83.44% accuracy.
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7

Yue, Jun, Zhenbo Li y Zetian Fu. "Ontology-based Vegetable Supply Chain Knowledge Searching System". En Sixth International Conference on Intelligent Systems Design and Applications]. IEEE, 2006. http://dx.doi.org/10.1109/isda.2006.211.

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8

Gulin, S. V. y A. G. Pirkin. "RE-ENGINEERING OF BUSINESS PROCESSES IN CREATION AND OPERATION OF ELECTROTECHNOLOGICAL SYSTEMS IN THE AGRICULTURAL SECTOR OF ECONOMY". En STATE AND DEVELOPMENT PROSPECTS OF AGRIBUSINESS Volume 2. DSTU-Print, 2020. http://dx.doi.org/10.23947/interagro.2020.2.525-529.

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The article proposes a universal methodology of business reengineering in solving the problems of designing, creating and operating electrotechnological systems using modern energy-saving lighting equipment. This methodology has been tested on the example of redesigning the processes of creating and operating irradiation plants for new generation greenhouses with an intelligent re-illumination system that allows growing vegetables year-round.
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9

Chen, Yaoyao, Zijun Yang, Shi Zhou, Aoran Xi, Yuting Wang y Zhang Lifeng. "Design of Control System for Vegetable Soilless Culture". En The 9th International Conference on Intelligent Systems and Image Processing 2022. The Institute of Industrial Applications Engineers, 2022. http://dx.doi.org/10.12792/icisip2022.025.

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10

Duth, P. Sudharshan y K. Jayasimha. "Intra Class Vegetable Recognition System using Deep Learning". En 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2020. http://dx.doi.org/10.1109/iciccs48265.2020.9121164.

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