Academic literature on the topic 'Intelligenza vegetale'
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Journal articles on the topic "Intelligenza vegetale"
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.
Full textZheng, 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.
Full textVan 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.
Full textWang, 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.
Full textTata, 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.
Full textANUSHA, 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.
Full textLiu, 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.
Full textChangdar, 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.
Full textFedosov, 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.
Full textDou, 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.
Full textDissertations / Theses on the topic "Intelligenza vegetale"
Luwes, Nicolaas Johannes. "Artificial intelligence machine vision grading system." Thesis, Bloemfontein : Central University of Technology, Free State, 2014. http://hdl.handle.net/11462/35.
Full textVIOLA, 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.
Full textChen, Chia-Tseng, and 陳加增. "Analysis of Nitrogen Content in Vegetables Using Intelligent Spectral Information." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/51886541067671038570.
Full text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.
CHEN, YU-MING, and 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.
Full text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.
Lee, Yi-Chi, and 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.
Full text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.
Shen, Zhenliang. "Colour differentiation in digitial images." Thesis, 2003. https://vuir.vu.edu.au/15529/.
Full textBooks on the topic "Intelligenza vegetale"
L, Wilson Charles, ed. Intelligent and active packaging for fruits and vegetables. Boca Raton: Taylor & Francis, 2007.
Find full textRyan, John C., Monica Gagliano, and Patrícia Vieira. Mind of Plants: Narratives of Vegetal Intelligence. Synergetic Press, 2021.
Find full textRyan, John C., Monica Gagliano, and Patrícia Vieira. Mind of Plants: Narratives of Vegetal Intelligence. Synergetic Press, 2021.
Find full textCharles L. Wilson Ph.D. Intelligent and Active Packaging for Fruits and Vegetables. CRC, 2007.
Find full textWilson, Charles L. Intelligent and Active Packaging for Fruits and Vegetables. Taylor & Francis Group, 2010.
Find full textWilson, Charles L. Intelligent and Active Packaging for Fruits and Vegetables. Taylor & Francis Group, 2007.
Find full textWilson, Charles L. Intelligent and Active Packaging for Fruits and Vegetables. Taylor & Francis Group, 2007.
Find full textGriffith, Teresa. Intelligence is Everywhere - Looking at Animals, Vegetables, and Minerals. Teresa Griffith, 2017.
Find full textSmith, 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.
Find full textMarket intelligence: Lifestyle trends, canned vegetables, fortified wine, contraceptives, overcoats and raincoats, packaging. London: Mintel, 1985.
Find full textBook chapters on the topic "Intelligenza vegetale"
Ahrary, Alireza, and D. A. R. Ludena. "A Cloud-Based Vegetable Production and Distribution System." In Intelligent Decision Technologies, 11–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19857-6_2.
Full textHabib, Md Tarek, Md Ariful Islam Arif, Sumaita Binte Shorif, Mohammad Shorif Uddin, and Farruk Ahmed. "Machine Vision-Based Fruit and Vegetable Disease Recognition: A Review." In Algorithms for Intelligent Systems, 143–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6424-0_10.
Full textJana, Susovan, Ranjan Parekh, and Bijan Sarkar. "Detection of Rotten Fruits and Vegetables Using Deep Learning." In Algorithms for Intelligent Systems, 31–49. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6424-0_3.
Full textVoronin, Viktor, Oksana Larsen, Dmitry Zamelin, and Nikolay Mikhailov. "Structure and Properties of Decorative Concrete Impregnated with Vegetable Oil." In 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.
Full textWei, Qingfeng, Changshou Luo, Jun Yu, Xuezhong Chen, and Sufen Shun. "Vegetable Technology Information Visual Service System Based on Knowledge Map." In 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.
Full textIkeda, Makoto, Yuki Sakai, Tetsuya Oda, and Leonard Barolli. "Performance Evaluation of a Vegetable Recognition System Using Caffe and Chainer." In 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.
Full textSuddapalli, Sai Raghunandan, and Perugu Shyam. "Using Mask-RCNN to Identify Defective Parts of Fruits and Vegetables." In Intelligent Human Computer Interaction, 637–46. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98404-5_58.
Full textYang, Xiu-Hong, Ji-Xiang Du, Hong-Bo Zhang, and Wen-Tao Fan. "Fine-Grained Recognition of Vegetable Images Based on Multi-scale Convolution Neural Network." In Intelligent Computing Theories and Application, 67–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_9.
Full textMaqrot, Sara, Simon de Givry, Gauthier Quesnel, and Marc Tchamitchian. "A Mixed Integer Programming Reformulation of the Mixed Fruit-Vegetable Crop Allocation Problem." In 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.
Full textShinde, Ranjeet, Victor Rodov, Shanthanu Krishnakumar, and Jayasankar Subramanian. "Active and Intelligent Packaging for Reducing Postharvest Losses of Fruits and Vegetables." In Postharvest Biology and Nanotechnology, 171–89. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119289470.ch7.
Full textConference papers on the topic "Intelligenza vegetale"
Sun, Gengchen, Yueyang Li, Xiaofei Cheng, Haoxiang Liu, Quanzhuang Zhou, and Wenyuan Song. "Intelligent family balcony vegetable garden based on embedded system." In International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), edited by Zhisheng Zhang and Jianxiong Zhu. SPIE, 2022. http://dx.doi.org/10.1117/12.2652078.
Full textAgrawal, Yash, Bhavesh Jain, Saurabh Kumar Mishra, and S. Indu. "Android Application for Vegetable and Fruit Classification." In 2021 International Conference on Intelligent Technologies (CONIT). IEEE, 2021. http://dx.doi.org/10.1109/conit51480.2021.9498517.
Full textBai, Qiuchan, and Chunxia Jin. "The Remote Monitoring System of Vegetable Greenhouse." In 2017 10th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2017. http://dx.doi.org/10.1109/iscid.2017.13.
Full textZhu, Changfeng, and Qing-rong Wang. "Technical Measures of Fruit and Vegetable Transportation." In 2010 International Conference on Logistics Engineering and Intelligent Transportation Systems (LEITS). IEEE, 2010. http://dx.doi.org/10.1109/leits.2010.5664997.
Full textKrishna, S. Sai, K. Sai Chandra Manoj, N. Sri Sarada, and T. Satyanarayana. "Generation of biological energy using vegetable waste." In 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2015. http://dx.doi.org/10.1109/isco.2015.7282348.
Full textYu, Yongqing, Yishan Zou, and Yu Sun. "An Intelligent Mobile Application to Automate the Analysis of Food Calorie using Artificial Intelligence and Deep Learning." In 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.
Full textYue, Jun, Zhenbo Li, and Zetian Fu. "Ontology-based Vegetable Supply Chain Knowledge Searching System." In Sixth International Conference on Intelligent Systems Design and Applications]. IEEE, 2006. http://dx.doi.org/10.1109/isda.2006.211.
Full textGulin, S. V., and A. G. Pirkin. "RE-ENGINEERING OF BUSINESS PROCESSES IN CREATION AND OPERATION OF ELECTROTECHNOLOGICAL SYSTEMS IN THE AGRICULTURAL SECTOR OF ECONOMY." In STATE AND DEVELOPMENT PROSPECTS OF AGRIBUSINESS Volume 2. DSTU-Print, 2020. http://dx.doi.org/10.23947/interagro.2020.2.525-529.
Full textChen, Yaoyao, Zijun Yang, Shi Zhou, Aoran Xi, Yuting Wang, and Zhang Lifeng. "Design of Control System for Vegetable Soilless Culture." In 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.
Full textDuth, P. Sudharshan, and K. Jayasimha. "Intra Class Vegetable Recognition System using Deep Learning." In 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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