Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/230038
Title: Soft Computing Application in Precision Agriculture
Researcher: Sarkate Rajesh Sukhdevrao
Guide(s): Khanale P. B.
Keywords: Computer Science,Automation and Control Systems
University: Swami Ramanand Teerth Marathwada University
Completed Date: 04/01/2018
Abstract: Computer based technologies have made great impact on the methodologies newlineused in many real world sectors. Many areas like medical, agriculture are highly newlinerelying on computers for data collection and analysis to engender fruitful information newlinethat can be used in decision making and reforms in management practices in relate newlinedomain. Chapter 1 introduces computer vision and soft computing concepts. It also newlinedescribes precision agriculture, a modern paradigm used in agriculture, considers newlineentire farm as collection of small units and find irregularities present in production newlineand requirements for those units. Ultimate goal of PA is to reduce overlay for newlinecultivation to increase profit. newlinePresent study is conducted to implement computer vision and soft computing newlinetechnologies for production estimation in floriculture. Real world Polyhouse scenes newlineare digitized using digital camera and used in the study. Study has designed and newlinedeveloped object detection, classification and quality assessment systems for Gerbera newlineflowers using various algorithms from mentioned technologies. In chapter 2, object newlinedetection system target to detect object of interests i.e. matured and immature flowers newlinefrom grayscale and color images using histogram slicing, threshold segmentation, newlineunsupervised clustering and soft computing artificial neural network technology. newlineIn chapter 3, detected objects are sorted into grown flowers and immature newlinebuds to determine current and advance production of flowers with classifiers based on newlineback propagation neural network. Different classifiers on color, size and shape newlinefeatures are designed to classify objects into desired output classes. newlineChapter 4 gives quality assessment system that implements Fuzzy Inference newlineSystem to grade Gerbera flower based on flower head size, stem length and stem newlinestrength parameters. Digital Image Processing methods are used to extract these newlineparameters from the digital image of selected Gerbera flower. All extracted newlineparameters are then passed through fuzzification, inferenc
Pagination: 191p
URI: http://hdl.handle.net/10603/230038
Appears in Departments:School of Computational Sciences

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01_title.pdfAttached File18.41 kBAdobe PDFView/Open
02_certificate.pdf16.48 kBAdobe PDFView/Open
03_abstract.pdf8.12 kBAdobe PDFView/Open
04_declaration.pdf5.39 kBAdobe PDFView/Open
05_acknowledgement.pdf8.47 kBAdobe PDFView/Open
06_contents.pdf11.32 kBAdobe PDFView/Open
07_list_of_tables.pdf8.57 kBAdobe PDFView/Open
08_list_of_figures.pdf15.31 kBAdobe PDFView/Open
09_abbreviations.pdf5.91 kBAdobe PDFView/Open
10_chapter 1.pdf1.64 MBAdobe PDFView/Open
11_chapter 2.pdf2.19 MBAdobe PDFView/Open
12_chapter 3.pdf1.06 MBAdobe PDFView/Open
13_chapter 4.pdf1.27 MBAdobe PDFView/Open
14_chapter 5.pdf7.45 MBAdobe PDFView/Open
15_conclusions.pdf14.12 kBAdobe PDFView/Open
16_summary.pdf29.54 kBAdobe PDFView/Open
17_bibliography.pdf133.31 kBAdobe PDFView/Open


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