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" Artificial Intelligence for Biology and Agriculture "
edited by S. Panigrahi, K.C. Ting.
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BL
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| Record Number
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728714
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b548466
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| Main Entry
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edited by S. Panigrahi, K.C. Ting.
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| Title & Author
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Artificial Intelligence for Biology and Agriculture\ edited by S. Panigrahi, K.C. Ting.
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| Publication Statement
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Dordrecht: Springer Netherlands, 1998
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| Page. NO
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(iv, 262 pages)
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| ISBN
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9401061203
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: 9401150486
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: 9789401061209
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: 9789401150484
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| Contents
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End-Effectors for Tomato Harvesting Robot --;A Computer Vision Method for Determining Length of Cheese Shreds --;Automated Modelling of Physiological Processes During Postharvest Distribution of Agricultural Products --;A Neuro-Fuzzy Approach to Identify Lettuce Growth and Greenhouse Climate --;Artificial Keys for Botanical Identification using a Multilayer Perceptron Neural Network (MLP) --;Video Grading of Oranges in Real-Time --;Cell Migration Analysis after In Vitro Wounding Injury with a Multi-Agent Approach --;Col or Computer Vision and Artificial Neural Networks for the Detection of Defects in Poultry Eggs --;Automatic Plankton Image Recognition --;Identification and Measurement of Convolutions in Cotton Fiber Using Image Analysis --;Fuzzy Logic for Biological and Agricultural Systems --;Robotics for Plant Production --;Three-Dimensional Image Reconstruction Procedure for Food Microstructure Evaluation.
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| Abstract
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This volume contains a total of thirteen papers covering a variety of AI topics ranging from computer vision and robotics to intelligent modeling, neural networks and fuzzy logic. There are two general articles on robotics and fuzzy logic. The article on robotics focuses on the application of robotics technology in plant production. The second article on fuzzy logic provides a general overview of the basics of fuzzy logic and a typical agricultural application of fuzzy logic. The article `End effectors for tomato harvesting' enhances further the robotic research as applied to tomato harvesting. The application of computer vision techniques for different biological/agricultural applications, for example, length determination of cheese threads, recognition of plankton images and morphological identification of cotton fibers, depicts the complexity and heterogeneities of the problems and their solutions. The development of a real-time orange grading system in the article `Video grading of oranges in real-time' further reports the capability of computer vision technology to meet the demand of high quality food products. The integration of neural network technology with computer vision and fuzzy logic for defect detection in eggs and identification of lettuce growth shows the power of hybridization of AI technologies to solve agricultural problems. Additional papers also focus on automated modeling of physiological processes during postharvest distribution of agricultural products, the applications of neural networks, fusion of AI technologies and three dimensional computer vision technologies for different problems ranging from botanical identification and cell migration analysis to food microstructure evaluation.
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| Subject
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Computer vision.
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Life sciences.
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Software engineering.
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| LC Classification
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QH324.25E358 1998
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| Added Entry
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K C Ting
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S Panigrahi
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