Artificial Intelligence for Biology and Agriculture

ISBN-13: 9780792350989 / Angielski / Twarda / 1998 / 262 str.

S. Panigrahi; K. C. Ting; S. Panigrahi

Opis

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 systems 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, cell migration analysis to food microstructure evaluation. This special issue "Artificial Intelligence in Biology and Agriculture" has been made possible due to the unconditional help, cooperation and time devotion from many people. We highly appreciate the contributions from the authors and their co-authors. We sincerely acknowledge all reviewers for taking time to review these articles. The reviewers were: Dr. Kuanglin Chao, Dr. Floyd E. Dowell, Dr. Laurent Gauthier, Dr. Paul H. Heinemann, Dr. Zhiwei Li, Dr. Bosoon Park, Dr. Jinglu Tan, Dr. Chi Ngoc Thai, Dr. Basant Ubhaya, Dr. Naiqian Zhang, Dr. Irfan Ahmad, Dr. David Vacaari, Dr. Young Han, Dr. Lary Kutz, Dr. David Slaughter, Dr. Digvir Jayas, Dr. Marvin Paulsen, Dr. George Hoogenboom, Dr. Mark Evans, Dr. Glen Kranzler, and Dr.

Spis treści

Preface. End-Effectors for Tomato Harvesting Robot; M. Monta, et al. A Computer Vision Method for Determining Length of Cheese Shreds; H. Ni, S. Gunasekaran. Automated Modelling of Physiological Processes During Postharvest Distribution of Agricultural Products; M. Sloof. A Neuro-Fuzzy Approach to Identify Lettuce Growth and Greenhouse Climate; B.T. Tien, G. van Straten. Artificial Keys for Botanical Identification Using a Multilayer Perceptron Neural Network (MLP); J.Y. Clark, K. Warwick. Video Grading of Oranges in Real-Time; M. Recce, et al. Cell Migration Analysis after In Vitro Wounding Injury with a Multi-Agent Approach; A. Boucher, et al. Color Computer Vision and Artificial Neural Networks for the Detection of Defects in Poultry Eggs; V.C. Patel, et al. Automatic Plankton Image Recognition; X. Tang, et al. Identification and Measurement of Convolutions in Cotton Fiber Using Image Analysis; Y.J. Han, et al. Fuzzy Logic for Biological and Agricultural Systems; B. Center, B.P. Verma. Robotics for Plant Production; N. Kondo, K.C. Ting. Three-Dimensional Image Reconstruction Procedure for Food Microstructure Evaluation; K. Ding, S. Gunasekaran.