Knowledge-based image understanding systems: a survey
Computer Vision and Image Understanding
A Cognitive Vision Platform for Automatic Recognition of Natural Complex Objects
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Image analysis based interface for diagnostic expert systems
WISICT '04 Proceedings of the winter international synposium on Information and communication technologies
Steps toward a cognitive vision system
AI Magazine
Contextual Coordination in a Cognitive Vision System for Symbolic Activity Interpretation
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
A Framework for Model-Based Tracking Experiments in Image Sequences
International Journal of Computer Vision
Ontology based complex object recognition
Image and Vision Computing
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Case-based object recognition for airborne fungi recognition
Artificial Intelligence in Medicine
Large-scale investigation of weed seed identification by machine vision
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Review: Wireless sensors in agriculture and food industry-Recent development and future perspective
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Bounded transparency for automated inspection in agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Original paper: Scale invariant feature approach for insect monitoring
Computers and Electronics in Agriculture
Segmentation and classification of tobacco seedling diseases
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
A web-based sensor network system with distributed data processing approach via web application
Computer Standards & Interfaces
Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
Computers and Electronics in Agriculture
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Early disease detection is a major challenge in horticulture. Integrated Pest Management (IPM) combines prophylactic, biological and physical methods to fight bioagressors of crops while minimizing the use of pesticides. This approach is particularly promising in the context of ornamental crops in greenhouses because of the high level of control needed in such agrosystems. However, IPM requires frequent and precise observations of plants (mainly leaves), which are not compatible with production constraints. Our goal is early detection of bioagressors. In this paper, we present a strategy based on advances in automatic interpretation of images applied to leaves of roses scanned in situ. We propose a cognitive vision system that combines image processing, learning and knowledge-based techniques. This system is illustrated with automatic detection and counting of a whitefly (Trialeurodes vaporariorum Westwood) at a mature stage. We have compared our approach with manual methods and our results showed that automatic processing is reliable. Special attention was paid to low infestation cases, which are crucial to agronomic decisions.