The recognition of an object in an image is a complex task that involves a broad range of techniques. The system consists of various stages among which are: image acquisition, preprocessing, features extraction, and recognition. The problem of insect recognition and classification in the cotton field is a complex example of image understanding.;In a particular season, the appearance of insects in the cotton field is subject to many factors such as: weather conditions, rainfall, humidity, host plants, and temperature. During the past decade, numerous attempts to recognize and classify insects have been performed at New Mexico State University and met with various degree of success. All previous insect classification approaches known to us were based on classical statistical pattern recognition techniques. Changes in parameters that affect insect appearance make statistical modeling a difficult problem. In this research, we used a soft computing approach, which is a model-free technique to recognize and classify insects. Soft computing technique uses artificial neural networks (ANN) and fuzzy logic.;In this work, we show that soft-computing approaches perform extremely well compared with previous classification attempts. We also show that soft computing techniques could be a solution to classification when statistical modeling is an issue. We also show that artificial neural networks have the ability to model complex systems and achieve good results.
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