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Automated System for Crops Recognition and Classification
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Author(s): Alaa M. AlShahrani (Taif University, Saudi Arabia), Manal A. Al-Abadi (Taif University, Saudi Arabia), Areej S. Al-Malki (Taif University, Saudi Arabia), Amira S. Ashour (Taif University, Saudi Arabia & Tanta University, Egypt)and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)
Copyright: 2018
Pages: 16
Source title:
Computer Vision: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-5204-8.ch050
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Abstract
Marketing profit optimization and preventing the crops' infections are a critical issue. This requires crops recognition and classification based on their characteristics and different features. The current work proposed a recognition/classification system that applied to differentiate between fresh (healthy) from rotten crops as well as to identify each crop from the other based on their common feature vectors. Consequently, image processing is employed to perform the statistical measurements of each crop. ImageJ software was employed to analyze the desired crops to extract their features. These extracted features are used for further crops recognition and classification using the Least Mean Square Error (LMSE) algorithm in Matlab. Another classification method based on Bag of Features (BoF) technique is employed to classify crops into classes, namely healthy and rotten. The experimental results are applied of databases for orange, mango, tomato and potatoes. The achieved recognition (classification) rate by using the LMSE for all datasets (healthy and rotten) has 100%. However, after adding 10%, 20%, and 30% Gaussian noise, the obtained the average recognition rates were 85%, 70%, and 25%; respectively. Moreover, the classification (healthy and rotten) using BoF achieved accuracies of 100%, 88%, 94%, and 75% for potatoes, mango, orange, and tomato; respectively. Furthermore, the classification for all the healthy datasets achieved accuracy of 88%.
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