Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach

Kamath, Radhika and Balachandra, Mamatha and Prabhu, Srikanth (2020) Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach. International Journal of Agronomy. ISSN 1687-8159

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Weeds are unwanted plants that grow among crops. +ese weeds can significantly reduce the yield and quality of the farm output. Unfortunately, site-specific weed management is not followed in most of the cases. +at is, instead of treating a field with a specific type of herbicide, the field is treated with a broadcast herbicide application. +is broadcast application of the herbicide has resulted in herbicide-resistant weeds and has many ill effects on the natural environment. +is has prompted many research studies to seek the most effective weed management techniques. One such technique is computer vision-based automatic weed detection and identification. Using this technique, weeds can be detected and identified and a suitable herbicide can be recommended to the farmers. +erefore, it is important for the computer vision technique to successfully identify and classify the crops and weeds from the digital images. +is paper investigates the multiple classifier systems built using support vector machines and random forest classifiers for plant classification in classifying paddy crops and weeds from digital images. Digital images of paddy crops and weeds from the paddy fields were acquired using three different cameras fixed at different heights from the ground. Texture, color, and shape features were extracted from the digital images after background subtraction and used for classification. A simple and new method was used as a decision function in the multiple classifier systems. An accuracy of 91.36% was obtained by the multiple classifier systems and was found to outperform single classifier systems

Item Type: Article
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 14 Aug 2020 04:29
Last Modified: 14 Aug 2020 04:29

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