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Vol. 10, Special Issue 10 (2021)

An image repository of fall armyworm (FAW) with different severity level of infestation in maize

Author(s):
R Prabha, JS Kennedy, G Vanitha and N Sathiah
Abstract:
Fall armyworm has become a major concern for maize farmers in recent years, as it has resulted in significant yield losses in the maize field. A fall armyworm infestation might be detected automatically using a machine learning system, allowing for faster and accurate scouting of farmers' field operations. However, it is tedious for creating a machine-learning algorithm to discern between the target fall armyworm infestation and other sources of weeds, soil in a typical field. So, a vast amount of human-generated training data is required to train a machine learning system to consistently detect a specific fall armyworm infestation in the maize field. In this study, we created an image repository for different severity levels of fall armyworm infestation in maize. All of the high-quality photographs were shot with a digital camera against a variety of backgrounds with distinct light intensities in different locations. Visual scale ratings were also given to fall armyworm infestation in maize.
Pages: 900-911  |  355 Views  143 Downloads
How to cite this article:
R Prabha, JS Kennedy, G Vanitha and N Sathiah. An image repository of fall armyworm (FAW) with different severity level of infestation in maize. The Pharma Innovation Journal. 2021; 10(10S): 900-911.

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