Identification of Armyworm-Infected Leaves in Corn by Image Processing and Deep Learning-Reference-Cited by-同舟云学术

Identification of Armyworm-Infected Leaves in Corn by Image Processing and Deep Learning

Published:2024-06-01 Issue:2 Volume:27 Page:92-100
ISSN:1338-5267
Container-title:Acta Technologica Agriculturae
language:en
Short-container-title:

Author:

Saadati Nadia¹,Pourdarbani Razieh¹,Sabzi Sajad²,Hernandez-Hernandez José Luis³

Affiliation:

1. University of Mohaghegh Ardabili, Faculty of Agriculture Department of Biosystem Engineering Ardabil , Iran

2. Sharif University of Technology, Faculty of Computer Engineering Department of Computer Engineering Tehran , Iran

3. National Technological Institute of Mexico Campus Chilpancingo, Chilpancingo Guerrero , Mexico

Abstract

Abstract Corn is rich in fibre, vitamins, and minerals, and it is a nutritious source of carbohydrates. The area under corn cultivation is very large because, in addition to providing food for humans and animals, it is also used for raw materials for industrial products. Corn cultivation is exposed to the damage of various pests such as armyworm. A regional monitoring of pests is intended to actively track the population of this pest in a specific geography; one of the ways of monitoring is using the image processing technology. Therefore, the aim of this research was to identify healthy and armyworm-infected leaves using image processing and deep neural network in the form of 4 structures named AlexNet, DenseNet, EfficientNet, and GoogleNet. A total of 4500 images, including healthy and infected leaves, were collected. Next, models were trained by train data. Then, test data were evaluated using the evaluation criteria such as accuracy, precision, and F score. Results indicated all the classifiers obtained the precision above 98%, but the EfficientNet-based classifier was more successful in classification with the precision of 100%, accuracy of 99.70%, and F-score of 99.68%.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/ata-2024-0013

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