<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 <italic>F</italic>-score of 99.68%.</p>
</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%.

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

University of Mohaghegh Ardabili, Faculty of Agriculture

Sharif University of Technology, Faculty of Computer Engineering

National Technological Institute of Mexico

Acta Technologica Agriculturae

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

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

Online veröffentlicht: 08. Juni 2024

Seitenbereich: 92 - 100

DOI: https://doi.org/10.2478/ata-2024-0013

Schlüsselwörter
EfficientNet, classification, image, convolutional neural networks

© 2024 Nadia Saadati et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 08. Juni 2024

Seitenbereich: 92 - 100

DOI: https://doi.org/10.2478/ata-2024-0013

SchlüsselwörterEfficientNet, classification, image, convolutional neural networks

© 2024 Nadia Saadati et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
EfficientNet, classification, image, convolutional neural networks