Title
A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm.
Author
Ali Mirzazadeh
Afshin Azizi
Yousef Abbaspour_Gilandeh
José Luis Hernández-Hernández
Mario Hernández Hernández
Iván Gallardo Bernal
Access level
Open Access
Subjects
Summary or description
Estimation of crop damage plays a vital role in the management of fields in the agricultura sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds¿ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of Deep learning-based models to classify other damaged crops.
Publisher
Agronomy
Publish date
November, 2021
Publication type
Article
Information Resource
Format
application/pdf
Language
English
Audience
General public
Source repository
Repositorio Institucional de Ciencia Abierta de la Universidad Autónoma de Guerrero
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