A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm
Ali Mirzazadeh,
Afshin Azizi,
Yousef Abbaspour-Gilandeh,
José Luis Hernández-Hernández,
Mario Hernández-Hernández,
Iván Gallardo-Bernal
Affiliations
Ali Mirzazadeh
Department of Agricultural Engineering and Technology, Faculty of Agriculture and Natural Resources (Moghan), University of Mohaghegh Ardabili, Ardabil 56131-56491, Iran
Afshin Azizi
Department of Agricultural Engineering and Technology, Faculty of Agriculture and Natural Resources (Moghan), University of Mohaghegh Ardabili, Ardabil 56131-56491, Iran
Yousef Abbaspour-Gilandeh
Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
José Luis Hernández-Hernández
Tecnológico Nacional de México/Campus Chilpancingo, Chilpancingo 39070, Guerrero, Mexico
Mario Hernández-Hernández
Faculty of Engineering, Autonomous University of Guerrero, Chilpancingo 39087, Guerrero, Mexico
Iván Gallardo-Bernal
Higher School of Government and Public Management, Autonomous University of Guerrero, Chilpancingo 39087, Guerrero, Mexico
Estimation of crop damage plays a vital role in the management of fields in the agriculture 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.
