Recognition of weeds in corn crops: System with convolutional neural networks

Autor: Rodrigo Nunes Wessner, Rejane Frozza, Daniela Duarte da Silva Bagatini, Rolf Fredi Molz
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Journal of Agriculture and Food Research, Vol 14, Iss , Pp 100669- (2023)
Druh dokumentu: article
ISSN: 2666-1543
DOI: 10.1016/j.jafr.2023.100669
Popis: This work proposes weed classification solutions in corn crops using Deep Artificial Neural Networks. The InceptionV3, MobileNetV2 and Adapted MobileNetV2 Convolutional Neural Network architectures were used to extract features from the images. The research problem refers to the question “Does the use of Artificial Neural Networks present good performance for visual analysis in the recognition of weeds in corn crops?”. In this way, the research was based on the analysys of image recognition methods and techniques with Artificial Neural Networks and organizing a database with images of corn crops, training and comparing the accuracy results and analyzing the behavior of different architectures on the dataset. Among the models, the one with the highest accuracy was InceptionV3, with 98%, demonstrating the ability of the technologies used in this work to become realistic for applications in crops, in order to help farmers to increase productivity in their crops.
Databáze: Directory of Open Access Journals
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