Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

Bosilj, Petra and Aptoula, Erchan and Duckett, Tom and Cielniak, Grzegorz (2019) Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture. Journal of Field Robotics . ISSN 1556-4967

Full content URL: https://doi.org/10.1002/rob.21869

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Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

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Abstract

Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds in order to perform selective treatments, increase yield and crop health while reducing the amount of chemicals used. Deep learning approaches have recently achieved both excellent classification performance and real-time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labelling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep-learning-based classifiers for different crop types, with the goal of reducing the retraining time and labelling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds, and compare the performance and retraining efforts required when using data labelled at pixel level with partially labelled data obtained through a less time-consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible, and reduces training times for up to $80\%$. Furthermore, we show that even when the data used for re-training is imperfectly annotated, the classification performance is within $2\%$ of that of networks trained with laboriously annotated pixel-precision data.

Keywords:precision agriculture, transfer learning, semantic segmentation
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:35535
Deposited On:11 Apr 2019 12:29

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