Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

Abstract

Mechanical damages of sugar beet during harvesting affects the quality of the final products and sugar yield. The mechanical damage of sugar beet is assessed randomly by operators of harvesters and can depend on the subjective opinion and experience of the operator due to the complexity of the harvester machines. Thus, the main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine. In this research, various detector models based on the CNN, including You Only Look Once (YOLO) v4, region-based fully convolutional network (R-FCN) and faster regions with convolutional neural network features (Faster R-CNN) were developed. Sugar beet image data during harvesting from a harvester in different farming conditions were used for training and validation of the proposed models. The experimental results showed that the YOLO v4 CSPDarknet53 method was able to detect damage in sugar beet with better performance (recall, precision and F1-score of about 92, 94 and 93%, respectively) and higher speed (around 29 frames per second) compared to the other developed CNNs. By means of a CNN-based vision system, it was possible to automatically detect sugar beet damage within the sugar beet harvester machine.

Plant phenotyping relevance

収穫中のテンサイの機械的損傷という植物状態を、画像とCNNで自動検出する手法を開発・比較し、性能検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's sugar beet image dataset (3425 images), annotations, and self-developed code are not stated as publicly available anywhere in the supplied blocks. The only URLs are a generic annotation tool (LabelImg), a cited prior-work reference, and a manufacturer's harvester schematic PDF—none are paper-specific data,

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