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A study on deep learning algorithm performance on weed and crop species identification under different image background

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Artificial Intelligence in Agriculture 2022年第1期6卷 242-256页

作者： Sunil G C cengiz koparan Mohammed Raju Ahmed Yu Zhang Kirk Howatt Xin Sun Department of Agricultural and Biosystems Engineering North Dakota State UniversityFargoND 58108USA Department of Plant Sciences North Dakota State UniversityPO Box 6050FargoND 58108-6050USA

Weed identification is fundamental toward developing a deep learning-based weed control *** learning algorithms assist to build a weed detection model by using weed and crop *** dynamic environmental conditions such as ambient lighting,moving cameras,or varying image backgrounds could affect the performance of deep learning *** are limited studies on how the different image backgrounds would impact the deep learning algorithms for weed *** objective of this research was to test deep learning weed identification model performance in images with potting mix(non-uniform)and black pebbled(uniform)backgrounds *** weed and crop images were acquired by four canon digital cameras in the greenhouse with both uniform and non-uniform background conditions.A Convolutional Neural Network(CNN),Visual Group Geometry(VGG16),and Residual Network(ResNet50)deep learning architectures were used to build weed classification *** model built from uniform background images was tested on images with a non-uniform background,as well as model built from non-uniform background images was tested on images with uniform *** showed that the VGG16 and ResNet50 models built from non-uniform background images were evaluated on the uniform background,achieving models'performance with an average f1-score of 82.75%and 75%,***,the VGG16 and ResNet50 models built from uniform background images were evaluated on the non-uniform background images,achieving models'performance with an average f1-score of 77.5%and 68.4%*** the VGG16 and ResNet50 models'performances were im-proved with average f1-score values between 92%and 99%when both uniform and non-uniform background images were used to build the *** appears that the model performances are reduced when they are tested with images that have different object background than the ones used for building the model.

关键词： Computer vision Deep learning Image background Neural network Weed classification

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