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Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network

Information Processing in Agriculture 2020年第4期7卷 535-545页

作者： Muhammad Hamza Asad abdul bais Electronic Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaSaskatchewanCanada

Herbicide use is rising globally to enhance food production,causing harm to environment and the *** agriculture suggests variable-rate herbicide application based on weed densities to mitigate adverse effects of *** weed density estimation using advanced computer vision techniques like deep learning requires large labelled agriculture *** large agriculture data at pixel level is a time-consuming and tedious *** this paper,a methodology is developed to accelerate manual labelling of pixels using a two-step *** the first step,the background and foreground are segmented using maximum likelihood classification,and in the second step,the weed pixels are manually *** labelled data is used to train semantic segmentation models,which classify crop and background pixels as one class,and all other vegetation as the second *** paper evaluates the proposed methodology on high-resolution colour images of canola fields and makes performance comparison of deep learning meta-architectures like SegNet and UNET and encoder blocks like VGG16 and ***-50 based SegNet model has shown the best results with mean intersection over union value of 0.8288 and frequency weighted intersection over union value of 0.9869.

关键词： Weed detection Semantic segmentation Variable rate herbicide Maximum likelihood classification

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Evaluation of model generalization for growing plants using conditional learning

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Artificial Intelligence in Agriculture 2022年第1期6卷 189-198页

作者： Hafiz Sami Ullah abdul bais Electronic Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSaskatchewanCanada

This paper aims to solve the lack of generalization of existing semantic segmentation models in the crop and weed segmentation *** compare two training mechanisms,classical and adversarial,to understand which scheme works best for a particular encoder-decoder *** use simple U-Net,SegNet,and DeepLabv3+with ResNet-50 backbone as segmentation *** models are trained with cross-entropy loss for classical and PatchGAN loss for adversarial *** adopting the Conditional Generative Adversarial Network(CGAN)hierarchical settings,we penalize different Generators(G)using PatchGAN Discriminator(D)and L1 loss to generate segmentation *** generalization is to exhibit fewer failures and perform comparably for growing plants with different data *** utilize the images from four different stages of sugar *** divide the data so that the full-grown stage is used for training,whereas earlier stages are entirely dedicated to testing the *** conclude that U-Net trained in adversarial settings is more robust to changes in the *** adversarially trained U-Net reports 10%overall improvement in the results with mIOU scores of 0.34,0.55,0.75,and 0.85 for four different growth stages.

关键词： Weed detection Semantic segmentation Adversarial training Late germination Sugar beet Crop segmentation Growing plants Domain change

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