Addressing the computational bottleneck of training deep learning models on high-resolution,three-dimensional images,this study introduces an optimized approach,combining distributed learning(parallelism),image resolu...Addressing the computational bottleneck of training deep learning models on high-resolution,three-dimensional images,this study introduces an optimized approach,combining distributed learning(parallelism),image resolution,and data augmentation.We propose analysis methodologies that help train deep learning(DL)models on proximal hyperspectral images,demonstrating superior performance in eight-class crop(canola,field pea,sugarbeet and flax)and weed(redroot pigweed,resistant kochia,waterhemp and ragweed)classification.Utilizing state-of-the-art model architectures(ResNet-50,VGG-16,DenseNet,EfficientNet)in comparison with ResNet-50 inspired Hyper-Residual Convolutional Neural Network model.Our findings reveal that an image resolution of 100x100x54 maximizes accuracy while maintaining computational efficiency,surpassing the performance of 150x150x54 and 50x50x54 resolution images.By employing data parallelism,we overcome system memory limitations and achieve exceptional classification results,with test accuracies and F1-scores reaching 0.96 and 0.97,respectively.This research highlights the potential of residual-based networks for analyzing hyperspectral images.It offers valuable insights into optimizing deep learning models in resource-constrained environments.The research presents detailed training pipelines for deep learning models that utilize large(>4k)hyperspectral training samples,including background and without any data preprocessing.This approach enables the training of deep learning models directly on raw hyperspectral data.展开更多
Weed identification is fundamental toward developing a deep learning-based weed control system.Deep learning algorithms assist to build a weed detection model by using weed and crop images.The dynamic environmental co...Weed identification is fundamental toward developing a deep learning-based weed control system.Deep learning algorithms assist to build a weed detection model by using weed and crop images.The dynamic environmental conditions such as ambient lighting,moving cameras,or varying image backgrounds could affect the performance of deep learning algorithms.There are limited studies on how the different image backgrounds would impact the deep learning algorithms for weed identification.The 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 interchangeably.The 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 models.The 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 background.Results 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%,respectively.Conversely,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%respectively.Both 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 model.It 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.展开更多
基金supported by the U.S.Department of Agriculture,agreement number 58-6064-8-023supported by the USDA National Institute of Food and Agriculture,Hatch project number ND01487.
文摘Addressing the computational bottleneck of training deep learning models on high-resolution,three-dimensional images,this study introduces an optimized approach,combining distributed learning(parallelism),image resolution,and data augmentation.We propose analysis methodologies that help train deep learning(DL)models on proximal hyperspectral images,demonstrating superior performance in eight-class crop(canola,field pea,sugarbeet and flax)and weed(redroot pigweed,resistant kochia,waterhemp and ragweed)classification.Utilizing state-of-the-art model architectures(ResNet-50,VGG-16,DenseNet,EfficientNet)in comparison with ResNet-50 inspired Hyper-Residual Convolutional Neural Network model.Our findings reveal that an image resolution of 100x100x54 maximizes accuracy while maintaining computational efficiency,surpassing the performance of 150x150x54 and 50x50x54 resolution images.By employing data parallelism,we overcome system memory limitations and achieve exceptional classification results,with test accuracies and F1-scores reaching 0.96 and 0.97,respectively.This research highlights the potential of residual-based networks for analyzing hyperspectral images.It offers valuable insights into optimizing deep learning models in resource-constrained environments.The research presents detailed training pipelines for deep learning models that utilize large(>4k)hyperspectral training samples,including background and without any data preprocessing.This approach enables the training of deep learning models directly on raw hyperspectral data.
基金based upon work partially supported by the USDA-Agricultural Research Service,agreement number 58-6064-8-023.
文摘Weed identification is fundamental toward developing a deep learning-based weed control system.Deep learning algorithms assist to build a weed detection model by using weed and crop images.The dynamic environmental conditions such as ambient lighting,moving cameras,or varying image backgrounds could affect the performance of deep learning algorithms.There are limited studies on how the different image backgrounds would impact the deep learning algorithms for weed identification.The 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 interchangeably.The 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 models.The 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 background.Results 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%,respectively.Conversely,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%respectively.Both 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 model.It 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.
