Stable diffusion for Data Augmentation in COCO and Weed Datasets

Generative models have increasingly impacted relative tasks ranging from image revision and object detection in computer vision to interior design and idea illustration in more general fields. Stable diffusion is an outstanding model series that paves the way for producing high-resolution images with thorough details from text prompts or reference images. It will be an interesting topic about how to leverage the capability of stable diffusion to elevate the image variations of certain categories (e.g., vehicles, humans, and daily objects); particularly, it has the potential to gain improvements for small datasets with image-sparse categories. This study utilized seven categories in the popular COCO dataset and three widespread weed species in Michigan to evaluate the efficiency of a recent version of stable diffusion. In detail, Stable diffusion was used to generate synthetic images belonging to these classes; then, YOLOv8 models were trained based on these synthetic images, whose performance was compared to the models trained on original images. In addition, several techniques (e.g., Image-to-image translation, Dreambooth, ControlNet) of Stable diffusion were leveraged for image generation with different focuses. In spite of the overall results being disappointing, promising results have been achieved in some classes, illustrating the potential of stable diffusion models to improve the performance of detection models, which represent more helpful information being conveyed into the models by the generated images. This seminal study may expedite the adaption of stable diffusion models to classification and detection tasks in different fields.

Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach

Weed management plays an important role in many modern agricultural applications. Conventional weed control methods mainly rely on chemical herbicides or hand weeding, which are often cost-ineffective, environmentally unfriendly, or even posing a threat to food safety and human health. Recently, automated/robotic weeding using machine vision systems has seen increased research attention with its potential for precise and individualized weed treatment. However, dedicated, large-scale, and labeled weed image datasets are required to develop robust and effective weed identification systems but they are often difficult and expensive to obtain. To address this issue, data augmentation approaches, such as generative adversarial networks (GANs), have been explored to generate highly realistic images for agricultural applications. Yet, despite some progress, those approaches are often complicated to train or have difficulties preserving fine details in images. In this paper, we present the first work of applying diffusion probabilistic models (also known as diffusion models) to generate high-quality synthetic weed images based on transfer learning. Comprehensive experimental results show that the developed approach consistently outperforms several state-of-the-art GAN models, representing the best trade-off between sample fidelity and diversity and highest FID score on a common weed dataset, CottonWeedID15. In addition, the expanding dataset with synthetic weed images can apparently boost model performance on four deep learning (DL) models for the weed classification tasks. Furthermore, the DL models trained on CottonWeedID15 dataset with only 10% of real images and 90% of synthetic weed images achieve a testing accuracy of over 94%, showing high-quality of the generated weed samples. The codes of this study are made publicly available at https://github.com/DongChen06/DMWeeds.

Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review

In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments. Large-scale, balanced and ground-truthed image datasets, however, are often difficult to obtain to fuel the development of advanced, high-performance models. As artificial intelligence through deep learning is impacting analysis and modeling of agricultural images, data augmentation plays a crucial role in boosting model performance while reducing manual efforts for data preparation, by algorithmically expanding training datasets. Beyond traditional data augmentation techniques, generative adversarial network (GAN) invented in 2014 in the computer vision community, provides a suite of novel approaches that can learn good data representations and generate highly realistic samples. Since 2017, there has been a growth of research into GANs for image augmentation or synthesis in agriculture for improved model performance. This paper presents an overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture (https://github.com/Derekabc/GANs-Agriculture), involving various vision tasks for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping as well as postharvest detection of fruit defects. Challenges and opportunities of GANs are discussed for future research.

Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems

Precision weed management offers a promising solution for sustainable cropping systems through the use of chemical-reduced/non-chemical robotic weeding techniques, which apply suitable control tactics to individual weeds. Therefore, accurate identification of weed species plays a crucial role in such systems to enable precise, individualized weed treatment. This paper makes a first comprehensive evaluation of deep transfer learning (DTL) for identifying common weeds specific to cotton production systems in southern United States. A new dataset for weed identification was created, consisting of 5187 color images of 15 weed classes collected under natural lighting conditions and at varied weed growth stages, in cotton fields during the 2020 and 2021 field seasons. We evaluated 27 state-of-the-art deep learning models through transfer learning and established an extensive benchmark for the considered weed identification task. DTL achieved high classification accuracy of F1 scores exceeding 95%, requiring reasonably short training time (less than 2.5 hours) across models. ResNet101 achieved the best F1-score of 99.1% whereas 14 out of the 27 models achieved F1 scores exceeding 98.0%. However, the performance on minority weed classes with few training samples was less satisfactory for models trained with a conventional, unweighted cross entropy loss function. To address this issue, a weighted cross entropy loss function was adopted, which achieved substantially improved accuracies for minority weed classes. Furthermore, a deep learning-based cosine similarity metrics was employed to analyze the similarity among weed classes, assisting in the interpretation of classifications. Both the codes for model benchmarking and the weed dataset are made publicly available, which expect to be be a valuable resource for future research in weed identification and beyond.

Out-of-focus Blur: Image De-blurring

Image de-blurring is important in many cases of imaging a real scene or object by a camera. This project focuses on de-blurring an image distorted by an out-of-focus blur through a simulation study. A pseudo-inverse filter is first explored but it fails because of severe noise amplification. Then Tikhonov regularization methods are employed, which produce greatly improved results compared to the pseudo-inverse filter. In Tikhonov regularization, the choice of the regularization parameter plays a critical rule in obtaining a high-quality image, and the regularized solutions possess a semi-convergence property. The best result, with the relative restoration error of 8.49%, is achieved when the prescribed discrepancy principle is used to decide an optimal value. Furthermore, an iterative method, Conjugated Gradient, is employed for image de-blurring, which is fast in computation and leads to an even better result with the relative restoration error of 8.22%. The number of iteration in CG acts as a regularization parameter, and the iterates have a semi-convergence property as well.

Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks

The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) parameters. In this work, we present empirical comparison between the standard LSTM recurrent neural network architecture and three new parameter-reduced variants obtained by eliminating combinations of the input signal, bias, and hidden unit signals from individual gating signals. The experiments on two sequence datasets show that the three new variants, called simply as LSTM1, LSTM2, and LSTM3, can achieve comparable performance to the standard LSTM model with less (adaptive) parameters.

Empirical Evaluation of A New Approach to Simplifying Long Short-term Memory (LSTM)

The standard LSTM, although it succeeds in the modeling long-range dependences, suffers from a highly complex structure that can be simplified through modifications to its gate units. This paper was to perform an empirical comparison between the standard LSTM and three new simplified variants that were obtained by eliminating input signal, bias and hidden unit signal from individual gates, on the tasks of modeling two sequence datasets. The experiments show that the three variants, with reduced parameters, can achieve comparable performance with the standard LSTM. Due attention should be paid to turning the learning rate to achieve high accuracies

Food Image Recognition by Using Convolutional Neural Networks (CNNs)

Food image recognition is one of the promising applications of visual object recognition in computer vision. In this study, a small-scale dataset consisting of 5822 images of ten categories and a five-layer CNN was constructed to recognize these images. The bag-of-features (BoF) model coupled with support vector machine was first tested as comparison, resulting in an overall accuracy of 56%, while the CNN performed much better with an overall accuracy of 74%. Data expansion techniques were applied to increase the size of training images, which achieved a significantly improved accuracy of more than 90% and prevent the overfitting issue that occurred to the CNN without using data expansion. Further improvement is within reach by collecting more images and optimizing the network architecture and relevant hyper-parameters.