An official website of the United States government
Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. One of the major sources of variability in image quality acquired in the outdoors for such tasks is changing lighting condition that can alter the appearance of the objects or the contents of the entire image. While transfer learning and data augmentation to some extent reduce the need for large amount of data to train deep neural networks, the large variety of cultivars and the lack of shared datasets in agriculture makes wide-scale field deployments difficult. In this paper, we present a high throughput robust active lighting-based camera system that generates consistent images in all lighting conditions. We detail experiments that show the consistency in images quality leading to relatively fewer images to train deep neural networks for the task of object detection. We further present results from field experiment under extreme lighting conditions where images without active lighting significantly lack to provide consistent results. The experimental results show that on average, deep nets for object detection trained on consistent data required nearly four times less data to achieve similar level of accuracy. This proposed work could potentially provide pragmatic solutions to computer vision needs in agriculture.
more »
« less
A Seed Segmentation Contour Generator and Counter
Living in a data-driven world with rapidly growing machine learning techniques, it is apparent that utilizing these methods is necessary to achieve state-of-the-art performance in object detection. Recent novel approaches in the deep-learning field have boasted real-time object segmentation methods given the algorithm is connected to a large validation dataset. Knowing that these algorithms are restricted to a given dataset, it is apparent that the need for data generating algorithms is on a rise. As some object detection problems may suffice with a statically trained deep-learning model, it is true that others will not. Given the no free lunch theorem, we know that no machine learning algorithm can truly generalize to data it has not been trained on; therefore, deep learning models trained on images of cats will not necessarily classify dogs correctly. With modern deep learning libraries being ported for mobile devices, a wide range of utilityhas been made apparent for plant researchers around the world. One such usage of these real-time approaches is to count and classify seed kernels, replacing monotonous-human-error-ridden tasks. Plant scientists around the world have daily jobs of counting seeds by hand or using multi-thousand dollar devices to automate the task. It is apparent that many third world countries, where such consumer devices do not exist or require too many resources, could benefit from such an automated task. PhenoApps, an organization started within Kansas State University, has been supplying a subset of these countries with modern phones for such uses. With the following seed segmentation algorithm and the usage of modern mobile devices, scientists can count seeds with the click of a button and produce results in split-seconds. The algorithms proposed in this paper achieve multiple novel implementations. Mainly, Rice’s Theorem was used to show that object detection in clusters is an undecidable task for Turing Machines. Along with this, the novel implementations include an Android application which can segment seed kernels and a machine learning algorithm which can accurately generate contour data sets. The data generator provided in this paper is an effective start for the later usage of deep learning models and is the first step for a real-time dynamic and static seed counter.
more »
« less
- Award ID(s):
- 1543958
- PAR ID:
- 10095598
- Date Published:
- Journal Name:
- 31st International Conference on Computer Applications in Industry and Engineering
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
A brain tumor is an abnormal growth in the brain that disrupts its functionality and poses a significant threat to human life by damaging neurons. Early detection and classification of brain tumors are crucial to prevent complications and maintain good health. Recent advancements in deep learning techniques have shown immense potential in image classification and segmentation for tumor identification and classification. In this study, we present a platform, BrainView, for detection, and segmentation of brain tumors from Magnetic Resonance Images (MRI) using deep learning. We utilized EfficientNetB7 pre-trained model to design our proposed DeepBrainNet classification model for analyzing brain MRI images to classify its type. We also proposed a EfficinetNetB7 based image segmentation model, called the EffB7-UNet, for tumor localization. Experimental results show significantly high classification (99.96%) and segmentation (92.734%) accuracies for our proposed models. Finally, we discuss the contours of a cloud application for BrainView using Flask and Flutter to help researchers and clinicians use our machine learning models online for research purposes.more » « less
-
Cracks of civil infrastructures, including bridges, dams, roads, and skyscrapers, potentially reduce local stiffness and cause material discontinuities, so as to lose their designed functions and threaten public safety. This inevitable process signifier urgent maintenance issues. Early detection can take preventive measures to prevent damage and possible failure. With the increasing size of image data, machine/deep learning based method have become an important branch in detecting cracks from images. This study is to build an automatic crack detector using the state-of-the-art technique referred to as Mask Regional Convolution Neural Network (R-CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object localization but also for object instance segmentation of natural images. It is found that the built crack detector is able to perform highly effective and efficient automatic segmentation of a wide range of images of cracks. In addition, this proposed automatic detector could work on videos as well; indicating that this detector based on Mask R-CNN provides a robust and feasible ability on detecting cracks exist and their shapes in real time on-site.more » « less
-
Summary Revealing the contributions of genes to plant phenotype is frequently challenging because loss‐of‐function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such asArabidopsis thaliana.An image segmentation‐based method using the software ImageJ and an object detection‐based method using the Faster Region‐based Convolutional Neural Network (R‐CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts.The segmentation‐based method was error‐prone (correlation between true and predicted seed counts,r2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection‐based algorithm yielded near perfect seed counts (r2 = 0.9996) and highly accurate fruit counts (r2 = 0.980). Comparing seed counts for wild‐type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes.Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.more » « less
-
Lack of enough labeled data is a major problem in building machine learning based models when the manual annotation (labeling) is error-prone, expensive, tedious, and time-consuming. In this paper, we introduce an iterative deep learning based method to improve segmentation and counting of cells based on unbiased stereology applied to regions of interest of extended depth of field (EDF) images. This method uses an existing machine learning algorithm called the adaptive segmentation algorithm (ASA) to generate masks (verified by a user) for EDF images to train deep learning models. Then an iterative deep learning approach is used to feed newly predicted and accepted deep learning masks/images (verified by a user) to the training set of the deep learning model. The error rate in unbiased stereology count of cells on an unseen test set reduced from about 3 % to less than 1 % after 5 iterations of the iterative deep learning based unbiased stereology process.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
